JP2007502670A - Respiratory disorder management system and method - Google Patents

Abstract

  A method of treating a disordered breathing comprising: sensing a condition affecting a patient; and adapting a therapy to alleviate the disordered breathing based on the sensed condition. The treatment can be adapted to enhance the effectiveness of the treatment, to provide a treatment that reduces the impact of the treatment on the patient, or to achieve other treatment goals. Cardiac electrical therapy to alleviate the respiratory disorder may include various cardiac pacing procedures and / or delivery of non-excitable electrical stimulation to the heart.

Description

  The present invention relates generally to the diagnosis and treatment of respiratory disorders.

  Breathing disorders can be caused by a wide range of respiratory conditions, including disruption of the normal respiratory cycle. Although breathing problems often occur during sleep, the condition may also occur while the patient is awake. Respiratory disruption can be particularly severe in patients who are simultaneously suffering from cardiovascular defects such as congestive heart failure. Unfortunately, respiratory disorders are often not diagnosed. The effects of breathing problems can have serious health consequences for patients if left untreated.

  To date, various respiratory disorders have been identified including, for example, apnea, hypopnea, dyspnea, hyperpnea, tachypnea, and periodic breathing such as Chain Stokes breathing (CSR). Apnea is a fairly common disorder characterized by periods of intermittent breathing. Apnea is generally classified based on its etiology. One type of apnea, called obstructive apnea, occurs when the patient's airways are obstructed by soft tissue collapse in the back of the pharynx. Central apnea is caused by disturbances in respiratory central nervous system control. The patient stops breathing when there is no control signal from the brain to the respiratory muscles or when it is interrupted. Mixed apnea is a combination of a central apnea type and an obstructive one. People who experience the apnea event stop breathing for a period of time, regardless of the type of apnea. Breathing pauses sometimes do not occur hundreds of times per night and sometimes occur repeatedly during sleep for one minute or more.

  In addition to apnea, other types of respiratory disturbance cycles have also been identified, including hypopnea (shallow breathing), tachypnea (rapid breathing), hyperpnea (heavy breathing), and dyspnea (difficult breathing) . Combinations of the respiratory cycles described above (eg, including periodic breathing and Chain Stokes breathing (CSR)) may be observed. Periodic breathing is characterized by a periodic breathing pattern that may indicate a rhythmic rise and fall in tidal volume. Chain Stokes breathing is a specific form of periodic breathing in which tidal volume is reduced to zero, resulting in apnea intervals. Cyclic breathing and CSR breathing interruptions may be associated with central apnea or may actually be obstructive. CSR is often observed in patients with congestive heart failure (CHF) and is associated with an increased risk of progressive CHF progression. Treatment for respiratory-related sleep disorders is particularly critical because of its cardiovascular association.

  Various embodiments of the present invention include methods and systems for providing adaptive treatment for respiratory disorders. The present invention is directed to a system and method for detecting respiratory disturbances. The systems and methods of the present invention are further directed to adapting cardiac electrotherapy to treat respiratory disorders. The systems and methods of the present invention are also directed to the delivery of cardiac electrical stimulation therapies adapted to treat respiratory disorders.

  According to one embodiment, a medical treatment device is coupled to a sensing system configured to sense a condition affecting a patient, and to adapt cardiac electrotherapy to treat a respiratory disorder A therapy control system configured to: The apparatus further includes a cardiac electrical stimulation generator coupled to the therapy control system and configured to deliver a therapy adapted to the patient.

  According to another embodiment, the automated method of treatment includes receiving sensory information indicative of one or more physiological or non-physiological conditions and, based on the sensory information, treating respiratory disorders. Adapting cardiac electrotherapy to treat is included. The method further includes generating an output signal that can be delivered to the heart based on the adapted cardiac electrotherapy. Preferably, at least two of receiving sensory information, adapting cardiac electrical therapy, and generating an output signal are performed using an implantable component.

  The above summary of the present invention is not intended to describe each embodiment or every implementation of the present invention. Advantages and results will become apparent and will be appreciated with a more complete understanding of the present invention by reading the following detailed description and claims with reference to the accompanying drawings.

  While the invention is amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that the intention is not to limit the invention to the particular embodiments described. On the contrary, the invention is intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims.

  In the following description of the illustrated embodiments, reference is made to the accompanying drawings, which form a part of the embodiments and in which the present invention is implemented. Various embodiments are shown by way of illustration. It should be understood that other embodiments may be utilized and that structural and functional changes may be made without departing from the scope of the present invention.

  A method, apparatus, and system for performing a disordered breathing treatment method according to embodiments of the present invention may include one or more of the features, components, configurations, methods, and / or combinations described herein. May be incorporated. For example, the medical system may be implemented to include one or more of the components, features, and / or processes described below. Such a method, apparatus, or system may not include all of the components, features, and / or functions described herein, but may provide one or more useful configurations and / or functionalities. It is contemplated that it may be implemented to include further selected components, features and functions.

  A significant percentage of patients between the ages of 30 and 60 experience some form of respiratory disorder. Breathing disorders occur primarily during sleep and are associated with excessive daytime sleepiness, generalized hypertension, increased risk of stroke, acute tonsillitis, and myocardial infarction. Respiratory disorders are particularly prevalent among patients with congestive heart failure and may contribute to the progression of heart failure.

  To date, various therapies have been used to treat respiratory disorders, including both central and obstructive types. Obstructive sleep apnea has been associated with a pharyngeal deviation of the tongue and its surroundings and thus obstruction of the respiratory pathway. A commonly prescribed treatment for obstructive apnea is continuous positive airway pressure (CPAP). The CPAP device applies air pressure through a nasal mask worn by the patient. The application of continuous positive airway pressure leaves the patient's pharynx open and reduces or eliminates the obstruction that results in apnea.

  Tongue muscle deviation has been attributed to a decrease in neuromuscular activity in the upper respiratory tract. Treatment for obstructive sleep apnea includes compensation for decreased muscle activity through electrical activation of the tongue muscle. The hypoglossal (HG) nerve innervates the lingual protruding and retracting muscles. For example, electrical stimulation appropriately applied to the hypoglossal nerve can prevent the tongue from retreating, thus preventing the tongue from blocking the airway.

  Cardiac pacing during sleep or wakefulness can prevent or reduce the occurrence of respiratory disturbances. Various embodiments described herein relate to systems and methods for applying effective cardiac electrotherapy to alleviate respiratory disturbances. Such treatment can be adapted, for example, to achieve an overall level of therapeutic effectiveness. The treatment can be adapted to provide a staged treatment that can achieve various therapeutic goals.

  For example, the treatment can be adapted to alleviate the onset of a current disordered breathing disorder or prevent the occurrence of a predicted disordered breathing disorder. The cardiac electrotherapy can be adapted to achieve a desired reduction in effectiveness, eg, the overall frequency and / or severity of the development of respiratory disorders. The cardiac electrical therapy can also be adapted, for example, to provide a therapy that balances treatment goals with device life retention.

  The treatment can be adapted, for example, to adjust the treatment impact on the patient to reduce the treatment impact on the patient. In performing reduced impact therapy, the system can evaluate the impact of the therapy on the patient taking into account various conditions. For example, conditions such as patient comfort as indicated by patient feedback, undesired side effects, stress on the physiology involved in respiratory disorder treatment, eg bradycardia pacing, cardiac resynchronization pacing and / or anti-frequency Measured by interaction with cardiac pacing algorithms and / or one or more sleep quality indicators, as determined by the effect of cardiac pacing of respiratory disorders such as pulse pacing In view of the quality of sleep, it is possible to perform a treatment that reduces the impact of the treatment on the patient.

  The useful useful life of an implantable therapy device used to deliver respiratory therapy and / or pacing therapy for cardiac dysfunction may decrease with impact on the patient. For example, the level of respiratory disorder treatment may be unacceptably high if the energy condition of the treatment results in an excessive decrease in the useful life of the device. In this situation, premature device removal and replacement creates a negative impact on the patient. Thus, cardiac electrotherapy to alleviate breathing disorders can be adapted based on a planned decrease in device lifetime.

  In one implementation, the treatment methods described herein can be used within an advanced patient management system configuration. In this implementation, an advanced patient management system with adaptive respiratory disorder treatment allows a physician to remotely and automatically monitor heart and / or respiratory function and other patient conditions. Treatment can be initiated or modified if desired. In one example, an implantable cardiac rhythm management system, such as a cardiac pacemaker, defibrillator, or resynchronizer, or other device, equipped with various remote communication and information technologies to provide real-time patient data It is possible to enable collection, diagnosis and treatment.

  The flowchart of FIG. 1 illustrates a method for performing cardiac electrotherapy to treat a respiratory disorder according to an embodiment of the present invention. The method includes sensing 110 one or more conditions affecting the patient. Cardiac electrical therapy is adapted 120 to treat a respiratory disorder based on the sensed condition. The treatment can be adapted, for example, to achieve a desired treatment goal, to reduce the impact of the treatment, and / or to balance the effectiveness of the treatment with the treatment impact. Treatment impact includes situations that may result in patient stress, patient discomfort, poor sleep quality, interaction with other pacing algorithms, and / or a decrease in the life of the treatment device. An adapted therapy is delivered 130 to the patient. At least one of sensing the condition, adapting the treatment, and delivering the adapted treatment is at least partially implantable. Performing the implantable operation includes performing the operation using a component, device, or system that is partially or wholly implanted within the body.

  Once initiated, the system may continue to sense the condition affecting the patient and the treatment is updated periodically, eg, the detected occurrence of a disordered breathing, the predicted occurrence of a disordered breathing Modifications may be made based on various factors including assessment of treatment effectiveness, patient comfort during treatment, sleep quality during treatment, pacing, or other parameters.

  Treatment modification may include initiating treatment, ending treatment, or adjusting treatment parameters. In various scenarios, cardiac electrotherapy modifications can be performed during cardiac pacing using different pacing vectors, pacing in fewer or more cardiac chambers, increasing or decreasing pacing energy, subthreshold pacing, and / or resistance periods. Non-excitatory pacing such as pacing may be included.

  The block diagram of FIG. 2 illustrates a treatment system 200 that can be used to implement a disordered breathing treatment methodology in accordance with an embodiment of the present invention. FIG. 2 shows a system 200 that can be used to perform cardiac electrical therapy for respiratory disorders as well as cardiac rhythm therapy. Various heart rhythm therapies, including cardiac resynchronization treatments, among other single heart cardiac pacing, dual chamber cardiac pacing, biventricular cardiac pacing, defibrillation, cardioversion and / or other cardiac arrhythmia treatments Can be performed by the treatment system 200. Delivery of one or more cardiac rhythm therapies may occur separately or in cooperation with the delivery of a disordered breathing therapy. Exemplary embodiments include an implantable therapy control system, an implantable therapy delivery system, and a therapy system having an implantable sensor, but the therapy system 200 may be all or part of the system 200. However, it may be configured to be placed externally to the patient. Sensors and other components of the sensing system may include patient external sensors or components, patient internal sensors or components or a combination of patient external and patient internal sensors or other components.

  The therapy system 200 may include circuitry for performing cardiac rhythm management therapy in addition to and possibly in cooperation with the disordered breathing therapy. For example, the therapy controller 265 may control cardiac electrical stimulation therapy for cardiac arrhythmias including bradyarrhythmias and / or tachyarrhythmias. The system may include an arrhythmia detector 252. In this embodiment, cardiac electrical stimulation generator 232 is coupled to a lead system having an implanted electrode 231 for electrically coupling heart 230 to electrical stimulation generator 232.

  A cardiac therapy module 265 receives the cardiac signal from the implanted cardiac electrode 231 and analyzes the cardiac signal to determine an appropriate cardiac rhythm therapy. The cardiac rhythm therapy may include a pacing therapy controlled by the cardiac rhythm therapy controller 269 to treat a cardiac rhythm that is too slow. In this situation, the cardiac rhythm therapy controller 269 controls the cardiac stimulation circuit 232 to deliver periodic low energy pacing pulses to one or more cardiac chambers, Ensure that it is maintained at a sufficient hemodynamic rate.

  Cardiac treatment may also include treatment to terminate tachyarrhythmia if the heart rate is too fast. Cardiac arrhythmia detector 252 detects the onset of tachyarrhythmia such as ventricular tachycardia and / or fibrillation. The arrhythmia detector 252 recognizes a cardiac signal indicating tachyarrhythmia. The cardiac rhythm therapy controller can initiate delivery of high energy electrical pulses from the stimulus generator 232 to the heart 230 and terminate the arrhythmia in response to detecting a tachyarrhythmia. A condition affecting a patient can be detected using one or more patient internal sensors 280, one or more patient external sensors 290, and one or more patient input devices 270. One or more of sensors 280, 290 and / or input device 270 may be network-based. Sensors and / or other components of treatment system 200 may be coupled using a wired or wireless communication link. In one example, some or all of the patient internal sensor 280, patient external sensor 290, and patient input device 270 may use remote communication functions such as a wireless Bluetooth communication link or a proprietary wireless communication protocol. In one implementation, the wireless communication link couples the sensors 280, 290, input device 270 to other components of the treatment system 200.

  The therapy system 200 processes signals received from patient internal sensors 280, patient external sensors 290, and / or other input devices 270 used to detect physiological or non-physiological conditions. The signal processing circuit 250 is included. The patient internal sensor may include one or more implanted cardiac electrodes 231. The cardiac electrode 231, patient internal sensor 280, patient external sensor 290, and / or other input device 270 can be used to sense or detect one or more of the conditions listed in Table 1. Good.

  In one implementation, heart rate and tidal volume may be determined using cardiac and respiratory signals from an intracardiac electrocardiogram (EGM) sensor and transthoracic impedance sensor, respectively. EGM and transthoracic impedance signals may be used in conjunction with cardiac rhythm management and respiratory disorder treatment. The treatment system 200 may derive additional physiological and non-physiological patient conditions using additional sensors and input devices. For example, patient activity may be detected using an implantable accelerometer, calm sleep patient perception may be input using an external patient input device, and the patient's proximity to the bed May be detected using a proximity sensor to a bed containing both patient internal and patient external components.

  In one embodiment, treatment system 200 may include a disordered breath predictor / detector 258 that receives input from sensors and input devices 231, 280, 290, 270 via input signal processing circuit 250. The disordered breath predictor / detector 258 uses the sensed state to detect, predict and / or classify the onset of disordered breathing. In various embodiments, the disordered breathing detector / predictor may include circuitry for implementing a respiratory monitor that includes a user-accessible respiratory logbook, described in more detail herein.

  The disordered breathing predictor / detector 258 is coupled to a therapy controller 265 that includes a circuit 267 for controlling cardiac electrical stimulation therapy for the disordered breathing. Patient breathing, including breathing disorders, may be monitored by breath monitor 254. Respiration monitoring may include a phenomenon-based collection of medical information associated with a respiration phenomenon.

  A therapy for treating a disordered breathing can be initiated, corrected, or terminated by the disordered breathing therapy control circuit 267 based on, for example, detection or prediction of a disordered breathing. Additionally or alternatively, the disordered breathing therapy control circuit 267 can use the medical information collected by the respiratory monitor 254 to adapt the disordered breathing therapy.

  In accordance with an embodiment of the present invention, the therapy controller 265 can receive information related to sleep from the sleep detector 256. The sleep processor can use the sensed physiological or non-physiological state to detect sleep onset and cessation, sleep stages, and / or wakefulness. The disordered breathing therapy circuit, as well as the cardiac rhythm therapy circuit, can utilize sleep onset, stop, wakefulness, and / or sleep stage information to enhance the therapy delivered to the patient. In one example, the therapy controller 265 can coordinate cardiac rhythm pacing therapy and / or respiratory disorder therapy delivered to the patient during sleep.

  As mentioned above, breathing problems occur most frequently during sleep and may wake the patient from sleep repeatedly throughout the night. Thus, sleep information, eg, sleep start / stop, sleep stage, and / or sleep awakening information may be combined with respiratory disorder predictor / detector 258 in conjunction with respiratory disorder detection and / or prediction. For example, the disordered breath predictor / detector 258 can detect the disordered breathing phenomenon only during sleep or verify the initial detection / prediction of disordered breathing by determining if the patient is sleeping be able to.

  Sleep-related information can also be used by the sleep quality monitor 254. The sleep quality monitor may be, for example, the number of sleep disruptions, for example, respiratory disturbances that occur during sleep, the number and / or depth of wakefulness events associated with respiratory disturbances, and / or other quality of sleep Various aspects of sleep quality, including aspects, can be assessed and / or quantified. The sleep quality monitor can further determine the number of respiratory disturbance events that occur during various sleep stages, such as the rapid eye movement (REM) sleep stage and the non-REM sleep stage. The sleep quality monitor may include functionality for organizing and storing sleep-related information in a sleep logbook format that can be accessed via an interactive user interface. .

  In one embodiment, the treatment delivered by the system 200 is based on, for example, an assessment of the effectiveness of the treatment, the impact of the treatment on the patient, and / or the interaction between the disordered breathing treatment and other types of treatment. And can be adapted to achieve or maintain therapeutic goals. The system 200 may include, for example, a treatment assessment processor 260 configured to evaluate one or more of treatment effectiveness, impact, and interaction. The therapy assessment processor 260 includes a cardiac arrhythmia detector 252, a sleep quality monitor 254, a sleep processor 256, a respiratory disorder predictor / detector 258 and a heart sensor 231, other patient internal sensors 280, patient external sensors 290, and / or Input from other input devices 270 can be used.

  A therapy controller 265 that utilizes information from one or more of the components of the therapy system 200 controls the delivery of an appropriate cardiac electrical therapy to alleviate the respiratory disorder. In various exemplary treatments, pacing to relieve breathing disorders is paced at a rate above the intrinsic rate, at or above the patient's normal pacing rate, or at a rate above the patient's normal sleep rate. Pacing in selected modes, eg, both chamber or single chamber modes, or pacing at a predetermined energy level above or below the capture threshold. The pacing may include heart chambers, eg, any or all of the right and left atrium and right and left ventricles, and further includes multi-site pacing within one heart chamber. Good. In one example, pacing pulses can be delivered to the left and right ventricles approximately simultaneously or in other timing sequences. Cardiac electrical stimulation therapy for breathing disorders can include non-excitable pacing, such as sub-threshold pacing and / or pacing during resistance periods. Cardiac electrical therapy for respiratory disorders can be coordinated with therapy given to the patient to treat cardiac rhythm disorders including, for example, bradycardia and / or cardiac resynchronization therapy.

  FIG. 3 is a partial view of an implantable device that may include a circuit for performing cardiac stimulation therapy for a respiratory disorder according to an embodiment of the present invention. In this example, the implantable device includes a cardiac rhythm management device (CRM) 300 that includes an implantable electrical stimulation generator 305 that is electrically and physically coupled to an intracardiac lead system 310. Yes. The portion of the lead system 310 within the heart is inserted into the patient's heart 390. The intracardiac lead system 310 senses the heart's electrical heart activity, delivers electrical stimulation to the heart, senses the patient's transthoracic impedance, and / or other physiological parameters, eg, cardiac It includes one or more electrodes configured to sense room pressure or temperature. The portion of the housing 301 of the pulse generator 305 may optionally be used as a can electrode.

  A communication circuit is disposed within the housing 301 and has electrical stimulation generator 305 and wireless communication functionality such as a portable or bedside communication station, a patient carrying / wearing communication station, or an external programmer. It facilitates communication with remote devices.

  The wireless communication circuit is unidirectional or both with one or more implanted, external, cutaneous, or subcutaneous physiological or non-physiological sensors, patient input devices, and / or other information systems. Directional communication can also be facilitated.

  The housing 301 of the electrical stimulus generator 305 may optionally incorporate various sensors including, for example, behavioral sensors that can be programmed to sense various conditions. For example, the behavior sensor may optionally be configured to sense chest wall motion associated with, for example, snoring, patient activity level, and / or respiratory effort. In one example implementation, the behavior detector may be implemented as an accelerometer positioned in or on the housing 301 of the electrical stimulus generator 305. If the behavioral sensor is implemented as an accelerometer, the behavioral sensor provides breathing information such as rales, cough, and heart information such as S1-S4 heart sounds, noise, and / or other acoustic information. Also good.

  The lead system 310 of the CRM 300 may incorporate one or more electrodes that are used to sense transthoracic impedance. Transthoracic impedance sensing can be used to acquire a patient's respiratory waveform, or other respiratory related information. Transthoracic impedance can be sensed using one or more intra-cardiac electrodes 341, 342, 351-355, 363 located in one or more rooms of heart 390. The electrodes 341, 342, 351-355, 363 in the heart can be coupled to an impedance drive / sense circuit located within the housing of the electrical stimulus generator 305.

  In one implementation, an impedance drive / sense circuit disposed within the housing 301 generates a current that flows through the tissue between the impedance drive electrode 351 and the can electrode in the housing 301 of the electrical stimulus generator 305. . The voltage at the impedance sensing electrode 352 relative to the can electrode changes as the patient's transthoracic impedance changes. The voltage signal appearing between the impedance sensing electrode 352 and the can electrode is detected by an impedance sensing circuit disposed in the housing 301 of the electrical stimulus generator 305. Other locations and / or combinations of impedance sensing and drive electrodes are possible.

  The voltage signal appearing on the impedance sensing electrode 352 shown in FIG. 5A is proportional to the patient's transthoracic impedance and represents the patient's respiratory waveform. Transthoracic impedance increases during respiratory inspiration 510 and decreases during respiratory exhalation 520. The transition from transthoracic impedance peak to peak is proportional to the amount of air moving in one breath, expressed as tidal volume. The amount of air moving per minute is expressed in minute ventilation. A normal “rest” breathing pattern, such as during non-REM sleep, includes a regular, rhythmic inspiration / expiration cycle without significant interruption, as shown in FIG. 5A.

  Returning to FIG. 3, the lead system 310 senses electrical signals from the patient's heart 390 and / or in one or more heart chambers to deliver pacing pulses to the heart 390. One or more cardiac pace / sense electrodes 351-355 may be included disposed on or around the top.

  The intracardiac sensing / pace electrodes 351-355 shown in FIG. 3 sense one or more cardiac chambers, including the left ventricle, right ventricle, left atrium and / or right atrium, and / or Can be used for pacing. The lead system 310 may include one or more defibrillation electrodes 341, 342 for delivering a defibrillation / cardioversion shock to the heart 390.

  As described above, the housing 301 of the electrical stimulation generator 305 detects cardiac arrhythmias and / or is paced and / or in the form of electrical stimulation pulses or shocks delivered to the heart via the lead system 310. ) It may include circuitry for controlling defibrillation therapy. Also included within housing 301 is a circuit for controlling cardiac electrical stimulation therapy for respiratory disorders, and / or a diagram, including sleep quality monitor 254, sleep processor 256, respiratory disorder detector / predictor 258, treatment assessment processor 260. The treatment controller circuit 265 described with reference to 2 may be provided.

  According to another embodiment, an implantable transthoracic heart sensing and / or stimulation (ITCS) device can be implemented to detect / monitor normal and abnormal heart and / or respiratory activity, and abnormal In response to the activity or condition, it can be configured to deliver an appropriate treatment. The ITCS device 400 of the present invention can be configured to monitor, diagnose, and / or treat heart and respiratory disorders / conditions. The ITCS device 400 is generally implemented to sense both cardiac and respiratory activity. The ITCS device 400 can be implemented to detect and monitor various respiratory disorders states, including sleep and non-sleep related respiratory disorders using appropriate sensors. The ITCS device 400 can be further implemented to detect sleep and further to detect the stage of patient sleep. The ITCS device 400 so implemented provides various sensing, monitoring, diagnostic, and / or therapeutic control / coordination functions, including those described herein and in each of the cited references incorporated herein. Can be configured to do.

  FIG. 4A is a block diagram illustrating various components of an ITCS device 400 that performs respiratory disorder detection and / or treatment according to an embodiment of the present invention. In general, the use of an ITCS device 400 as shown in FIG. 4A detects, monitors and / or treats cardiac activity and breathing disorders (eg, sleep breathing disorder and wake breathing disorder). be able to. The ITCS device 400 can be implanted under the skin in the patient's chest region. The ITCS device 400 is located on the front, back, side or other body part of the patient where all or selected elements of the device are suitable for sensing cardiac activity and delivering cardiac stimulation therapy. For example, it can be implanted subcutaneously. The elements of the ITCS device 400 can be positioned at several different body parts, such as the chest, abdomen, or subclavicular area, with the electrode elements each positioned near, around, or above the heart I understand.

  For example, the main housing (eg, active or inactive can) 402 of the ITCS device 400 can be an intercostal or rib in the abdomen or in the upper chest region (eg, a subclavian area, such as above the third rib). It may be configured to be located outside the rib cage at the lower site. In one implementation, one or more electrodes are placed on the main housing 402 and / or other sites around, but not in direct contact with, the heart, large blood vessels, or annular arterial vasculature (eg, electrodes). 404). The main housing 402 is provided with a pulse generator and a cardiac stimulation controller. The cardiac stimulator controller determines and coordinates the appropriate heart and / or breathing therapy to be delivered to the patient, and the pulse generator produces the appropriate energy waveform associated with the selected therapy. Also disposed within the main housing 402 is a heart activity detector configured to detect normal and abnormal (eg, arrhythmia) heart activity.

  In a further implementation, one or more subcutaneous electrode subsystems or electrode arrays 404 sense cardiac activity in an ITCS device 400 configuration using an active can or a configuration using an inactive can, and Can be used to deliver cardiac stimulation energy. An electrode (eg, electrode 404) may be located at a site anterior and / or posterior to the heart.

  The ITCS device 400 shown in FIG. 4A may be configured in the manner described above, or may have other configurations. The ITCS device 400 of the present invention includes a heart and / or respiration detection / monitoring circuit (eg, heart activity, respiratory pattern due to transthoracic impedance signal, heart sound, blood gas / oxygen saturation and / or pH, etc.) Can be implemented to include one or more of a cardiac and respiratory diagnostic circuit, and a cardiac and respiratory treatment circuit. The ITCS device 400 of the present invention can be implemented with upgradeability in terms of functionality and / or configuration. For example, the ITCS device 400 can be implemented as an upgradeable or reconfigurable cardiac / respiratory monitor or stimulator.

  The ITCS device 400 according to an embodiment of the present invention performs patient respiratory monitoring and respiratory disorder detection and / or prediction. Such embodiments may further include, for example, signals received from an advanced patient management system or programmer, as determined by the therapy controller, for a detected or predicted disordered breathing event or condition, etc. ) An action can be taken in response to an externally generated command signal. Detection and treatment of breathing disorders and / or breathing conditions can be accomplished by using the ITCS device 400 with appropriate sensing / detection / therapeutic delivery capabilities, or via a communication interface and an external programmer or advancement. This can be facilitated by the cooperative use of patient management systems.

  With continued reference to FIG. 4A, the ITCS device 400 includes a housing 402 within which various heart and respiratory sensing, detection, processing, and energy delivery circuits can be housed. Communication circuitry is within housing 402 to facilitate communication between ITCS device 400 and an external communication device such as, for example, a portable or bedside communication station, a patient carrying and wearing communication station, or an external programmer. It is arranged. The communication circuit may also facilitate one-way or two-way communication with one or more external, skin, or subcutaneous physiological or non-physiological sensors. The housing 402 is generally configured to include one or more electrodes (eg, a can electrode and / or an indifferent electrode). Although the housing 402 is generally configured as an active can, it will be appreciated that an inactive can configuration can be realized, in which case at least two electrodes spaced from the housing 402 are used.

  In the configuration shown in FIG. 4A, the subcutaneous electrode 404 can be placed under the skin in the thoracic region and positioned distally from the housing 402. Subcutaneous and, if applicable, housing electrode (s) are placed around the heart at various sites and orientations, such as various anterior and / or posterior sites relative to the heart. be able to. Subcutaneous electrode 404 is coupled to circuitry in housing 402 via lead assembly 406. One or more conductors (eg, coils or cables) are provided in the lead assembly 406 and electrically couple the subcutaneous electrode 404 and circuitry in the housing 402. One or more sensing, sensing / pacing or defibrillation electrodes may be attached to the electrode support extension structure, housing 402, and / or distal electrode assembly (in the configuration shown in FIG. 4A, subcutaneous electrode 404). (Shown as).

  In one configuration, the lead assembly 406 is generally flexible and has a structure similar to a conventional implantable medical electrical lead (eg, a defibrillation lead or a combined defibrillation / pacing lead). is doing. In another configuration, the lead assembly 406 is made to be somewhat flexible, yet has an elastic, spring, or mechanical memory that maintains the desired configuration even after being shaped or manipulated by the clinician. Have. For example, the lead assembly 406 may incorporate a gooseneck or braid system that can be bent with hand force to assume a desired shape. In this manner, the lead assembly 406 can be conformed to accommodate the unique anatomical configuration of a particular patient, and generally maintains a customized shape after implantation. The shaping of the lead assembly 406 with this configuration may be performed before and during implantation of the ITCS device 400.

  According to a further configuration, the lead assembly 406 includes an electrode support assembly such as an extension structure that positionally stabilizes the subcutaneous electrode 404 relative to the housing 402. In this configuration, the stiffness of the stretch structure maintains the desired spacing between the subcutaneous electrode 404 and the housing 402 and the desired orientation of the subcutaneous electrode 104 / housing 402 relative to the patient's heart. The stretch structure may be formed from a structural plastic, composite or metal material and includes or is coated with a biocompatible material. Proper electrical insulation between the housing 402 and the subcutaneous electrode 404 is provided when the stretch structure is formed from a conductive material such as metal.

  In one configuration, the electrode support assembly and housing 402 define a unit configuration (eg, a single housing / unit). The electronic components and electrode conductors / connectors are disposed in or on the unit ITCS device 400 housing / electrode support assembly. At least two electrodes are supported on the unit configuration near opposite ends of the housing / electrode support assembly. The unit configuration may have, for example, an arcuate or angled shape.

  According to another configuration, the electrode support assembly defines a unit that is physically separable from the housing 402. The electrode support assembly includes a mechanical and electrical coupling that facilitates engagement with a corresponding mechanical and electrical coupling of the housing 402. For example, the header block device may be configured to include both electrical and mechanical couplings that provide mechanical and electrical connections between the electrode support assembly and the housing 402. The header block device may be provided on the housing 402 or electrode support assembly. Alternatively, a mechanical / electrical coupler may be used to establish a mechanical and electrical connection between the electrode support assembly and the housing 402. In such a configuration, a variety of different electrode support assemblies, varying in shape, size, and electrode configuration, may be made available for physical and electrical connection to the standard ITCS device 400 housing 402. .

  It is noted that the electrode and lead assembly 406 may be configured to take various shapes. For example, the lead assembly 406 may have a wedge shape, an inverted V shape, a flat oval shape, or a ribbon shape, and the subcutaneous electrode 404 is spaced a number of intervals such as an array or band of electrodes. An electrode may be included. Further, two or more subcutaneous electrodes 404 may be attached to the multiple electrode support assembly 406 to achieve the desired spacing relationship between the subcutaneous electrodes 404.

  FIG. 4B is a block diagram illustrating various components of ITCS device 400 according to one configuration. According to this configuration, the ITCS device 400 incorporates a processor-based control system 405 that includes a microprocessor 426 coupled to appropriate memory (volatile and non-volatile) 409 and uses any logic-based control architecture. I understand that I can do it. The control system 405 is a circuit and component for delivering electrical stimulation energy to the heart in a predetermined state for sensing, detecting and analyzing electrical signals generated by the heart and treating cardiac arrhythmias Is bound to. In some configurations, the control system 405 and related components also provide pacing therapy to the heart. The electrical energy delivered by the ITCS device 400 may be in the form of a low energy pacing pulse, non-excitable energy (eg, subthreshold stimulation energy) or a high energy pulse for cardioversion or defibrillation. .

  The cardiac signal is sensed using the subcutaneous electrode (s) 414 and the can or indifferent electrode 407 provided on the ITCS device 400 housing. A cardiac signal can also be sensed using a subcutaneous electrode 414, such as in an inactive can configuration. As such, monopolar, bipolar, or combination monopolar / bipolar electrode configurations and combinations of multi-element and noise canceling electrodes and standard electrodes may be used. The sensed cardiac signal is received by sensing circuit 424, which includes a sense amplifier circuit and may include a filter circuit and an analog to digital (A / D) converter. The sensed cardiac signal processed by the sensing circuit 424 can be received by the noise reduction circuit 403, which can further reduce the noise before the signal is sent to the detection circuit 422.

  The noise reduction circuit 403 may be incorporated after the sensing circuit 422 when a high power or computationally intensive noise reduction algorithm is required. The noise reduction circuit 403 may perform the function of the sensing circuit 424 by an amplifier that is used to perform operations based on electrode signals. Combining the functionality of sensing circuit 424 and noise reduction circuit 403 may be useful in minimizing the required componentry and reducing system power requirements.

  In the exemplary configuration shown in FIG. 4B, the detection circuit 422 is coupled to the noise reduction circuit 403 or incorporates the noise reduction circuit 403 differently. The noise reduction circuit 403 operates to improve the signal-to-noise ratio (SNR) of the sensed heart signal by removing noise from the sensed heart signal introduced from various sources. Typical types of transthoracic heart signal noise include, for example, electrical noise and noise arising from skeletal muscle.

  The detection circuit 422 generally includes a signal processor that coordinates analysis of sensed cardiac signals and / or other sensor inputs to detect cardiac arrhythmias, particularly tachyarrhythmias. Velocity-based and / or morphological discrimination algorithms may be performed by the signal processor of the detection circuit 422 that detects and verifies the presence and severity of arrhythmia episodes.

  The detection circuit 422 communicates cardiac signal information to the control system 405. The memory circuit 409 of the control system 405 includes various sensing, defibrillation and, if applicable, parameters that operate in the pacing mode, and stores data indicative of the cardiac signal received by the detection circuit 422. is doing. The memory circuit 409 may also be configured to store historical ECG and treatment data, which may be used for various purposes and externally received as needed or desired. It may be sent to the device.

  In some configurations, the ITCS device 400 may include a diagnostic circuit 410. Diagnostic circuit 410 generally receives input signals from detection circuit 422 and sensing circuit 424. It will be appreciated that the diagnostic circuit 410 provides diagnostic data to the control system 405, and thus the control system 405 may incorporate all or part of the diagnostic circuit 410, or functionality thereof. The control system 405 can store and use information provided by the diagnostic circuit 410 for various diagnostic purposes. This diagnostic information can be stored, for example, following a triggering event or at predetermined intervals, and includes system diagnostics such as power status, treatment delivery history, and / or patient diagnostics. You can leave. Diagnostic information may take the form of electrical signals or other sensor data acquired immediately prior to treatment delivery.

  According to the configuration for providing cardioversion and defibrillation therapy, the control system 405 processes the cardiac signal data received from the detection circuit 422 and terminates the onset of cardiac arrhythmia and normalizes the heart. Initiate appropriate tachyarrhythmia therapy to restore sinus rhythm. Control system 405 is coupled to shock therapy circuit 416. The shock therapy circuit 416 is coupled to the ITCS device 400 housing subcutaneous electrode (s) 414 and the can or indifferent electrode 407. By command, shock therapy circuit 416 delivers cardioversion and defibrillation stimulation energy to the heart in accordance with the selected cardioversion or defibrillation therapy. In lower altitude configurations, shock therapy circuit 416 is controlled to deliver defibrillation therapy, unlike configurations that provide both cardioversion and defibrillation therapy delivery.

  According to another configuration, the ITCS device 400 may incorporate cardiac pacing functions in addition to cardioversion and / or defibrillation functions. As indicated by the dotted lines in FIG. 4B, the ITCS device 400 may include a pacing therapy circuit 430 that is coupled to the control system 405 and the subcutaneous and can / indifferent electrodes 414, 407. . By command, the pacing therapy circuit delivers pacing pulses to the heart according to the selected pacing therapy. When initiated, the control signal produced by the pacemaker circuit in the control system 405 according to the pacing measure is transmitted to the pacing therapy circuit 430, where a pacing pulse is generated. The pacing measure can be modified by the control system 405.

  For transthoracic heart monitoring and / or stimulation devices, a number of cardiac pacing therapies may be useful. Such cardiac pacing therapy can be delivered via a pacing therapy circuit 430, as shown in FIG. 4B. Alternatively, cardiac pacing therapy may be delivered via shock therapy circuit 416, which effectively eliminates the need for a separate pacemaker circuit.

  The ITCS device 400 shown in FIG. 4B is configured to receive signals from one or more physiological and / or non-physiological sensors. Depending on the type of sensor being used, the signal generated by the sensor may be transmitted to a converter circuit that is coupled directly to the detection circuit 422 or indirectly through the sensing circuit 424. It is noted that some sensors can transmit sensing data to the control system 405 without processing by the detection circuit 422.

  Non-electrophysiological heart sensor 461 may be coupled to detection circuit 422 directly or indirectly through sensing circuit 424. The non-electrophysiological heart sensor 461 actually senses non-electrophysiological heart activity. Examples of non-electrophysiological heart sensors 461 include blood oxygen sensors, transthoracic impedance sensors, blood volume sensors, acoustic sensors and / or pressure transducers, and accelerometers. Signals from these sensors are generated based on cardiac activity, but do not come directly from electrophysiological sources (eg, R-waves or P-waves). Non-electrophysiological heart sensor 461 is connected to one or more of sensing circuit 424, detection circuit 422 (connection not shown for clarity) and control system 405, as shown in FIG. 4B. It's okay.

  Communication circuit 418 is coupled to microprocessor 426 of control system 405. Communication circuit 418 enables ITCS device 400 to communicate with one or more receiving devices or systems located outside ITCS device 400. According to this example, the ITCS device 400 can communicate via a communication circuit 418 with a portable or bedside communication system worn by the patient. In one configuration, one or more physiological or non-physiological sensors (subcutaneous, skin, or external to the patient) are short-range such as an interface compliant with known communication standards such as Bluetooth or IEEE 802 standards. A wireless communication interface may be provided. Data acquired by such sensors may be communicated to the ITCS device 400 via the communication circuit 418. It is noted that a physiological or non-physiological sensor equipped with a wireless transmitter or transceiver can communicate with a receiving system external to the patient.

  The communication circuit 418 allows the ITCS device 400 to communicate with an external programmer. In one configuration, the communication circuit 418 and programmer unit (not shown) use a wire loop antenna and a radio frequency telemetric link between the programmer unit and the communication circuit 418, as is known in the art. Receive and transmit signals and data. In this manner, programming instructions and data are transferred between the ITCS device 400 and the programmer unit during and after the transplant. The physician can set or modify various parameters used by the ITCS device 400 using a programmer. For example, a physician can set or modify parameters that affect the sensing, detection, pacing, and defibrillation functions of the ITCS device 400, including pacing and cardioversion / defibrillation therapy modes.

  In general, the ITCS device 400 is housed and sealed in a housing suitable for implantation in the human body, as is known in the art. Electric power for the ITCS device 400 is supplied by an electrochemical power source 420 stored in the ITCS device 400. In one configuration, the power source 420 includes a rechargeable battery. According to this configuration, the charging circuit is coupled to the power source 420 to facilitate repeated non-invasive charging of the power source 420. The communication circuit 418, or a separate receiver circuit, is configured to receive RF energy that is transmitted to an external RF energy transmitter. ITCS device 400 may include a non-rechargeable battery in addition to a rechargeable power source. It is noted that if a long-life and non-rechargeable battery is used, it is not necessary to use a rechargeable power source.

  The components, functionality, and structural configurations shown in FIGS. 4A-4E are intended to provide an understanding of the various features and combinations of features that may be incorporated into the ITCS device 400. It can be seen that a wide range of ITCS and other implantable cardiac monitoring and / or stimulator configurations are contemplated, ranging from relatively advanced designs to relatively simple designs. As such, certain ITCS or cardiac monitoring and / or stimulation device configurations may include certain features as described herein, while other such device configurations are described herein. It is not necessary to include such a specific feature.

  According to embodiments of the present invention, ITCS device 400 may be implemented to include a subcutaneous electrode system that provides one or both of cardiac sensing and arrhythmia therapy delivery. According to one method, the ITCS device 400 may be implemented as a chronically implantable system that performs monitoring, diagnostic and / or therapeutic functions. The ITCS device 400 may automatically detect and treat cardiac arrhythmias.

  In one configuration, the ITCS device 400 includes a pulse generator and one or more electrodes that are implanted subcutaneously in the breast region of the body, such as in the chest region in front of the body. The ITCS device 400 may be used to perform atrial and / or ventricular treatment for bradycardia and tachyarrhythmia. Tachyarrhythmia therapy may include, for example, cardioversion, defibrillation and anti-tachycardia pacing (ATP) to treat atrial or ventricular tachycardia or fibrillation. Bradycardia treatment may include temporary post-shock pacing for bradycardia or asystole.

  In one configuration, the ITCS device 400 according to one method may utilize conventional pulse generator and subcutaneous electrode implantation techniques. The pulse generator device and electrodes may be chronically implanted subcutaneously. Such ITCS may be used to automatically detect and treat arrhythmias, similar to conventional implantable systems. In another configuration, the ITCS device 400 may include a unit configuration (eg, a single housing / unit). The electronic components and electrode conductors / connectors are disposed in or on the unit ITCS device 400 housing / electrode support assembly.

  The ITCS device 400 includes an electronic device and may be similar to a conventional implantable defibrillator. High voltage shock therapy may be delivered between two or more electrodes, one of which may be a pulse generator housing (eg, can) placed subcutaneously in the chest region of the body.

  Additionally or alternatively, the ITCS device 400 may provide lower energy electrical stimulation for bradycardia treatment. The ITCS device 400 may perform bradycardia pacing, similar to a conventional pacemaker. The ITCS device 400 may perform temporary post-shock pacing for bradycardia or asystole. Sensing and / or pacing may be performed using a sensing / pace electrode located in an electrode subsystem that also incorporates a shock electrode, or by a separate electrode implanted subcutaneously.

  The ITCS device 400 can detect various physiological signals that can be used in conjunction with various diagnostic, therapeutic or monitoring implementations according to the present invention. For example, the ITCS device 400 may include sensors or circuits for detecting pulse pressure signals, blood oxygen levels, heart sounds, heart acceleration, and other non-electrophysiological signals related to heart activity. In one embodiment, the ITCS device 400 may sense impedance in the thoracic cavity and then derive various respiratory parameters including, for example, respiratory tidal volume and minute ventilation. Sensors and associated circuitry may be incorporated in connection with the ITCS device 400 to detect one or more body movement or body position related signals. For example, accelerometers and GPS devices may be used to detect patient activity, patient site, body orientation, or torso position.

  The ITCS device 400 may be used in the configuration of an APM system. The APM system may allow a physician to remotely and automatically monitor heart and respiratory function and other patient conditions. In one example, implantable cardiac rhythm management systems, such as cardiac pacemakers, defibrillators, and resynchronizers, use a variety of remote communication and information technologies that enable real-time patient data collection, diagnosis, and treatment. May be equipped. The various embodiments described herein may be used in conjunction with advanced patient management.

  The ITCS device 400 according to one method is an implantable treatment, diagnosis or monitoring system. The ITCS system can be implanted without the need for intravenous or intrathoracic access, has a simpler and less invasive implantation procedure, and minimizes lead and surgical complications. This system may also be advantageous when used on patients whose transvenous lead system causes complications. Such complications include, among other things, surgical complications, infections, inadequate patency, complications associated with the presence of prosthetic valves, and limitations in pediatric patients due to patient growth. It is not limited to. An ITCS system according to this method differs from conventional methods in that it can be configured to include a combination of two or more electrode subsystems implanted subcutaneously in the anterior thorax.

  In one ITCS system configuration, as shown in FIG. 4C, the electrode subsystem of the ITCS system includes a first electrode subsystem that includes a can electrode 433 and a second electrode that may include, for example, at least one coil electrode. An electrode subsystem 435 is included. The second electrode subsystem 435 may include a number of electrodes used for sensing and / or electrical stimulation. In various configurations, the second electrode subsystem 435 may include a single electrode or a combination of electrodes. A single electrode or combination of electrodes including the second electrode subsystem 435 includes, for example, a coil electrode, a tip electrode, a ring electrode, a multi-element coil, a spiral coil, a spiral coil attached to a non-conductive backing, and a screen A patch electrode may be included. A suitable non-conductive backing material is, for example, silicone rubber.

  The can electrode 433 is positioned on the housing 431 that encloses the ITCS device 400 electronic device. In one embodiment, the can electrode 433 includes the entire outer surface of the housing 431. In other embodiments, various portions of the housing 431 may be electrically isolated from the can electrode 433 or from tissue. For example, the active area of the can electrode 433 may include all or a portion of the surface of either the front or back side of the housing 431 to conduct current in a manner that favors cardiac sensing and / or stimulation.

The housing 431 may resemble that of a conventional implantable ICD, volume of about 20 to 100 cc, a thickness of 0.4 to 2 cm, the surface area of each surface is about 30 to 100 cm ^2. As described above, the housing portion may be electrically isolated from the tissue to optimally conduct current. For example, the portion of the housing 431 may be covered with a non-conductive or otherwise electrically resistive material to conduct current. Suitable non-conductive material coatings include, for example, those formed from silicone rubber, polyurethane, or parylene.

  FIG. 4CA shows a can electrode 433 placed subcutaneously above the heart 440 in the region of the left pectoral muscle, which is the site used for the housing 431 and generally conventional pacemaker and defibrillator implants. The second electrode subsystem 435 may include a coil electrode attached to the distal end of the lead body 437, where the coil is about 3-15 Fr in diameter and 5-12 cm in length. . The coil electrode may have a slightly preformed curve along its length. The lead may be introduced through the lumen of the subcutaneous sheath by conventional tunneling implantation, and the second electrode subsystem 435, including for example a coil electrode, is deep with respect to any subcutaneous fat, And it may be placed subcutaneously adjacent to the underlying muscle layer.

  In this configuration, the second electrode subsystem 435 is positioned directly below the free wall of the right ventricle, approximately parallel to the lower surface of the right ventricle of the heart 440, with one end slightly extending the apex of the heart 440. It stretches a little beyond. For example, the tip of electrode subsystem 435 may extend about 3 cm or less and may be about 1-2 cm to the left of the apex of heart 440. This electrode arrangement may be used to include most of the ventricular tissue within the volume defined between the housing 431 and the second electrode subsystem 435. In one configuration, the majority of ventricular tissue is bounded by a line drawn between the distal and proximal ends of the second electrode subsystem 435 and the lateral edge of the left pectoral muscle of the can electrode 433. Contained within the volume associated with the defined region.

  In one arrangement, the volume containing the majority of ventricular tissue is bounded by a line drawn between the ends of the electrode subsystems 433, 435 or between the active elements of the electrode subsystems 433, 435. It may be associated with the attached cross-sectional area. In one implementation, lines drawn between the active elements of the electrode subsystems 433, 435 are utilized in the central and lateral edges of the can electrode 433 and in the second electrode subsystem 435. A proximal end and a distal end of the coil electrode may be included. If the electrode subsystem is positioned so that the majority of ventricular tissue is contained within the volume defined between the active elements of the electrode subsystems 433, 435, then there is between the electrode subsystems 433, 435. The voltage gradient in the ventricle of the heart 440 for a given applied voltage is increased, providing an efficient position for defibrillation.

  In a similar configuration, as shown in FIG. 4D, the housing 431 including the can electrode 433 is disposed in the right pectoral muscle region. The second electrode subsystem 435 is positioned more laterally to re-include most of the ventricular tissue within the volume defined between the can electrode 433 and the second electrode subsystem 435.

  In a further configuration, as shown in FIG. 4E, the ITCS device housing 431 containing the electronic device (ie, can) is not used as an electrode. In this case, an electrode system comprising two electrode subsystems 438, 439 coupled to the housing 431 may be implanted subcutaneously in the thoracic region of the body, such as in the anterior rib cage. The first and second electrode subsystems 438, 439 are located at opposite positions relative to the ventricle of the heart 440, and the majority of the ventricular tissue of the heart 440 is defined between the electrode subsystems 438, 439. Contained within the specified volume. As shown in FIG. 4E, the first electrode system 438 is positioned above the heart 440 relative to the upper surface of the heart 440, eg, parallel to the free wall of the left ventricle. The second electrode system 439 is positioned below the heart 440 and is disposed relative to the lower surface of the heart 440, for example, parallel to the free wall of the right ventricle.

  In this configuration, the first and second electrode subsystems 438, 439 may include any combination of electrodes, with or without can electrodes used for sensing and / or electrical stimulation. In various configurations, the electrode subsystems 438, 439 may each be a single electrode or a combination of electrodes. An electrode or group of electrodes including first and second electrode subsystems 438, 439 is attached to, for example, one or more coil electrodes, tip electrodes, ring electrodes, multi-element coils, spiral coils, non-conductive backings Any combination of spiral coils and screen patch electrodes may be included.

4F-4H are additional detailed views of a subcutaneous electrode subsystem installation that may be particularly useful with an ITCS device incorporating respiratory disorder detection according to embodiments of the present invention. FIG. 4F shows first and second electrode subsystems configured as a can electrode 462 and a coil electrode 464, respectively. FIG. 4F shows the can electrode 462 positioned above the heart 460 in the region of the left pectoral muscle and the coil electrode 464 positioned below the heart 460 and parallel to the right ventricular free wall of the heart 460.
The can electrode 462 and the coil electrode 464 are positioned such that the majority of ventricular tissue is contained within the volume defined between the can electrode 462 and the coil electrode 464. FIG. 4F shows a cross-sectional area 465 formed by a line drawn between the can electrode 462 and coil electrode 464 active elements. The lines drawn between the active regions of the electrodes 462, 464 are the central and lateral edges of the can electrode 462, and the proximal end and distal end of the coil electrode utilized as the second electrode subsystem 464. May be defined by the end of the. The coil electrode 464 extends beyond the apex of the heart 460 by a predetermined distance, for example, about 3 cm or less.

  FIG. 4G shows a similar configuration. In this embodiment, the can electrode 462 is placed above the heart 460 in the right pectoral muscle region. The coil electrode 464 is positioned below the heart. In one arrangement, the coil electrode is positioned relative to the lower surface of the heart 460, eg, the apex of the heart. The can electrode 462 and the coil electrode 464 are arranged so that most of the ventricular tissue is contained within the volume defined between the can electrode 462 and the coil electrode 464.

  FIG. 4G shows a cross-sectional area 465 formed by a line drawn between the can electrode 462 and coil electrode 464 active elements. The lines drawn between the active regions of the electrodes 462, 464 are the central and lateral edges of the can electrode 462, and the proximal end and distal end of the coil electrode utilized as the second electrode subsystem 464. May be defined by the end of the. The coil electrode 464 extends beyond the apex of the heart 460 by a predetermined distance, for example, about 3 cm or less.

  FIG. 4H shows a configuration where the pulse generator housing 461 does not include electrodes. In this implementation, the two electrode subsystems are positioned around the heart such that the majority of ventricular tissue is contained within the volume defined between the electrode subsystems. According to this embodiment, the first and second electrodes are configured as first and second coil electrodes 468 and 469.

  The first coil electrode 468 is positioned above the heart 460 and may be positioned relative to the upper surface of the heart, eg, the free wall of the left ventricle. The second coil electrode 469 is positioned below the heart 460. The second electrode 469 may be positioned relative to the lower surface of the heart 460. In one configuration, the second electrode 469 is positioned parallel to the free wall of the right ventricle and the tip of the electrode 469 extends beyond the apex of the heart 460 by no more than about 3 cm. . As shown in FIG. 4H, the volume defined between the electrodes may be defined by a cross-sectional area 465 bounded by a line drawn between the active areas of electrodes 468, 469.

Respiratory Disorder Detection According to various embodiments of the present invention, the detection of a respiratory disorder phenomenon can be used in conjunction with the adaptation of a therapy to treat a respiratory disorder. In one embodiment, the detection and assessment of disordered breathing is used to tailor (start, modify and / or terminate) therapy delivery. In another embodiment, disordered breathing events detected during and / or after therapy delivery can be used to assess the effectiveness of the disordered breathing therapy. In various implementations (some of which are described below), the occurrence of a disordered breathing is detected by analyzing the patient's breathing pattern and / or other conditions associated with the disordered breathing, and Can be classified.

  According to one embodiment of the present invention, cardiac electrical therapy for a disordered breathing can be adapted based on the detected onset of the disordered breathing. In one scenario, one or more episodes of respiratory disorder are detected and cardiac electrical therapy is initiated or augmented to treat the detected episodes. Treatment adaptation can continue, allowing the system to deliver a therapeutically appropriate treatment throughout the onset or group of respiratory disorders. If the system determines that the disordered breathing has alleviated or has ended, the treatment may be reduced or terminated. Treatment may continue even after the onset of breathing disorders has stopped to prevent future occurrences of breathing disorders.

  Table 1 shows a preferred set of conditions that can be used to detect respiratory disturbances and / or to adapt cardiac electrotherapy. The list shown in Table 1 is not limiting and the use of other or additional conditions to detect respiratory disturbances and / or to adapt treatments is possible. A condition may include both physiological and non-physiological conditions. Physiological conditions may encompass a wide range of conditions associated with physiological conditions inside the patient. Physiological conditions can also include, for example, respiratory quality, sleep quality, and patient, as well as cardiovascular, respiratory and nervous system status, blood chemistry, body related status (eg, position and activity). ).

  A non-physiological state may include a contextual state that relates to the external or background state of the patient. Non-physiological conditions may be broadly defined to include conditions related to the patient's current environment, including, for example, the patient's location, ambient temperature, ambient humidity, air pollution indicators. Non-physiological / contextual conditions may include historical / background conditions related to the patient, including, for example, the patient's normal sleep time and the patient's treatment history.

  Table 1 shows a representative set of conditions affecting a patient that can be used to detect respiratory disorders and / or to adapt a disordered breathing therapy. Table 1 also shows example sensing methods that can be used to sense the condition.

  The detection of a respiratory disorder may include the detection of one or more conditions indicative of the respiratory disorder listed in Table 1. The patient conditions listed in Table 1 may be used in a multi-sensor method to detect and confirm the onset of respiratory disorders. For example, the accuracy of detection of preliminary respiratory disturbances can be determined by whether the patient is asleep, on the floor, inactive, lying down, or the current environmental condition is the patient's respiratory disorder By verifying that it is related to

  Table 2 shows an example of how a representative subset of the physiological and non-physiological conditions listed in Table 1 can be used in conjunction with the detection of respiratory impairment.

  The development of respiratory disorders is associated with acute and chronic physiological effects. Acute responses to breathing disorders may include, for example, intrathoracic negative pressure, hypoxia, sleep awakening, and increased blood pressure and heart rate. Negative pressure in the thoracic cavity may result from increased efforts to create airflow during the development of obstructive apnea. Inspiratory effort in the presence of an obstructed airway results in a rapid decrease in intrathoracic pressure. Repeated futile inhalation efforts associated with obstructive sleep apnea may trigger a series of secondary reactions, including mechanical, hemodynamic, chemical, neurological, and inflammatory reactions .

  Obstructive sleep apnea may be terminated by awakening from sleep a few seconds after the apnea peak, allowing breathing to resume. Concurrent with wakefulness from sleep, sympathetic activity, blood pressure, and heart rate surges may occur. The negative effects of obstructive apnea are not limited to sleep. Arousal states such as sympathetic nerve activity and systemic blood pressure are enhanced. A decrease in vagal tone may occur, resulting in a decrease in total heart rate variability during periods of wakefulness.

  Central sleep apnea is generally caused by a failure of respiratory control signals from the brain. Central sleep apnea is a component of Chain Stokes Respiration (CSR), a breathing pattern that is observed primarily in patients with chronic heart failure (CHF). Chain Stokes breathing is a form of periodic breathing, in which central apnea and hypopnea alternate, and periods of hyperventilation produce a repeating pattern of tidal volume changes. In some CHF patients, obstructive sleep apnea and central sleep apnea can coexist. In these patients, there may be a gradual change from predominantly obstructive apnea at the beginning of the night to predominantly central apnea at the end of the night.

Central apnea observed in patients suffering from chronic heart failure may involve several mechanisms. According to one mechanism, increased carbon dioxide sensitivity in CHF patients triggers hyperventilation that initiates sleep apnea episodes. Respiration is regulated by a negative feedback system that maintains the arterial pressure of carbon dioxide (PaCO ₂ ) within limits. The change in PaCO ₂ leads to a change in ventilation, and the greater the sensitivity to carbon dioxide, the greater the ventilation response.

In patients with cardiopulmonary disorders, increased carbon dioxide sensitivity minimizes PaCO ₂ swaying, thereby reducing the long-term consequences of percarbonate (excess carbon dioxide in the blood). Can be protected. This protection mechanism may be advantageous while the patient is awake, but increased sensitivity to carbon dioxide may disturb breathing during sleep.

During sleep, ventilation decreases and PaCO ₂ levels increase. When PaCO ₂ levels decrease below a level called apnea threshold, ventilation stops, central sleep apnea occurs, and PaCO ₂ rises to the previous level.

In patients with increased sensitivity to carbon dioxide, when PaCO ₂ rises, the negative feedback system that controls respiration initiates a large ventilatory response. The resulting hyperventilation results in central sleep apnea by driving PaCO ₂ levels below the apnea threshold. As a result of apnea, PaCO ₂ levels rise again, leading to increased ventilation. In this manner, the hyperventilation and central apnea cycles may recur throughout sleep.

  The position of a sleeping CHF patient may be related to triggering apnea. When a CHF patient is lying down and resting, prone position can cause central fluid accumulation and pulmonary congestion, and the patient may be reventively hyperventilated. Hyperventilation may initiate the hyperventilation-apnea periodic pattern described above.

In central sleep apnea, awakening is not necessarily required to resume breathing at the end of the apnea event. In central apnea, wakefulness follows the onset of breathing, stimulates hyperventilation recurrently, and reduces the occurrence of fluctuations in ventilation by reducing PaCO ₂ below the apnea threshold. May make it easier. Once triggered, hyperventilation and apnea alternate by a combination of increased respiratory drive, pulmonary congestion, arousal, and apnea-induced hypoxia, resulting in PaCO ₂ fluctuations above and below the apnea threshold The pattern is maintained. Changes in the patient's consciousness, especially due to repeated arousals, can further destabilize breathing.

With the transition from wakefulness to NREM sleep, the wakefulness nerve drive to breathe decreases and the threshold of the ventilatory response to carbon dioxide increases. Thus, if the awake patient's PaCO ₂ level is below this higher sleep threshold, the transition to NREM sleep may be a central apnea with a transient decrease in respiratory drive. During apnea, PaCO ₂ rises and breathing begins when a new higher threshold level is reached. When sleep is firmly established, regular breathing is resumed. However, when arousal occurs, the increased PaCO ₂ level associated with sleep becomes too high for the state of arousal and stimulates hyperventilation. Thus, while arousal terminates obstructive sleep apnea, arousal triggers respiratory variability associated with central apnea, particularly Chain Stokes breathing.

In addition to acute responses to sleep breathing disorders, such as the acute responses described above, sleep breathing disorders can include a number of secondary or including, for example, chronic decreases in heart rate variability (HRV) and blood pressure changes. It is also associated with chronic reactions. Patients with central sleep apnea may have higher urinary and circulating norepinephrine concentrations and lower PaCO ₂ during both sleep and wakefulness.

  Acute responses to respiratory disorders are associated with physiological conditions that are modulated during an ongoing respiratory disorder event. Sensing a condition modulated by an acute response to a disordered breathing can be used to detect a disordered breathing event simultaneously with the occurrence of the disordered breathing event. The chronic response to a disordered breathing may be modulated by a collection of disordered breathing events that occur over a period of time. A chronic response to a disordered breathing can be used to determine whether a disordered breathing phenomenon has occurred.

  Both acute and chronic responses to breathing disorders can be used to assess the effectiveness and impact of breathing disorder treatments. In one implementation, a first subset of patient conditions can be used to detect respiratory disturbances, including a phenomenon that is currently occurring and / or a set of phenomena that are occurring over a period of time. A second subset of patient conditions (which may overlap with the subset used to detect disordered breathing) can be used to assess treatment for disordered breathing. For example, according to one embodiment, the effectiveness of a treatment can be assessed and the treatment can be adapted to enhance efficacy based on the assessment. In another embodiment, treatment can be assessed to determine the impact of the treatment on the patient. The treatment can be adapted to reduce the treatment impact of the treatment on the patient based on the assessment. In still further embodiments, the treatment can be adapted to enhance the effectiveness of the treatment and reduce the impact of the treatment on the patient. Other constraints may be utilized for treatment adaptation, including, for example, maintaining the useful life of the device and / or avoiding interactions between the treatment of respiratory disorders and other treatments delivered to the patient. it can.

  The condition used to assess the effectiveness of treatment may or may not be the same as the condition used to assess the impact of treatment on the patient. Table 3 shows a representative set of conditions that can be used for therapeutic assessment.

  For example, patient conditions that may be used in conjunction with treatment of respiratory disorders including respiratory disorder detection and / or treatment assessment may not be limited to the representative set described in Tables 1-3 or those described herein. I understand. Further, while an exemplary sensing method for detecting the patient condition described above is shown, it will be appreciated that the patient condition may be detected using a wide variety of techniques. The present invention is not limited to the specific conditions or specific sensing techniques described herein in connection with the exemplary embodiments.

  According to an embodiment of the present invention, a disordered breathing is detected and a therapy for treating the disordered breathing is adapted based on the detected disordered breathing. A methodology for adapting treatment of breathing disorders is illustrated by the flowchart of FIG. 5B. One or more conditions affecting the patient are sensed 530 and a disordered breathing is detected 540 based on the sensed conditions. The therapy is adapted 550 to treat the detected respiratory disorder. The system delivers 560 an adapted therapy.

In one embodiment, the disordered breath predictor / detector 258 (FIG. 2) can detect the onset of disordered breathing by monitoring the respiratory waveform output of the transthoracic impedance sensor. A hypopnea event is declared when the patient's tidal volume (TV) of the patient's breath (indicated by the transthoracic impedance signal) falls below the low breath threshold. For example, a hypopnea event may be declared if the patient's tidal volume falls below about 50% of a recent average tidal volume or other baseline tidal volume value. If the patient's tidal volume further falls to an apnea threshold (eg, about 10% of the recent average tidal volume or other baseline value), an apnea event is declared.
In another embodiment, the detection of a disordered breathing includes defining and reviewing the number of respiratory cycle intervals. FIG. 6 shows a breathing interval used for detection of breathing disorders according to an embodiment of the present invention. The respiratory cycle is divided into an inspiration period corresponding to the patient's inspiration, an exhalation period corresponding to the patient's exhalation, and a non-breathing period that occurs between inspiration and exhalation. The breath interval is determined using inspiration 610 and exhalation 620 thresholds. The inspiration threshold 610 is determined by the transthoracic impedance signal that marks the beginning of the inspiration period 630 and occurs at or above the inspiration threshold 610. The inspiration period 630 ends at 640 when the transthoracic impedance signal is highest. The highest transthoracic impedance signal 640 corresponds to both the end of the inspiration interval 630 and the beginning of the expiration interval 650. The exhalation interval 650 continues until the transthoracic impedance falls below the exhalation threshold 620. The non-breathing interval 660 begins at the end of the expiration period 650 and continues until the beginning of the next inspiration period 670.

  FIG. 7 illustrates the detection of sleep apnea and severe sleep apnea according to an embodiment of the present invention. The patient's respiratory signal is monitored and the respiratory cycle is defined by the inspiration 730, exhalation 750, and non-respiration 760 intervals described with reference to FIG. A sleep apnea condition is detected when the non-breathing period 760 exceeds a first predetermined interval 790 (represented by a sleep apnea interval). A severe sleep apnea condition is detected when the non-breathing period 760 exceeds a second predetermined interval 795 (expressed as a severe sleep apnea interval). For example, sleep apnea can be detected when the non-breath interval exceeds about 10 seconds, and severe sleep apnea can be detected when the non-breath interval exceeds about 20 seconds.

  Hypopnea is a state of respiratory impairment characterized by abnormally shallow breathing. 8A-B are graphs of tidal volume derived from transthoracic impedance measurements. The graph compares the tidal volume of a normal respiratory cycle with the tidal volume of hypopnea episodes. FIG. 8A shows the tidal volume and rate of normal breathing. As shown in FIG. 8B, hypopnea includes an unusually shallow period of breathing.

  According to embodiments of the present invention, hypopnea is detected by comparing a patient's respiratory tidal volume to a hypopnea tidal volume threshold. The tidal volume for each respiratory cycle is derived from transthoracic impedance measurements acquired in the manner described above. The hypopnea tidal volume threshold can be determined using clinical results showing typical tidal volume and duration of hypopnea events. In one configuration, hypopnea is detected when the average of a patient's tidal volume taken over a selected time interval falls below a hypopnea tidal volume threshold. Furthermore, various combinations of hypopnea cycle, breath interval, and non-breath interval can be used to detect hypopnea, where the non-breath interval is determined as described above.

  FIG. 9 is a flowchart illustrating a method of apnea and / or hypopnea detection according to an embodiment of the present invention. Various parameters are established 901 and patients for the development of respiratory disorders, including, for example, inspiration and expiration thresholds, sleep apnea intervals, severe sleep apnea intervals, and hypopnea tidal volume thresholds Respiration is analyzed.

  The patient's transthoracic impedance is measured 905 as described in more detail above. If the transthoracic impedance exceeds 910, the beginning of the inspiration interval is detected 915. If the transthoracic impedance is still 910 below the inspiration threshold, the impedance signal is periodically checked 905 until inspiration 915 occurs.

  During the inspiration interval, the patient's transthoracic impedance is monitored until 920, when the maximum value of transthoracic impedance is detected. Detection of the maximum value indicates the end of the inspiration period and the beginning of the expiration period 935.

  The exhalation interval is characterized by a decrease in transthoracic impedance. If the transthoracic impedance falls 940 below the expiration threshold, a non-breathing interval is detected 955.

  If the transthoracic impedance does not exceed the inspiration threshold within the first predetermined interval 965 (represented by the sleep apnea interval) 960, a sleep apnea condition is detected 970. Severe sleep apnea is detected 980 if the non-breathing period extends beyond a second predetermined interval 975 (represented by a severe sleep apnea interval).

  If the transthoracic impedance exceeds 960, the tidal volume due to peak-to-peak transthoracic impedance is calculated 985 along with the moving average of the past tidal volume. The peak-to-peak transthoracic impedance indicates a value proportional to the tidal volume of the respiratory cycle. This value is compared 990 to the hypopnea tidal volume threshold. A hypopnea cycle is detected 995 if the peak-to-peak transthoracic impedance is 990 consistent with a hypopnea tidal volume threshold for a predetermined time 992.

  Additional sensors, such as behavioral sensors and / or posture sensors, can be used to confirm or verify detection of sleep apnea or hypopnea episodes. Additional sensors can be used to prevent false detection or lack of detection of sleep apnea / hypopnea due to posture and / or behavior related artifacts.

Another embodiment of the invention may classify a respiratory pattern as the onset of a disordered breathing based on the respiratory interval and / or tidal volume of one or more respiratory cycles within the respiratory pattern. include. According to this embodiment, the duration and tidal volume associated with the breathing pattern are compared to the duration and tidal volume threshold. Based on the comparison, a breathing pattern is detected as the onset of a respiratory disorder.
In accordance with the principles of the present invention, a breathing interval is established for each breathing cycle. The breath interval represents the time interval between successive breaths, as shown in FIG. The breath interval 1030 can be defined in various ways (eg, as the time interval between successive maxima 1010, 1020 of the impedance signal waveform).

  Detection of a respiratory disorder includes determination of a duration threshold and a tidal volume threshold according to an embodiment of the invention. An apnea event is detected when the breath interval exceeds the duration threshold. The graph of FIG. 10 shows sleep apnea detection according to this embodiment. Apnea represents a period of non-breathing. A respiratory interval 1030 that exceeds the duration threshold 1040 includes an apnea episode.

  Hypopnea can be detected using a duration threshold and a tidal volume threshold. A hypopnea event represents a period of shallow breathing. Each respiratory cycle in a hypopnea event is characterized by a tidal volume that is less than the tidal volume threshold. In addition, hypopnea events include periods of shallow breathing that are greater than the duration threshold.

  FIG. 11 shows a hypopnea detection method according to an embodiment of the present invention. Shallow breathing is detected when the tidal volume of one or more breaths is below the tidal volume threshold 1110. If the shallow breath continues for an interval greater than the duration threshold 1120, the breathing pattern represented by the continuation of the shallow breathing cycle is classified as a hypopnea event.

  12 and 13 are charts showing the classification of each respiratory disorder phenomenon and series of respiratory disorder phenomena that recur regularly. As shown in FIG. 12, each respiratory disorder phenomenon can be classified into apnea, hypopnea, tachypnea, and other respiratory disorder phenomena. Apnea events are characterized by a lack of breathing. Reduced breathing intervals are classified as hypopnea events. Tachypnea events include rapid breath intervals characterized by elevated breathing rates.

  As shown in FIG. 12, apnea and hypopnea events can be further subdivided as either central events related to central nervous system dysfunction or obstructive events caused by upper airway obstruction. Tachypnea events can be further classified as hyperventilation events represented by hyperventilation (ie, rapid deep breathing). Alternatively, the tachypnea phenomenon can generally be classified as rapid breathing with a long duration.

  FIG. 13 shows a classification of combinations of respiratory disturbance events that recur regularly. Periodic breathing can be classified as obstructive, central or mixed. Obstructive periodic breathing is characterized by a periodic breathing pattern with obstructive apnea or hypopnea events in each cycle. Central periodic breathing includes a periodic breathing pattern that includes a central apnea or hypopnea event in each cycle. The periodic breathing shown in FIG. 14F may be of mixed origin. Periodic breathing of mixed origin is characterized by a periodic breathing pattern with a mixture of obstructive and central apnea events in each cycle. The Chain Stokes breath shown in FIG. 14G is a specific type of periodic breathing that includes repeated gradual increases and decreases in tidal volume and has central apnea and hyperpnea events in each cycle. There may be other signs of periodic breathing. Various forms of disordered breathing can be determined based on a characteristic breathing pattern associated with a particular type of disordered breathing.

  As shown in FIGS. 14A-E, the respiratory pattern detected as the onset of respiratory failure is 1410 (FIG. 14A) for apnea respiratory cycle only, 1450 for hypopnea respiratory cycle (FIG. 14D), or breathing for hypopnea Cycle and apnea breathing cycle mixes 1420 (FIG. 14B), 1430 (FIG. 14C), 1460 (FIG. 14E) may be included. The disordered breathing phenomenon 1420 begins with an apnea breathing cycle and ends with one or more hypopnea cycles. In another pattern, the disordered breathing event 1430 begins with a hypopnea cycle and ends with an apnea cycle. In yet another pattern, the disordered breathing event 1460 begins and ends with a hypopnea cycle and has an apnea cycle between the hypopnea cycles.

  FIG. 15 is a flow graph of a method for detecting a respiratory disorder according to an embodiment of the present invention. As described above, the method shown in FIG. 15 functions by classifying the breathing pattern using the breathing interval, the tidal volume, and the duration threshold value in combination. In this example, a duration threshold and a tidal volume threshold are established to determine both apnea and hypopnea breath intervals. An apnea episode is detected when the breath interval exceeds the duration threshold. A hypopnea episode is detected when the tidal volume of continuous breathing is still below the tidal volume threshold for a period exceeding the duration threshold. Mixed apnea / hypopnea episodes may occur. In these cases, the period of disordered breathing is characterized by shallow breathing or non-breathing intervals. During the onset of mixed apnea / hypopnea, the tidal volume of each breath is still below the tidal volume threshold for periods exceeding the duration threshold.

  Transthoracic impedance is sensed and used to determine the patient's respiratory cycle. Each breath 1510 may be characterized by a breathing interval, a time interval between local maxima of two impedance signals, and a tidal volume (TV).

  If the breathing interval exceeds the duration threshold 1515, the breathing pattern matches the apnea event and the apnea trigger is turned on 1520. If the tidal volume of the breath interval exceeds the tidal volume threshold 1525, the breath pattern is characterized by two breath cycles of normal volume separated by a non-breath interval. This pattern represents a pure apnea disorder of breathing, and apnea is detected 1530. Since the last breath in the breath interval was normal, the apnea trigger is turned off 1532 indicating the end of the onset of breathing disorder. However, if the tidal volume during the breath interval does not exceed the tidal volume threshold 1525, the period of impaired breathing continues and the next breath is checked 1510.

  If the breathing interval does not exceed the duration threshold 1515, the tidal volume of breathing is checked 1535. If the tidal volume does not exceed the tidal volume threshold 1535, the breathing pattern matches the hypopnea cycle and the hypopnea trigger is set on 1540. If the tidal volume exceeds the tidal volume threshold, breathing is normal.

  If the period of respiratory disturbance is ongoing, detection of normal breathing indicates the end of the respiratory disorder. If a breathing disorder was previously detected 1545, and if the duration of the breathing disorder phenomenon is 1550 state that does not exceed the duration threshold and the current breathing is normal, no breathing disorder phenomenon is detected 1555 . If a breathing disorder is detected in 1545, and if the duration of the breathing phenomenon exceeds the duration threshold and the current breathing is normal, the breathing disorder trigger is turned off 1560. In this situation, the duration of the onset of respiratory disorder was of sufficient duration to be classified as the onset of respiratory disorder. If an apnea event was previously triggered 1565, an apnea event is declared 1570. If hypopnea was previously triggered 1565, a hypopnea event is declared 1575.

Distinguishing Central / Occluded Breathing Disorders In some embodiments of the present invention, the respiratory disorder predictor / detector 258 (FIG. 2) may include circuitry for classifying the origin of the disordered breathing event. The origin of the disordered breathing phenomenon can be classified as a central type, an obstructive type, or a combination of central and obstructive types. Classification of respiratory disorder origin can be used to more accurately diagnose respiratory rhythm disorders. Further, the therapy delivered to treat central respiratory disorder may be different from the therapy delivered to treat obstructive respiratory disorder. The classification of the disordered breathing origin allows the therapy controller 265 (FIG. 2) to provide an appropriate therapy corresponding to the disordered breathing origin.

  According to various implementations, disordered breathing can be classified based on patient behavior associated with breathing effort during the disordered breathing. For example, central apnea can be identified by insufficient breathing for at least about 10 seconds due to insufficient respiratory effort. Obstructive apnea can be identified by insufficient respiratory inspiration for at least about 10 seconds with respiratory effort. Respiratory effort can be detected by sensing patient behavior associated with respiratory effort during a disordered breathing event. The sensed behavior may include the patient's chest, abdomen, diaphragm behavior, and / or other behaviors associated with respiratory effort.

  The onset of respiratory disorder can be classified as central respiratory disorder, obstructive respiratory disorder, or a combination of central and obstructive types. Various forms of respiratory impairment that can be classified for origin (central, obstructive, or mixed origin) are, for example, apnea, hypopnea, hyperpnea, tachypnea, periodic breathing, Chain Stokes breathing (CSR) and / or other forms of respiratory disorder may be included.

  FIG. 16A is a flowchart of a method for classifying a disordered breathing phenomenon according to an embodiment of the present invention. The method includes detecting 1601 a disordered breathing event and sensing 1602 behavior associated with breathing effort during the disordered breathing event. The disordered breathing can be detected based on the patient's breathing pattern or by other methods. Behavior associated with respiratory effort may include chest wall behavior, abdominal behavior, and / or other behavior associated with respiratory effort. The disordered breathing event can be classified 1603 as central, obstructive, or a mixture of central and obstructive types based on patient movements associated with respiratory effort during the disordered breathing event.

  In one scenario, the disordered breathing phenomenon may include both central and obstructive types. A disordered breathing event is a mixture of central and obstructive when central disordered breathing is classified as part of the disordered breathing phenomenon and obstructive disordered breathing is classified as another part of the disordered breathing phenomenon. Can be classified as a respiratory disorder phenomenon.

  FIG. 16B is a block diagram of a respiratory disorder classification circuit 1600 that can be implemented with a respiratory disorder predictor / detector 258 (FIG. 2) for classifying respiratory disorders according to an embodiment of the present invention. The disordered breathing classification circuit 1600 shown in FIG. 16B includes a disordered breathing classification processor 1651 that receives signals from the disordered breathing phenomenon detector 1637 and the behavior sensor 1661.

  The disordered breathing event detector 1637 received a signal from at least one sensor 1635 (eg, a respiratory sensor) for detecting a physiological signal indicative of the disordered breathing. The disordered breathing event processor 1637 can analyze the sensor signal and determine based on the analysis that a disordered breathing event is in progress.

  In one implementation, sensor 1635 generates a signal modulated by patient respiration. Such a signal may be generated, for example, by a transthoracic impedance sensor, an anemometer, or other sensing method. A disordered breathing event can be detected based on the patient's breathing interval and / or tidal volume, which is more fully described herein.

  The behavior sensor 1661 may be configured to sense other patient movements indicative of chest wall behavior, abdominal behavior, and / or respiratory effort. The behavior sensor 1661 generates a signal indicative of respiratory effort that is communicated to the disordered breathing classification processor 1651.

  The sensors 1635, 1661 may include any number of patient-internal and / or patient-external sensors coupled to other components of the disordered breathing classification circuit 1600 via leads or wirelessly. In various embodiments, the signal indicative of patient respiration may be transmitted using an implantable or patient-external transthoracic impedance sensor, blood oxygen sensor, microphone, flow meter, or other patient interior and / or patient exterior. It can be obtained by the perception method.

  Sensing the chest, abdomen, or other behavior associated with breathing effort can be accomplished using sensing devices inside or outside the patient. In one example, patient behavior associated with respiratory effort can be sensed using an implanted or external patient accelerometer. The accelerometer can be incorporated as a component of an implanted medical device, such as an implantable cardiac rhythm management system that has the functionality to deliver cardiac electrical therapy for respiratory disorders.

  In another example, behavior associated with respiratory effort can be detected based on changes in an electromyogram (EMG) sensor signal. The EMG sensor may be placed internally or externally to detect electrical activity in the intercostal, pectoral and / or diaphragm muscles of the patient exhibiting behavior. In another example, behavior associated with respiratory effort can be detected using a transthoracic impedance sensor. The patient's transthoracic impedance is modulated as the chest wall and / or abdomen move during inspiration efforts. Transthoracic impedance can be sensed using intracardiac electrodes, subcutaneous electrodes, or electrodes external to the patient, located at an appropriate location in, on, or around the patient's thorax .

  The disordered breathing phenomenon can be classified as a central, obstructive or mixed type based on the respiratory effort of the patient during the onset of the disordered breathing. The disordered breathing classification processor 1651 can discriminate between a central disordered breathing phenomenon and an obstructive disordered breathing phenomenon using signals received from the behavior sensor 1661 and the disordered breathing detector 1641. If the patient behavior associated with breathing effort during the disordered breathing is sufficiently large, the disordered breathing classification processor 1651 may determine that the disordered breathing event is obstructive in origin. If the respiratory effort behavior was insufficient during the disordered breathing event, the disordered breathing classification processor 1651 can classify the disordered breathing event as having a central origin. If breathing effort behavior is sufficient during some of the onset of respiratory disturbances but not in other parts, the respiratory disorder classification processor 1651 develops as a mixture of central and obstructive types. Can be classified.

  In one configuration, the disordered breathing classification circuit 1600 may be completely external to the patient. In another configuration, some functions of the disordered breathing classification circuit may be implemented in an implantable device and other functions may be implemented as a device external to the patient. The components of the implantable and patient-external respiratory classification system can be coupled via leads or wireless communication links (such as Bluetooth or proprietary wireless communication links).

  In another configuration, the disordered breathing classification circuit 1600 is fully implantable. The disordered breathing classification circuit 1600 can be incorporated as a component of, for example, a cardiac device such as a pacemaker, defibrillator, cardiac resynchronizer, implantable heart monitor, or other implantable medical device.

  Classification of disordered breathing events according to the process of the present invention includes assessment of chest wall behavior, or other behavior associated with breathing effort. FIGS. 16C and 16D show graphs of accelerometer signals representing chest wall behavior for central and obstructive respiratory disorders, respectively. As shown in FIG. 16C, apnea is detected when transthoracic impedance signal 1671 is still below inspiration threshold 1672 for a period of time longer than apnea interval 1673 (eg, 10 seconds). . In this example, the apnea event is a central apnea event, and the signal 1674 from the accelerometer that senses the behavior of the patient's chest wall also falls below the behavior threshold 1675 during the non-breathing period. The lack of chest wall behavior indicates that the patient's respiratory relaxation has not been triggered by the central nervous system, which exhibits a central respiratory disorder phenomenon.

  FIG. 16D shows the accelerometer signal and transthoracic impedance signal for an obstructive apnea event. Apnea is detected when the transthoracic impedance signal 1676 is still below the inspiration threshold 1677 for a period of time greater than the apnea interval 1673. In this example, the apnea event is an obstructive apnea event, and the signal 1678 from the accelerometer that senses the behavior of the patient's chest wall rises above the chest wall behavior threshold 1679 during the non-breathing period. The chest wall behavior indicates that the patient's respiratory relaxation is triggered by the central nervous system, which exhibits an obstructive respiratory disorder phenomenon.

  FIG. 16E is a flowchart of a method for classifying a disordered breathing event as a central, obstructive or mixed phenomenon according to an embodiment of the present invention. One or more conditions associated with respiratory disorder are sensed 1680. For example, one or more of the conditions listed in Table 1 can be sensed to detect the occurrence of a disordered breathing phenomenon. During the disordered breathing event, behavior of the patient's chest wall is sensed 1681.

  When a respiratory disorder is detected 1682, the chest wall behavior signal is analyzed 1683 to discriminate obstructive / central origin. A parameter (eg, average amplitude or frequency) of the signal generated by the behavior sensor can be compared to a threshold value. If the chest wall behavior signal is not greater than the threshold 1684, the respiratory disorder is classified 1686 as a central respiratory disorder. If the chest wall behavior signal is greater than or equal to 1684 1684 and the chest wall behavior was associated with respiratory effort 1685, the respiratory disorder is classified 1687 as obstructive respiratory disorder. For example, if the chest wall behavior from the accelerometer is synchronized with reduced transthoracic impedance during the onset of respiratory disturbance, the simultaneous occurrence of respiratory disturbance and chest wall behavior is obstructive in origin Indicates respiratory disturbance.

  If the disorder of breathing continues 1688, chest wall behavior continues to be sensed 1683. The second or subsequent portion of the disordered breathing event may have a classification different from the initial classification based on the presence or absence of behavior associated with respiratory effort.

  The flowchart of FIG. 16F illustrates an optional process that can be implemented following FIG. 16E and following classification of the disordered breathing phenomenon. Breathing disorder information may optionally be stored, transmitted, displayed, and / or printed 1690. For example, disordered breathing information may be stored for weeks or months to enhance the diagnosis of disordered breathing or other conditions, or to analyze trends in disordered breathing and / or effectiveness of treatment.

  Additionally or alternatively, classification of the origin of the disordered breathing event may be performed in conjunction with the administration of therapy 1691 to treat the disordered breathing. Therapies for treating breathing disorders may include cardiac electrotherapy, among other therapies. In one scenario, the first therapeutic measure can be used to treat a respiratory disorder that is central in origin. The second therapeutic measure can be used to treat respiratory disorders that are obstructive in origin. The first and / or second treatment may begin after the origin of the disordered breathing is determined.

  Further, based on the classification of the disordered breathing, treatments other than the disordered breathing treatment may be initiated, modified, or terminated 1692. For example, as noted above, respiratory disorders in the form of Chain Stokes breathing are associated with congestive heart failure and may be used to monitor the progression of CHF. As mentioned above, Chain Stokes breathing is marked by a cyclic pattern of repetitive breathing that is interrupted by a period of central apnea. The characteristics (eg, origin, duration, and severity) of the disordered breathing experienced by the patient initiates or coordinates the therapy delivered to the patient, such as cardiac pacing therapy and / or cardiac resynchronization therapy. Can be used to

  In the various embodiments of the present invention described herein, discrimination between central and obstructive respiratory disturbances is due to the sensing of chest wall behavior performed using an implanted behavioral sensor (eg, an accelerometer). Is based. In other embodiments, the patient-external behavior detector, such as a patient-external accelerometer, patient-external breathing band, transthoracic impedance sensor, or mercury switch, alone or other implanted or patient-external May be used in combination with other respiratory sensors and detection algorithms for classification of central / obstructive respiratory disorders.

  In one example, a motion sensor such as an accelerometer is mounted inside an implantable CRM device for sensing chest wall behavior indicative of obstructive apnea. The output of the motion sensor can be used in combination with other sensors (such as transthoracic impedance sensors) for the classification of obstructive apnea. The multi-sensor pulse generator is a unique location product for accurate long-term monitoring and prediction of disease progression in patients with respiratory impairment. Discrimination between types of apnea events allows more accurate diagnosis, monitoring, and / or treatment of abnormal breathing patterns associated with CHF or sleep breathing disorders. Monitoring with discrimination between apnea types may allow improved treatment to offset the effects of abnormal breathing patterns.

Respiratory Disorder Prediction The flowchart of FIG. 16G illustrates a method for triggering treatment of a respiratory disorder based on the prediction of respiratory disorder according to various embodiments of the invention. The method includes sensing 1630 a predictive one or more conditions of a disordered breathing and predicting a disordered breathing 1640 based on the sensed conditions. The disordered breathing can be predicted, for example, by comparing the detected state with a criterion for predicting disordered breathing. A representative set of conditions that can be used to predict breathing problems is listed in Table 1. The representative set of conditions described in Table 1 is not limiting and conditions other than those described can be used to predict respiratory impairment. If a disordered breathing is anticipated, the therapy is adapted 1650 to treat the disordered breathing (eg, reduce the severity of the disordered breathing or prevent the disordered breathing from occurring). The adapted therapy is delivered 1660 to the patient. One or more of sensing a condition affecting the patient, predicting a disordered breathing based on the sensed condition, and delivering a therapy to treat the disordered disorder is at least Partially portable.

  FIG. 17 shows a block diagram of a respiratory disorder treatment system configured in accordance with an embodiment of the present invention and including respiratory disorder predictive functionality. The system may use a patient internal sensor 1710 implanted within the patient's body to detect a physiological condition. For example, the system uses an intracardiac electrocardiogram (EGM) signal detector and transthoracic impedance sensor that are part of an implanted cardiac rhythm management system, such as a cardiac pacemaker or defibrillator, Heart rate variability, respiratory cycle, tidal volume, and / or other physiological signals can be determined.

  The system can use a patient-external sensor 1720 to detect a physiological or non-physiological condition. In one scenario, whether a patient is snoring may be useful in predicting respiratory disturbances. Snoring can be detected, for example, using an external microphone or an implanted accelerometer. In other situations, temperature and humidity may be factors that exacerbate the patient's respiratory impairment. Signals from temperature and humidity sensors can be used to help predict breathing problems.

  Additionally, the system can use the information input 1730 by allowing the patient to inform the respiratory disorder prediction system of one or more patient conditions. In various embodiments, a patient's medical history, self-explained drug use, alcohol or tobacco use, daytime sleepiness, or sleep quality perception over one or more past sleep periods may cause respiratory impairment. It may be useful to connect with the predictions.

  Signals from one or more of patient-internal sensor 1710, patient-external sensor 1720, and patient input device 1730 can be coupled to respiratory disorder prediction engine 1740 for predictive evaluation. In one implementation, the prediction engine 1740 may compare the patient condition to one or more sets of criteria for disordered breathing and predict the disordered breathing based on the comparison. Prediction engine 1740 is coupled to treatment controller 1750. If a disordered breathing is predicted, the therapy controller 1750 delivers the appropriate therapy to the patient to mitigate the disordered breathing.

  In one example, patient status can be sensed and processed using implantable sensor 1710, and predictive analysis and therapy delivery can be performed by patient-external respiratory disorder prediction engine 1740 and patient-external therapy controller 1750. Some or all of the implantable sensors 1710 may have remote communication capabilities such as wireless proprietary or wireless Bluetooth communication links. In this implementation, the wireless communication link couples the implantable sensor or group of sensors 1710 to a breathing disorder prediction engine 1740 external to the patient. An electrical signal representative of the patient condition is generated by the implantable sensor 1710 and transmitted to the respiratory disorder prediction engine 1740 external to the patient.

  In another example, an implantable therapy device may incorporate a disordered breath prediction engine 1740 and one or more patient-external sensors 1720. A signal representative of the patient condition may be transmitted over the wireless communication link from a sensor external to the patient to the implanted prediction engine 1740.

  In a further example, the prediction engine may be a patient-external device that is wirelessly coupled to the therapy controller. Various combinations of wireless or wired connections between patient-internal sensor 1710, patient-external sensor 1720, patient input device 1730, prediction engine 1740, and treatment controller 1750 are possible.

  The above examples are just a few of the many possible configurations that can be used to provide a disordered breathing treatment based on the prediction of disordered breathing according to various embodiments of the present invention. It will be appreciated that the components and functionality shown and described herein can be implemented in hardware, software, or a combination of hardware and software. The components and functionality shown as separate or individual blocks / elements in the figures can be realized in combination with other components and functionality, and such configurations in individual or integral form It will further be appreciated that the depiction of elements and functionality is for purposes of clarity of description and not for purposes of limitation.

  A subset of detected patient conditions, such as the representative conditions listed in Table 1, may represent conditions that cause the patient to suffer from a disordered breathing. The etiological state may be statistically associated with the onset of a disordered breathing during the next few hours following detection of a condition leading to a predicted disordered breathing. Another subset of conditions may represent the precursor condition used to predict the impending onset of a respiratory disorder that may occur within a time window measured over a period of minutes or seconds. Detection of the patient condition associated with the disordered breathing and prediction of the disordered breathing based on the etiology or precursor condition is done in real time.

  A subset of patient conditions can be used to verify or otherwise inform the prediction of disordered breathing. In one example, information about sleep onset or sleep stage or condition (eg, REM or non-REM sleep) can be used in predicting sleep disordered breathing. A subset of the conditions listed in Table 1 can be used to detect whether a patient is asleep and to track various stages of sleep. Another subset of conditions can be used to detect the development of respiratory disorders and to classify the development of respiratory disorders. Table 4 below shows an example of how some of the conditions listed in Table 1 can be used in predicting respiratory impairment.

  FIG. 18 conceptually illustrates how patient conditions such as those described in Tables 1 and / or 4 can be used in predicting respiratory impairment 1810 according to embodiments of the present invention. In one embodiment, the system tracks one or more of the conditions listed in Table 1, Table 4, or both to predict respiratory impairment. For example, over a period of time (eg, at least one 16-hour window that precedes and includes the patient's historical sleep time), the system tracks one or more states. To determine the existence and / or level of each particular state.

  In one implementation, the system tracks a state 1820 that is determined to have the patient suffering from a disorder of breathing disorder. The etiological state represents a patient condition that is statistically associated with the onset of respiratory impairment. The presence of one or more etiological conditions indicates that a disordered breathing may occur within the next time period, such as the eight hour period following prediction of disordered breathing, or during the current sleep period There is a case. For example, the etiological status may be an indication of air pollution in the patient's environment downloaded from an air quality website, recent tobacco use reported by the patient, or a patient's lung congestion detected by an implanted transthoracic impedance sensor As well as other etiological conditions detected internally and / or externally to the patient.

  Additionally or alternatively, the system can use a previous onset of breathing disorder to determine that the patient suffers from a further onset of breathing disorder within a particular time period, such as during a sleep period. For example, a previous episode of respiratory disorder during a first interval within a sleep period may indicate that additional episodes may occur at a second and subsequent interval within the same sleep period. In one example, the occurrence of a first type of disordered breathing can be used to predict a second type of disordered breathing. In another example, the disorder of breathing disorder can be used to predict future onset of breathing disorder.

  The disordered breathing prediction engine can use the type, duration, frequency, and / or severity of previous disordered breathing to inform a predictive analysis of disordered breathing. Quantification of the severity, frequency, and duration of disordered breathing should be accomplished using any of a number of disordered breathing measures, including, for example, the percent time of disordered breathing and the apnea / hypopnea index. Can do.

  A further example of a condition that causes the patient to suffer from hypopnea or apnea is body posture. The supine posture is more likely to cause upper airway obstruction and can be used to predict the onset of obstructive hypopnea and apnea. Sensing of posture and / or torso orientation can be accomplished using, for example, an implantable multi-axis accelerometer.

  As mentioned above, sleep disordered breathing is a prevalent form of disordered breathing. Thus, a patient may be more likely to experience the development of a disordered breathing when the patient is sleeping and sleeping. Thus, access to the bed can be used as an etiological condition for breathing problems. The disordered breathing treatment system can use a bed proximity sensor to detect that the patient is in bed. Bed approach can be detected by placing a beacon transmitter on the patient's bed. For example, a receiver circuit on or within a patient, incorporated in the patient's pacemaker, receives the beacon signal and determines that the patient is in bed.

  A condition 1820 that causes a patient to suffer from a disordered breathing is a condition that indicates that one or more of the disordered breathing events may occur during the next period of time, such as at night or over the course of another sleep period. is there. Based on the etiology state 1820, it can be predicted 1812 that the onset of a respiratory disorder occurs within a window of time that may include several hours (eg, 8 hours).

  A second set of conditions (represented here as precursor states 1830) can be used to predict the impending onset of respiratory impairment 1814. The precursor state 1830 indicates that the onset of a respiratory disorder is imminent and occurs within a window of time that may be measured, for example, in minutes or seconds. In one implementation, the precursor state 1830 can be used to predict the onset of a disordered breathing, for example, within the next 1800 seconds.

In one embodiment, the precursor state 1830 that indicates the impending onset of a disordered breathing may include, for example, a pre-apnea or pre-hypopnea condition. In one implementation, changes in blood gas concentrations such as CO ₂ may cause central apnea. Thus, a precursor state of pre-apnea in a particular patient is, for example, an imminent onset of apnea episodes when the patient's CO ₂ level, as measured by the patient's external CO ₂ sensor, falls below a selected level Can be detected.

  In another embodiment, a patient's heart rate variability can be significantly altered before, during, and after the onset of apnea. Heart rate variability can be used, for example, as a predictive state to predict an impending onset of breathing problems.

  In yet another embodiment, a pre-apnea or pre-hypopnea condition can be detected by analyzing a patient's breathing pattern. The respiratory cycle immediately prior to a disordered breathing event (eg, apnea or hypopnea event) may exhibit a characteristic pattern. For example, apnea events are preceded by a number of rapid and deep breathing periods of hyperventilation in many patients. Hyperventilation patterns can be detected by analyzing the patient's transthoracic impedance signal to determine respiratory rate and tidal volume.

  The onset of Chain Stokes breathing and some apnea / hypopneas may show a gradual / weak breath pattern. The gradual and gradual breathing pattern results in hyperventilation during the gradual phase and hypoventilation during the gradual phase. Hyperventilation (secondary to pulmonary congestion) drives the arterial partial pressure of carbon dioxide downward. A decrease in the arterial partial pressure of carbon dioxide below the apnea level may be a causative mechanism for central apnea. According to one embodiment of the present invention, the detection of an impending onset of respiratory disturbance can be achieved by detecting a series of increasing tidal volumes followed by a series of decreasing tidal volumes.

  In some patients, disordered breathing occurs at regular intervals, allowing the periodicity of the onset of disordered breathing to be used as a precursor. If the onset of the patient's respiratory disorder occurs at regular intervals, the next occurrence of the respiratory disorder can be predicted based on the time elapsed since the last onset was detected.

  Also, the occurrence of one form of respiratory disorder can be used to predict another form of respiratory disorder. For example, a patient is characterized by one or more episodes of obstructive sleep apnea during the first part of the night, and then by the development of central sleep apnea during the later part of the night. May experience. In another example, one or more episodes of hypopnea can be used to predict future apnea episodes.

  Snoring is an additional example of a pre-apnea or pre-hypopnea condition. In many cases, the patient's snoring or, more commonly, any abnormal airflow in the upper airway (which may be detectable via acoustic means) is more significant sleep such as hypopnea or apnea. Precedes the state of respiratory impairment. The precursor state 1830 can be analyzed individually or in combination with one or more etiological states 1820 to predict the impending onset of respiratory disorder.

  The conditions and associated prediction criteria used to predict breathing disorders can be highly patient specific. A condition that is a reliable predictor of disordered breathing in one patient may not be effective in another patient. Thus, the conditions used to predict breathing disorders and each predictive criteria are preferably based on patient specific data.

  A subset of patient conditions can be used to verify or confirm a prediction of respiratory impairment. For example, one or more verification states 1840 can be checked before or after a disordered breathing prediction is made to confirm the prediction. The verification state used to predict breathing disorders, as well as the physiological and contextual state, can be highly patient specific.

  In one example embodiment, the characteristic pattern of breathing is a reliable predictor of breathing disorder only in a particular patient when the patient is supine. If predictions are made while the patient is not in the supine position, normal fluctuations in the respiratory cycle in this particular patient may lead to false predictions of respiratory impairment. Therefore, the posture sensor signal is checked to verify that the patient is supine before a respiratory disorder is predicted. If the patient is in the supine position and the patient's respiratory cycle matches a criterion that indicates that there is a possibility of a disordered breathing, a disordered breathing is predicted.

  In another example, the patient is known to suffer from apnea throughout sleep. The patient's sleep apnea can be predicted using a number of contextual and physiological conditions. Sleep apnea may be predicted after assessing that the patient's body position and location are consistent with sleep. Before sleep apnea prediction is made, the system checks that the patient is lying on the bed and resting by checking signals from the implantable body position sensor and the bed proximity sensor.

  The block diagram of FIG. 19 conceptually illustrates the operation of the disordered breathing prediction engine 1900 according to various embodiments. One or more patient conditions are periodically detected and compared to a library of predictive criteria 1910. The prediction criteria library 1910 may incorporate one or more sets of prediction criteria 1911, 1912, 1913, 1914. Each of these sets of criteria may be compared to the detected patient condition. When the judgment criteria of the prediction judgment criteria set 1911, 1912, 1913, 1914 substantially coincide with the patient state, a preliminary respiratory disorder may be predicted.

  In various embodiments, the predictive criteria set 1911, 1912, 1913, 1914 represents one or more state thresholds associated with the onset of disordered breathing. In one example embodiment, the level of one or more detected conditions may be compared to the prediction criteria set 1911, 1912, 1913, 1914. If the level of one or more states approximately matches the threshold specified in the prediction criteria set 1911, 1912, 1913, 1914, a preliminary prediction of respiratory impairment may be made.

  Hereinafter, by way of example, it will be described that the state matches the prediction determination criterion when the state exceeds the threshold of the prediction determination criterion. However, it will be appreciated that different threshold conditions may be defined for different states. For example, a state may be defined as matching a prediction criterion if the state exceeds a prediction criterion threshold. Another state may be defined as matching the prediction reference threshold if the state falls below a threshold. In another example, a state may be defined as matching a prediction criterion when the state falls within a specified range of values. The patient condition may be compared to the predictive criteria, for example based on the timing of the condition, the rate of change, or the maximum or minimum value.

  In the example shown in FIG. 19, the prediction criterion N 1914 includes two contextual states, C1 and C2, and two physiological states, P1 and P2. In this particular example, the patient may experience respiratory disturbances during the night if states C1, C2, P1, and P2 exceed levels, Level1, Level2, Level3, and Level4, respectively. Accordingly, when the states C1, C2, and P1, P2 reach the level specified by the criterion N 1914, a preliminary prediction of respiratory disorder is performed. One or more additional validation criteria 1920 may be used to confirm a preliminary prediction of respiratory impairment.

  In another embodiment of the present invention, the relationship between detected states may be analyzed to predict respiratory impairment. In this embodiment, the disordered breathing prediction may be based on presence and relative values associated with two or more patient conditions. For example, if state A exists at the level of x prior to making a disordered breath prediction, state B must also exist at the level of f (x).

In yet another embodiment of the present invention, the estimated probability P (C _n ) that a disordered breathing will occur if a particular state level is detected is determined within a selected time interval following the detection of the particular state level. Can be expressed as a function of the ratio of the number of occurrences to the observed total number of occurrences of the state level. The probability P (C _n ) of occurrence of a disordered breathing is compared with a threshold probability level to predict a disordered breathing. Other methods of calculating the estimated probability are possible.

A disordered breathing prediction may be based on the convergence or divergence of multiple conditions that occur within the same time period. In this situation, the composite probability score can be calculated as a combination of individual probabilities. In one embodiment, the probabilities are combined by multiplying each of the state probabilities by a weighting factor and then adding the state probabilities. For example, the prediction of disordered breathing, the state of the four substantially simultaneous _{C 1, C 2, C 3} , and if it is based on C _4, the probability score PS _T in total can be calculated as follows. That is,
PS _T = A x P (C ₁ ) + B x P (C ₂ ) + C x P (C ₃ ) + D x P (C ₄ ) [1]
Where A, B, C, and D are scalar weighting factors that can be used to estimate the relative importance of each of states C ₁ , C ₂ , C ₃ , and C ₄ . If the probability score exceeds the threshold of the selected prediction criterion, a respiratory disorder is predicted.

While the above process describes a combination of estimated probabilities for each state by adding each of the estimated probabilities, other methods are possible. For example, the detected patient condition may have the opposite effect of predicting respiratory impairment. In this situation, the estimated probability P _n (C _n ) that no disordered breathing would occur if a particular state level was detected was that the disordered disorder did not occur within the selected time interval following the detection of the particular state level It can be expressed as a function of the ratio between the number of times and the observed total number of occurrences of the state level. Subtract this value from the total to get the probability score. Non-linear methods that combine estimated probabilities to arrive at composite probabilities are also possible.

  If the condition affecting the patient is consistent with a predicted disordered breathing, the prediction can be verified by comparing one or more verification states to verification criteria. If the verification state matches the verification criteria, a respiratory disorder is predicted.

  In the above embodiment, the disordered breathing prediction is based on a comparison of one or more patient conditions with a set of prediction criteria. The initial data from which the set of initial prediction criteria is based may be derived from past observations taken from the population data or data collected from a particular patient. Thus, the initial prediction criteria set may be modified as additional data is collected from the patient.

  In one embodiment, the estimated accuracy of the prediction criteria is updated for each prediction phenomenon. The estimated positive predictive value (PPV) of the prediction criterion set N can be expressed as: That is,

Here, TP (true positive) is the number of times that the prediction judgment criterion set has succeeded in predicting a respiratory disorder, and FP (false positive) is the number of times that the prediction judgment standard has erroneously predicted a respiratory disorder.

If the estimated accuracy of the prediction criterion set N, PPV _N , falls below a predetermined level, for example, 0.7 or less, the prediction criterion set N may be modified. In one embodiment, possible prediction criteria sets are formed, for example, by modifying one or more threshold levels of the states represented by the original prediction criteria set N. In one embodiment, each threshold in the original prediction criterion set N is modified with an incremental value to make the prediction criterion set more accurate.

  In another embodiment, how the state represented in the original prediction criterion set N is compared with the state that exists immediately before the occurrence of a respiratory disorder, and how a possible correction of the prediction criterion set is performed. Decide what to do. For example, if the level of a specific state just before the occurrence shows relatively large fluctuations just before the onset of respiratory disorder, but the level of other states is still constant, only the level that changes is predicted It may be modified within the reference set.

  Each time a possible set of predictive criteria is satisfied, no respiratory disorder is predicted, but the accuracy of the possible set of predictive criteria is updated using, for example, an equation similar to the form of Equation 2. The If the accuracy of the possible prediction criteria set reaches a selected level, e.g. 0.7, and the accuracy of the original prediction criteria set N is still below 0.7, in the prediction criteria library: The original prediction criterion set N may be replaced with a possible prediction criterion set.

  According to various embodiments, a new set of prediction criteria can be added to the prediction criteria library. According to these embodiments, if a disordered breathing episode occurs without prediction, the level of patient condition detected prior to the disordered breathing episode is stored as a possible set of predictive criteria. Each time a possible set of predictive criteria is satisfied, no respiratory disorder is predicted, but the accuracy of the possible set of predictive criteria is updated using, for example, an equation similar to the form of Equation 2. The If the accuracy of a possible set of prediction criteria reaches a selected level, eg, 0.7, the possible set of prediction criteria may be added to the prediction criteria library.

  According to various embodiments, the system can also be adjusted to increase the sensitivity of the sensitive breathing disorder prediction criteria set. The estimated sensitivity of the prediction criterion set N can be expressed as follows. That is,

Where TP (true positive) is the number of times the predictive criterion was successful in predicting disordered breathing, and FN (false negative) is erroneously predicted that the predictive criterion would not cause disordered breathing. This is the predicted number of times.

  In one embodiment, if the accuracy of the prediction criteria of the prediction criteria set N is greater than a selected number, eg, 0.9, of the states represented in the prediction criteria set N One or more threshold levels can be adjusted to enhance sensitivity.

  In one example, the threshold level of each state represented in the prediction criterion set N is modified with an incremental value to increase the sensitivity of the prediction criterion set N. In another embodiment, the state represented in the prediction criterion set N is compared with the state that exists immediately before the occurrence of the disordered breathing to determine how to modify the prediction criterion set N. In yet another embodiment, the modified state threshold level is based on the relative importance of the state in the overall predictive criteria. In another example, if the level of a particular state is changing just before the onset of breathing disorder, but the level of other states is still constant, only the changing state may be corrected .

  A prediction criterion set adjusted according to adjustments by any of the above processes may be referred to as a possible prediction criterion set. Each time a possible set of predictive criteria is satisfied, no prediction of respiratory impairment is made, but the accuracy of the possible set of predictive criteria is updated using, for example, Equation 2 or 3. If the accuracy of a possible set of prediction criteria reaches a selected level, eg, 0.7, the possible set of prediction criteria may be added to the prediction criteria library.

  The system can also be adjusted to improve the negative predictive value (NPV) of specificity or disordered breathing prediction criteria in a manner similar to the adaptive method described above. The calculation of the NPV of specificity and prediction criterion N can be performed using the following equations 4 and 5.

Where TN (true negative) is the number of times the predictive criteria have successfully predicted a lack of respiratory impairment, and FP (false positive) is the number of times the predictive criteria have incorrectly predicted respiratory impairment. , And FN (false negative) is the number of times the prediction criteria incorrectly predicted a lack of respiratory impairment.

  The flowchart of FIG. 20 illustrates a method for determining and updating a prediction criteria library according to an embodiment of the present invention. Previous observations of respiratory impairment may be categorized from population data 2002 or from specific patient 2004 past observations. One or more prediction criteria sets are determined and built in the prediction criteria library 2006.

  A condition associated with a disordered breathing is periodically detected 2008 and compared to a set of predictive criteria in the predictive criteria library. If the condition matches any of the predictive criteria sets in the library, 2010, a disordered breathing is predicted 2015. Within a selected window of time following the prediction of a disordered breathing, the system determines 2020 whether a disordered breathing has occurred.

  One exemplary method for detecting a respiratory disorder includes, for example, monitoring respiratory waveform output using a transthoracic impedance sensor. A hypopnea event is declared if the patient's respiratory tidal volume (TV) indicated by the transthoracic impedance signal falls below a low respiratory threshold. For example, a hypopnea event may be declared if a patient's tidal volume falls below about 50% of a recent average tidal volume or other baseline tidal volume. An apnea event is declared if the patient's tidal volume has further dropped to an apnea threshold (eg, about 10% of the recent average tidal volume).

  If the disordered breathing occurred 2020, the accuracy of the predictive criteria of the predictive criteria set used to predict the disordered breathing is updated 2025. If the accuracy of the updated prediction criteria is greater than the selected number 2030, a possible set of prediction criteria is formed 2035. Possible prediction criteria sets may be formed, for example, by replacing with a more sensitive state level when compared to the original prediction criteria set.

  Following prediction, if the respiratory disorder is not detected 2020, the accuracy of the prediction criteria set is updated 2040. If the accuracy of the prediction criteria set is reduced 2045 below the selected number, a possible prediction criteria set is formed 2050. A possible set of prediction criteria may be formed, for example, by replacing with a more severe state level to produce a more accurate prediction.

  If the detected patient condition does not match any of the predictive criteria sets in the predictive criteria library, 2010, a respiratory disorder is not predicted. Within the window of time following the prediction of the disordered breathing, the system determines 2055 whether the disordered breathing occurs. If breathing disorder 2055 occurs, the system checks 2060 if the patient condition matches any of the possible set of predictive criteria. If the patient status does not match any of the possible predictive criteria sets 2060, a possible predictive criteria set is formed 2056.

  If the patient status matches 2060 with the possible set of criteria, the accuracy of the possible predictive criteria set is updated 2070. If the accuracy of possible prediction criteria has increased 2075 beyond the selected number, a set of possible prediction criteria is added 2080 to the prediction criteria library.

Adapting Treatment Based on Treatment Effectiveness As previously described with reference to FIG. 2, the disordered breathing treatment system may include a treatment assessment processor 260 for assessing various parameters of the treatment. The treatment assessment processor may receive from one or more of sensors 280, 231 inside the patient, sensors 290 outside the patient, and / or other input devices 270 capable of sensing the condition affecting the patient. Can receive input. The treatment assessment processor may also receive information from one or more of cardiac arrhythmia detector 252, sleep quality monitor 254, sleep detector 256 and / or respiratory disorder detector / predictor 258. . The treatment assessment processor can use information obtained from one or more of these sources to tailor the treatment to achieve a treatment goal, for example, to achieve a level of effectiveness The treatment to do can be adapted.

  In one implementation, the treatment goal may include terminating the onset of the detected disordered breathing, and the treatment of the disordered breathing may be adapted to achieve this goal. Additionally or alternatively, the treatment goal may include ending the onset of breathing problems and preventing further breathing problems. In this example situation, the therapeutic action is a second prophylactic treatment to give a first treatment to end the onset of breathing disorders and to reduce or eliminate the onset of further breathing disorders. Can be adapted to be given. The second prophylactic treatment can be adapted to reduce the onset of respiratory disorders below a predetermined respiratory disorder onset threshold. The respiratory disorder onset threshold may be expressed, for example, in terms of apnea / hypopnea index (AHI) or periodic respiratory percent time (% PB).

  FIG. 21 is a flow chart illustrating a method for adapting a disorder of breathing according to an embodiment of the present invention. The flowchart of FIG. 21 illustrates a method for adapting the treatment of breathing disorders to achieve a desired level of treatment effectiveness. In this embodiment, a first set of conditions associated with a disordered breathing is detected 2110 and used to determine whether a disordered breathing episode has occurred. If a disordered breathing is detected 2120, a disordered breathing therapy is delivered to the patient 2130 to alleviate the disordered breathing. In one embodiment, the therapy delivered to the patient is, for example, cardiac pacing at a rate above the intrinsic rate, or cardiac pacing at a rate above the normally programmed rate, such as a normally programmed sleep rate. May be included.

  Adaptation of cardiac electrical therapy may also include modification of electrical stimulation energy with or without increased pacing rate. Increased stimulation energy has been shown to produce higher cardiac contractility, which is particularly advantageous for patients suffering from chronic heart failure. Decreased cardiac contractility is thought to initiate and drive the progression of heart failure (a disorder associated with Chain Stokes breathing).

  Furthermore, adaptation of cardiac electrical therapy to alleviate respiratory disturbances may include non-excitable electrical stimulation of one or more heart chambers (eg, left and / or right ventricle) or other heart sites. Adaptation of therapy including may be included. Non-excitable electrical stimulation may be delivered during the absolute refractory period of heart tissue, for example, to improve heart contractility. Non-excitatory stimulation therapy may be used alone or in combination with pacing to provide comprehensive treatment for patients with respiratory disorders such as CHF and Chain Stokes breathing.

  In other embodiments, adapting cardiac electrotherapy to alleviate breathing disorders may include initiating a specific pacing procedure or switching from one pacing mode to another. In one example, the cardiac pacing procedure may be switched from a dual chamber pacing mode to a bichamber or other resynchronization mode. In other examples, the pacing mode may be switched to a pacing mode that facilitates atrial pacing or facilitates consistent ventricular pacing. In yet another example, cardiac electrical therapy may include initiating multi-site electrical stimulation to the heart or changing from one electrical stimulation site to another. The pacing mode may be switched from a single chamber to multiple chambers or vice versa. For example, the biventricular mode may be switched only to the left ventricular mode. Alternatively, single chamber mode (eg, LV or RV) may be switched to a dual chamber mode. Various pacing modes, pacing sites, or other treatment measures including non-excitable electrical stimulation are possible in conjunction with the delivery of cardiac electrical therapy for respiratory disorders. The type of cardiac electrotherapy that is beneficial to the patient is highly patient specific and may be determined based on the particular patient response.

  A second set of conditions associated with treatment effectiveness is sensed 2140 and used to assess treatment effectiveness. The detected condition used to assess the effectiveness of the treatment and to adapt the treatment to alleviate the disordered breathing is an acute condition associated with the disordered breathing, such as interrupted breathing, hypoxia, It may represent one or more of arousal, negative pressure in the chest cavity, blood pressure, and detected onset of heart rate or blood pressure surge.

Additionally or alternatively, conditions used to assess treatment effectiveness and to adapt cardiac electrotherapy include, for example, reduced heart rate variability, increased blood pressure, sympathetic activity It may include chronic changes and one or more chronic conditions associated with respiratory disorders, including changes in blood chemistry such as PaCO ₂ levels and, inter alia, increased norepinephrine levels.

  In general, therapeutic goals in the treatment of respiratory disorders are the least aggressive treatments that effectively relieve, terminate or prevent a patient's respiratory disorder, or achieve a specific therapeutic goal associated with the treatment of a respiratory disorder Is to give. Respiratory disorder treatment measures may be enhanced by increasing the intensity or level of therapy to more effectively alleviate the respiratory disorder. Alternatively, the treatment of disordered breathing maintains a desired reduction in the severity or frequency of the onset of disordered breathing while reducing the intensity or level of therapy, thus reducing unwanted side effects due to therapy and the device It may be enhanced by extending the life time.

  Less aggressive if treatment effectiveness was acceptable 2150 (eg, to terminate or reduce a patient's respiratory disorder or meet some other desired goal) The therapy can be adapted 2160 such that the therapy is performed (eg, reduced pacing rate, reduced pacing energy, or changed pacing mode as described above). If the treatment was not effective 2150, to increase the effectiveness of the treatment by providing a more aggressive treatment (eg, increasing pacing speed, increasing pacing energy, or switching pacing mode) The treatment can be adapted 2170.

  In one embodiment, the treatment may be determined to be ineffective if the disordered breathing has not been alleviated after treatment delivery. In this situation, the therapy can be adapted to provide a more aggressive therapy (eg, a higher rate of cardiac pacing). In another embodiment, if the severity of the disordered breathing is sufficiently reduced or otherwise sufficiently alleviated, a less aggressive treatment (eg, slower pacing or reduced energy levels) The treatment may be enhanced by adapting the treatment to be performed. As noted above, less aggressive treatments are preferred, for example, to reduce the risk of arousal, avoid unnecessary stress on the patient's heart, and extend battery life.

  The flowchart of FIG. 22 illustrates a method for adapting treatment of respiratory disorders taking into account both the effectiveness of treatment and the impact of treatment, according to an embodiment of the present invention. In this example, a first set of conditions indicative of a disordered breathing is sensed 2210 and used to determine whether a disordered breathing episode has occurred. If a respiratory disorder is detected 2220, treatment is delivered to the patient 2230 to alleviate the respiratory disorder.

  A second set of conditions (which may overlap the first set) is sensed 2240 and used to adapt the treatment. Based on the second set of sensed conditions, the effectiveness of the treatment is assessed 2245. If the effectiveness of the treatment is not acceptable 2250, the treatment may be adapted 2260 to enhance the effectiveness of the treatment. If the treatment effectiveness was acceptable 2250, the impact of treatment on the patient may be assessed 2270.

  If the impact of treatment on the patient was acceptable 2280, the system continues to deliver the treatment. If the treatment was complete 2285, treatment is terminated 2290.

  If the impact of the treatment on the patient has exceeded an acceptable limit, the treatment impact is not acceptable 2280, and thus the treatment may be adapted 2260 to reduce the treatment impact. Various methods for assessing treatment impact and determining whether treatment impact is acceptable will now be described.

  The methods illustrated in the flow graphs of FIGS. 21 and 22 are intended for real-time monitoring of patient status, allowing the treatment system to dynamically adjust treatment measures to meet changing patient needs. It is to make. In one configuration, the treatment may be adjusted during the period in which the treatment is delivered to the patient. In another configuration, therapy is based on an assessment of the effectiveness of the therapy delivered in connection with the development of one or more previously detected respiratory disorders, or between the development of respiratory disorders and Can be adapted from night to night.

  The assessment of the impact of the disordered breathing treatment on the patient preferably takes into account the impact of the disordered breathing treatment on the overall treatment goal for the patient, including the goal of cardiac pacing therapy and the goal of treating the disordered breathing. The treatment of breathing disorders may include various treatment measures that are performed to achieve a predetermined therapeutic goal. Detecting and analyzing the onset of respiratory disorders that occur during treatment delivery or other periods, including periods of wakefulness, to the extent that the effectiveness of the therapy or therapy meets one or more therapeutic goals May be assessed.

  For example, a therapeutic goal may include ending the onset of a disordered breathing, and the treatment of the disordered breathing may be adapted to achieve this goal. Additionally or alternatively, the treatment goal may include ending the onset of breathing problems and preventing further breathing problems. In this example situation, the therapeutic measures are adapted to provide a first treatment to end the onset of breathing disorder and a second preventive treatment to reduce or eliminate the onset of further breathing disorders. be able to. The second prophylactic treatment can be adapted to reduce the onset of respiratory disorders below a predetermined respiratory disorder onset threshold. The respiratory disorder onset threshold can be expressed, for example, in terms of apnea / hypopnea index (AHI) or periodic respiratory percent time (% PB).

Cardiac Pacing for Respiratory Disorders In various implementations, treatment of respiratory disorders may include overdrive pacing. Overdrive pacing involves pacing one or more heart chambers at a rate higher than the intrinsic rate. According to an embodiment of the present invention, the therapy for alleviating the disordered breathing includes overdrive cardiac pacing of one or more atria and / or one or more ventricles as a treatment for the disordered breathing. It is out.

  When operating in an overdrive pacing mode, a cardiac rhythm management device can deliver pacing pulses at a pacing priority (PP) rate that is above a small amount of intrinsic heart rate. If an intrinsic beat is detected, the PP speed may be increased until it is slightly faster than the intrinsic heart rate of the sensed beat. The PP rate may then be gradually decreased to look for the intrinsic heart rate. After an intrinsic beat is sensed, the PP rate may be increased until the pacing rate is a small amount above the intrinsic heart rate.

  In one implementation, the CRM device may be switched to operate in an overdrive pacing mode upon detection or prediction of a respiratory disorder. In another implementation, the CRM device may be switched to operate in an overdrive pacing mode following determination that the patient is asleep. In another implementation, the disordered breathing characteristic is used to develop the indicated pacing interval. The following description includes atrial overdrive pacing in AAI (R) or DDD (R) mode. It will be appreciated that a similar approach may be performed to perform ventricular overdrive pacing in VVI (R) mode or overdrive pacing in biventricular mode.

  FIG. 34 is a block diagram illustrating a pacemaker controller 3425 according to an embodiment of the present invention. Pacemaker controller 3425 uses signals from several different inputs to modify the rate at which pacing or other therapy is delivered. For example, input # 1 can give information about atrial heart rate, input # 2 can give information about ventricular heart rate, and input # 3 gives an indication of accelerometer-based activity And input # 4 can provide an impedance-based respiration indicator, such as minute ventilation. Based on at least one of these and / or other inputs, the controller 3425 is delivered to a treatment delivery circuit (eg, one or more of an atrial treatment delivery circuit and a ventricular treatment delivery circuit). A pacing speed output instruction is given as a control signal.

  The atrial and ventricular treatment delivery circuit issues pacing pulses based on one or more such control signals received from controller 3425. The pacing rate can be controlled using any combination of software, hardware, firmware, or the like, controller 3425 alone or in combination with peripheral circuits or modules. The software embodiment provides flexibility in how the input is processed and allows the device software to remain implanted in the patient without the need to perform surgery to remove and / or replace the device. An opportunity to update remotely can also be provided.

  In various embodiments, the CRM device provides cardiac pacing therapy for treating respiratory disorders. The CRM device obtains the interval between successive sensed or induced atrial beats. The CRM device calculates a new first indicated pacing interval based at least in part on the duration of the cardiac interval and the previous value of the first indicated pacing interval. In various implementations, the duration of the cardiac interval used to calculate the new first indicated pacing interval includes the duration of the previous cardiac interval or the duration of the most recent cardiac interval. You can leave. The CRM device provides pacing therapy delivered at a rate corresponding to the inverse of the duration of the first indicated pacing interval.

  FIG. 35 is a block diagram illustrating one conceptualization of the portion of the controller 3425 used to perform overdrive pacing for the treatment of respiratory disorders, according to an embodiment of the present invention. The following description includes atrial overdrive pacing in AAI (R) or DDD (R) mode. It will be appreciated that a similar approach may be performed to perform ventricular overdrive pacing in VVI (R) mode or overdrive pacing in biventricular mode.

  At least one signal from the atrial sensing circuit is received by the atrial phenomenon module 501, which recognizes the occurrence of an atrial phenomenon included in the signal. Such a phenomenon is also called “beat”, “activation”, “depolarization”, “P-wave”, or “contraction”. The atrial phenomenon module 3501 can detect an intrinsic phenomenon (also called a sensed phenomenon) from a signal obtained from the atrial sensing circuit. The atrial phenomenon module 3501 also detects an induced phenomenon (resulting from the pace) from a signal obtained from the atrial sensing circuit or preferably from an atrial pacing control signal obtained from the pacing control module 3505. The module can also trigger delivery of pacing stimuli by the atrial therapy circuit. Thus, atrial phenomena include both intrinsic / sensed phenomena and induced / paced phenomena.

  The time interval between successive atrial events (referred to as the AA interval) is recorded by a first timer such as the AA interval timer 3510. Filter 3515 calculates a “first indication pacing interval” (ie, an indication of the desired time interval between atrial events, or otherwise, the desired atrial heart rate). The first indicated pacing interval is also referred to as an atrial pacing priority (APP) indicated pacing interval. In various embodiments, filter 3515 may be an averager, a weighted averager, a median filter, an infinite impulse (IIR) filter, a finite impulse response (FIR) filter, or some other that performs the desired signal processing described in more detail below. Analog or digital signal processing circuitry.

  In one embodiment, filter 3515 is based on the duration of the most recent AA interval recorded by timer 3510 and the previous value of the first indication pacing interval stored in first indication pacing interval register 3520. Calculate a new value for the first indication pacing interval (also referred to as the APP indication pacing interval). Register 3520 is then updated by storing the newly calculated first indication pacing interval in register 3520. Based on the first indicated pacing interval stored in register 3520, pacing control module 3505 delivers a therapy, such as a pacing stimulus, at an APP indicated atrial heart rate corresponding to the inverse of the duration of the first indicated pacing interval. A control signal is delivered to the atrial treatment circuit for

FIG. 36 is a signal flow diagram illustrating one embodiment of a process for operating filter 3515. Upon occurrence of a sensed or induced atrial beat, timer 3510 provides to filter 3515 the duration of the AA interval (referred to as the most recent AA interval (AA _n )) determined by that beat. Filter 515 also receives the previous value (T _n-1 ) of the first indicator pacing interval stored in register 3520. The most recent AA interval AA _n and the previous value T _n-1 of the first indication pacing interval are respectively scaled by the respective constants A and B and then summed to give the first indication pacing interval (T _n ), Which is stored in register 3520 and provided to pacing control module 3505. In one embodiment, coefficients a and b are different values and are either programmable, variable, or constant.

If no atrial beat is sensed during the new first indicating pacing interval T _n (which is measured as the time since the occurrence of the atrial beat that determines the most recent AA interval AA _n ) , the pacing control module 3505, upon expiration of the new first indicated pacing interval T _n, to deliver an atrial pacing pulse, it instructs atrial therapy circuit. In one embodiment, the operation of the filter is described by T _n = A ● AA _n + B ● T _n−1 , where A and B are coefficients (also called “weights”), and AA _n is , The duration of the most recent AA interval, and T _n-1 is the previous value of the first indicated pacing interval.

  From these examples, the first indicated pacing interval is calculated using either a sensed or paced termination phenomenon and using either a sensed or paced onset phenomenon. I understand that I can do it.

  Initializing filter 3515 includes seeding the filter by storing the initial interval value in register 3520. In one embodiment, register 3520 is initialized to an interval value corresponding to a lower rate limit (LRL) (ie, the lowest rate at which pacing pulses are delivered by the device). Register 3520 may alternatively be initialized with some other suitable value.

In one embodiment, the operation of filter 3515 is that the beat that determines the most recent AA interval AA _n is a sensed / endogenous beat, or a paced / induced beat. Is there or is based on. In this embodiment, the pacing control module 3505 (which controls the timing and delivery of pacing pulses) is initiated by a pacing stimulus delivered to the filter 3515 with the most recent AA interval AA _n delivered by the CRM device. An input is provided that indicates whether it was determined by an induced beat or by an intrinsic beat sensed by an atrial sensing circuit.

In general terms, if the most recent AA interval AA _n is determined by a sensed / endogenous beat, then the filter 3515 will remove the value from the previous first indicated pacing interval T _n-1 . A new adjusted first indication pacing interval T _n is provided. For example, the new first indication pacing interval T _n is an amount based at least in part on the duration of the most recent AA interval AA _{n and} the duration of the previous value T _n-1 of the first indication pacing interval. Only it can be reduced. However, if the most recent AA interval AA _n was determined by a paced / induced beat, the filter 3515 increased the value of the previous first indicated pacing interval T _n-1 it may be given a first indication pacing interval T _n. For example, the new first indication pacing interval T _n is an amount based at least in part on the duration of the most recent AA interval AA _{n and} the duration of the previous value T _n-1 of the first indication pacing interval. It may only be increased. New during the first instruction pacing interval T _n (measured as the time from the most recent occurrence of atrial beat to determine the AA interval AA _n), if it is not sensed any atrial beats, the pacing control module 3505, upon expiration of the new first indicated pacing interval T _n, to deliver an atrial pacing pulse, may instruct the atrial therapy circuit.

FIG. 37 is a signal flow diagram illustrating another conceptualization of the process of operating the filter 3515, which is slightly different from FIG. 36, as will be described in more detail below. In this embodiment, the pacing control module 3505 (which controls the timing and delivery of pacing pulses) is initiated by a pacing stimulus delivered to the filter 3515 with the most recent AA interval AA _n delivered by the CRM device. An input is provided that indicates whether it was determined by an induced beat or by an intrinsic beat sensed by an atrial sensing circuit.

If the most recent AA interval AA _n was determined by an intrinsic beat, the most recent AA interval AA _n and the previous value T _n-1 of the first indicated pacing interval are the constant A and Scaled by B and then summed to a new value T _{n for} the first indicated pacing interval, which is stored in register 3520 and provided to pacing control module 3505. Alternatively, if the most recent AA interval AA _n is determined by induced / paced beats, the most recent AA interval AA _n and the previous value T _{n− of} the first indicated pacing interval ₁ is scaled by each constant C and D, respectively, and then summed to a new value T _{n for} the first indicated pacing interval, which is stored in register 3520 and provided to pacing control module 3505 . In one embodiment, the coefficients C and D are different from each other and are either programmable, variable, or constant. In a further embodiment, coefficient C is a different value than coefficient A and / or coefficient D is a different value than coefficient B, and these coefficients are programmable, variable, or constant. Either. In another embodiment, coefficient D is the same value as coefficient B.

In one embodiment, operation of filter 3515, if AA _n is determined by the pulsation of the endogenous, described by _{T n = A ● AA n +} B ● T n-1, and, AA _n is pacing If it is determined by the beats it is described by _{T n = C ● AA n +} D ● T n-1, wherein, a, B, C and D is a coefficient (also referred to as "weight") Yes, AA _n is the duration of the most recent AA interval, T _n is the new value of the first indication pacing interval, and T _n-1 is the previous value of the first indication pacing interval Value. If no atrial beat is sensed during the new first indicating pacing interval T _n (which is measured as the time since the occurrence of the atrial beat that determines the most recent AA interval AA _n ) , the pacing control module 3505, upon expiration of the new first indicated pacing interval T _n, to deliver an atrial pacing pulse, to instruct the atrial therapy circuit.

Another method of operating the filter 3515 is shown in the signal flow graph of FIG. In this embodiment, coefficients A, B, C, and D can be described in more detail using intrinsic coefficients (a), paced coefficients (b), and weighting coefficients (w). In one such embodiment, A = a.fwdarw.B = (1-w), C = b.fwdarw.w, and D = (1-w). In one example, the behavior of filter 3515 is described by T _n = a ● w ● AA _n + (1-w) ● T _n-1 , where AA _n is determined by an intrinsic beat, and AA _n Is determined by the paced beat, it is otherwise described by T _n = b w w A AA _n + (1-w) T T _n-1 .

If no atrial beat is sensed during the new first indicating pacing interval T _n (which is measured as the time since the occurrence of the atrial beat that determines the most recent AA interval AA _n ) , the pacing control module 3505, upon expiration of the new first indicated pacing interval T _n, to deliver an atrial pacing pulse, to instruct the atrial therapy circuit. In one embodiment, coefficients a and b are different from each other and are either programmable, variable, or constant.

The above parameters (eg, A, B, C, D, a, b, w) are described in terms of time intervals (eg, AA _n , T _n , T _n-1 ). However, other systems can produce results in terms of speed, not time intervals, without departing from the method and apparatus. In one embodiment, the weighting factor w, the intrinsic factor a, and the paced factor b are variables. If the selection of w, a, and b is different, the operation of the method and apparatus will be different. For example, as w increases, the weighting effect of the most recent AA interval AA _n increases and the weighting effect of the previous first indicated pacing rate T _n-1 decreases. In one embodiment, w = 1/16 = 0.0625. In another embodiment, w = 1/32. Another possible range for w ranges from w = 1/2 to w = 1/1024. Further possible ranges for w range from about 0 to about 1. Other values of w (which may not include division by a power of 2) may be substituted without departing from the method and apparatus.

  In one embodiment, the intrinsic coefficient a is selected to be 1.0 or less (or alternatively, 1.0 or less or equal to 1.0). In one example, the intrinsic factor a is selected to be a value less than the pacing factor b. In one embodiment, a may be about 0.6 and b may be about 1.5. In another embodiment, a = 1.0 and b = 1.05. One possible range for a ranges from a = 0.6 to a = 1.0, and one possible range for b ranges from b = 1.05 to b = 1.5. The coefficients may be changed without departing from the method and apparatus.

In one embodiment, if a <1.0, filter 3515 provides a new first indicator pacing interval T _n that is at least slightly shorter than the expected intrinsic AA interval being measured by timer 3515. Accordingly, filter 3515 operates to promote atrial pacing by increasing the APP indicated rate to be slightly faster than the intrinsic atrial rate. The APP command rate is then gradually reduced to search for the underlying intrinsic atrial heart rate. When an atrial beat is sensed, the APP filter 3515 again increases the APP indicated pacing rate to a small amount faster than the intrinsic atrial rate. As a result, most atrial heart beats are paced rather than sensed.

  The overdrive pacing described above, or overdrive pacing performed in conjunction with one or more ventricular pacings, may be given as a treatment for respiratory disorders. Additionally, such pacing therapies may be activated upon detection or prediction of respiratory impairment. For example, pacing may be performed at a programmed rate until the onset of a respiratory disorder is detected. After detecting a disordered breathing, the CRM device may switch to overdrive pacing to alleviate the disordered breathing.

  In another example, the CRM may deliver pacing at a programmed rate until the patient condition indicates that a disordered breathing may occur. After a disordered breathing is predicted, the CRM may deliver overdrive pacing to prevent or alleviate the development of the disordered breathing.

  Although breathing problems may occur while the patient is awake, it is most likely during sleep. In another example, the CRM may comprise a sleep detection system. If the CRM detects that the patient is asleep or if the CRM detects a specific sleep state (eg, non-REM sleep), the CRM transitions from pacing at a programmed rate to overdrive pacing. You may switch.

  FIG. 39 is a block diagram illustrating another conceptualization of the portion of the controller 3425, generally, by way of example, but not limitation, which is different from FIG. 35 as described in more detail below. Somewhat different. In FIG. 39, the controller 3425 receives a control signal indicating an overdrive pacing rate for the disordered breathing therapy from the disordered breathing therapy control circuit. The signal may be based on, for example, the severity, duration, frequency, or type of respiratory disorder experienced by the patient, or other factors such as treatment interaction and / or patient comfort. In one example, the control signal may be based on a disordered breathing index such as an apnea / hypopnea index. The pacing rate for the disordered breathing treatment is expressed in terms of a second indicated pacing interval stored in register 3910.

The pacing control module 3505 delivers a control signal, which signals pacing pulses based on either (or both) of the first or second indicated pacing intervals stored in registers 3520 and 3910, respectively. Instruct the atrial treatment circuit to deliver. In one embodiment, the pacing control module 3505 includes a selection module 3915 that selects between the second indication pacing interval that is modulated by the state of the new first indicated pacing interval T _n and respiratory disorders.

In one embodiment, selection module 3925, as the selected indicated pacing interval S _n, selects whichever shorter of the first and second instructions pacing interval. If no atrial beat is sensed during the selected indicated pacing interval S _n (which is measured as the time since the occurrence of the atrial beat that determines the most recent AA interval AA _n ), pacing control module 3505, upon expiration of the selected indicated pacing interval S _n, so as to deliver an atrial pacing pulse, to instruct the atrial therapy circuit.

In general terms, in this embodiment, the atrium is paced at the higher of the disordered breathing rate and the APP indicated rate. For example, if the patient is not experiencing any respiratory disturbances or is experiencing only mild respiratory disturbances, the respiratory disorder treatment rate will be lower than the patient's intrinsic rate, and then the atrial pacing pulse However, it will be delivered at the APP indicated rate, which is generally slightly higher than the patient's intrinsic atrial heart rate. However, for example, if the patient is experiencing a more significant disordered breathing and therefore the disordered breathing rate is higher than the APP indicated rate, pacing pulses will generally be delivered at the disordered breathing rate . In another embodiment, the pacing rate is not by selecting the higher of these two indicated rates (ie, the shorter of the first and second indicated pacing intervals), Determined by mixing treatment rate and APP indicated rate. In one such example, selection module 3925, the first and second instruction pacing interval, plus a predetermined or other weights to compute the pacing interval S _n to be selected.

  FIG. 40 is a graph illustrating one embodiment of the APP indicated rate for successive atrial heart beats, generally by way of example but not limitation, for one mode of operating filter 3515. As noted above, the APP indicated rate is simply the frequency between atrial heart beats associated with the first indicated pacing interval. In other words, the APP indication speed is inversely proportional to the duration of the first indication pacing interval. If pacing is based solely on the APP indicated rate, the pacing control module 3505 will issue a pacing pulse after the last atrial beat is equal to or exceeds the first indicated pacing interval. Command the atrial treatment circuit. However, as described above, in certain embodiments, the pacing control module 3505 may generate pacing pulses based on factors other than the APP indicated rate (e.g., based on the severity of the respiratory disorder experienced by the patient). Command the atrial treatment circuit.

In the example shown in FIG. 40, the first paced atrial beat (denoted “P”) is a first indicated pacing interval (ie, an APP indicated pacing interval) calculated based on the previous atrial beat. ) Occurs during T ₀ exhalation. In one embodiment, the new APP indication pacing interval T ₁ is calculated based on the duration of the most recent AA interval AA ₁ and the previous value T ₀ of the APP indication pacing interval, as described above. In Figure 40, the new APP indicated pacing interval T ₁ corresponds to the time interval of the velocity limit (LRL). In one embodiment, as shown in FIG. 40, the allowable range of the APP indication pacing interval is such that the APP indication pacing interval does not exceed the duration of the LRL time interval and the APP indication pacing interval is the upper speed limit. Limited so that it is not shorter than the duration of the (URL) time interval.

In the example of FIG. 40, when expiration of APP indicated pacing interval T _1, the second atrial beat is also paced. In one embodiment, the new APP indication pacing interval T ₂ is calculated based on the duration of the most recent AA interval AA ₂ and the previous value T ₁ of the APP indication pacing interval, as described above. Because the APP indicated atrial heart rate is higher than the underlying intrinsic atrial heart rate, the first and second atrial beats are paced beats.

The third atrial beat is sensed well before than exhalation of APP indicated pacing interval T _2, therefore, not issued any pacing pulses. For the sensed third atrial beat, filter 3515 calculates a new APP indicated pacing interval T ₃ such that the duration is shorter than the previous APP indicated pacing interval T ₂ .

The fourth, fifth, and sixth atrial beats are sensed prior to the expiration of the APP indicated pacing intervals T ₃ , T ₄ , and T ₅ , respectively. For each of the sensed fourth, fifth, and sixth atrial beats, filter 3515 calculates a new APP indicated pacing interval so that the duration is shorter than the previous APP indicated pacing interval. .

During the seventh atrial beat, the APP indicated heart rate has increased above the underlying intrinsic atrial heart rate, and therefore the seventh atrial beat is exhaled at the APP indicated pacing interval T ₆ . Sometimes paced. Since the seventh atrial beat is paced rather than sensed, the new APP indication pacing interval T ₇ is calculated to be longer than the previous APP indication pacing interval T ₆ .

Similarly, the 8th and 9th atrial beats are respectively paced during expiration of the corresponding APP indicated pacing interval (ie, T ₇ and T ₈ ), respectively. Each APP indication pacing interval T ₇ and T ₈ is longer than the corresponding previous APP indication pacing interval (ie, T ₆ and T ₇ ), respectively. In this manner, the APP indicated atrial heart rate is gradually reduced to search for the underlying intrinsic atrial heart rate.

At the time of the 10th atrial beat, the APP indicated heart rate is sufficiently reduced so that the 10th atrial beat can be sensed. Atrial beat of the 10 is sensed before expiration of the APP indicated pacing interval T _9, therefore, not issued any pacing pulses. For the sensed tenth atrial beat, filter 3515 calculates a new APP indicated pacing interval T ₁₀ such that the duration is shorter than the previous APP indicated pacing interval T ₉ .

Atrial beat 11, upon expiration of the APP indicated pacing interval T _10, is paced. About 11 atrial beat that is paced, the filter 3515, so that the duration for the previous APP indicated pacing interval T ₁₀ becomes longer, calculating the new APP indicated pacing interval T _11. Similarly, the twelfth and thirteenth atrial beats are respectively paced during expiration of the corresponding APP indicated pacing interval (ie, T ₁₁ and T ₁₂ ). Each APP indication pacing interval T ₁₂ and T ₁₃ is longer than the corresponding previous APP indication pacing interval (ie, T ₁₁ and T ₁₂ ), respectively. In this manner, the APP indicated atrial heart rate is gradually reduced to find the underlying intrinsic atrial heart rate.

Atrial beat of the 14 is sensed before expiration of the APP indicated pacing interval T _13, therefore, is not issued any pacing pulses. For the sensed 14th atrial beat, filter 3515 calculates a new APP indication pacing interval T ₁₄ such that the duration is shorter than the previous APP indication pacing interval T ₁₃ .

Atrial beat 15 is paced at expiration of the APP indicated pacing interval T _14. For the 15th paced atrial beat, the filter 3515 calculates a new APP indicated pacing interval T ₁₅ such that the duration is longer than the previous APP indicated pacing interval T ₁₄ .

  The intrinsic factor of the filter 3515 controls the “attack slope” of the APP command heart rate as the APP command heart rate increases due to the sensed intrinsic beat. The paced factor b of filter 3515 controls the “decay slope” of the APP indicated heart rate as the APP indicated heart rate decreases during the paced beat. In one embodiment, if a <1.0 and b> 1.0, further reducing the value of a to 1.0 or less increases the attack slope, and thus the APP indicated speed is reduced to the sensed intrinsic beat. In response, increasing faster, while decreasing the value of b toward 1.0 reduces the decay slope, and thus the APP indicated rate decreases more slowly during the paced beat. Conversely, if a <1.0 and b> 1.0, increasing the value of a towards 1.0 reduces the attack slope, and thus the APP indicated speed is reduced to the detected intrinsic beat. In response, increasing slower, while increasing the value of b from 1.0 increases the decay slope, and thus the APP indicated rate decreases faster during the paced beat.

  In one embodiment, if a <1.0 and b> 1.0, reducing both a and b increases the APP indication rate, and thus the APP indication rate is higher than the average intrinsic rate. Due to the higher APP indication speed, intrinsic heart rate variability is less likely to be a perceived phenomenon. On the other hand, if a <1.0 and b> 1.0, increasing both a and b decreases the APP indication rate, and thus is closer to the average intrinsic rate. Since the APP indicated speed is closer to the average intrinsic speed, the same degree of variability in intrinsic heart rate is more likely to be a perceived phenomenon. Thus, by adjusting the coefficient of filter 3515 as described above, a more intrinsic beat can be obtained than a paced beat for a certain degree of variability in the patient's heart rate. .

  In one embodiment, these coefficients are programmable by the user, such as by using a remote programmer. In another embodiment, the user selects a desired performance parameter (e.g., desired degree of overdrive pacing, desired attack slope, desired attenuation slope, etc.) from a corresponding range of possible values, and The CRM device automatically selects the appropriate combination of coefficients for the filter 3515 and generally sets the filter settings corresponding to the selected user programmed performance parameters as shown in Table 5. Other levels of programmability or different combinations of coefficients may also be used.

  FIG. 40 shows that the sensed atrial beat increases the APP indicated rate by an amount based on the sensed atrial heart rate. Thus, if the increase in sensed atrial rate is rapid and large, the APP indication rate will increase faster than if the sensed atrial heart rate is slower and smaller. However, the increase in APP indicated speed is not dependent only on the sensed atrial heart rate. Instead, this increase in APP indicated heart rate is also dependent on previous values of APP indicated heart rate. This is because the APP indicated heart rate is not overly sensitive to a single very early atrial beat, the change in atrial speed is more gradual, and the extent of such speed change As described above, a smoothing function is provided so that it can be adjusted in a programmable manner. Further, in one embodiment, the filter 3515 operates continuously to provide continuous speed adjustment based on the APP command speed.

  FIG. 41 is a graph illustrating one embodiment of selecting between two or more directed pacing intervals, generally by way of example but not limitation. FIG. 41 is similar in some respects to FIG. 40, but FIG. 41 includes a second indicated pacing interval. In one embodiment, the first indication pacing interval is the APP indication pacing interval described above and the second indication pacing interval is the severity, frequency, duration, type, or type of respiratory disorder experienced by the patient, or Respiratory disorder pacing interval based on other parameters.

In one embodiment, the selected instruction pacing interval is based on the shorter of the first and second instruction pacing intervals. In other words, the CRM device delivers pacing pulses at a higher indicated pacing rate. In the example shown in FIG. 41, the first and second pulsations and the eighth to fifteenth pulsations are paced at the respiratory disorder treatment instruction speed because the respiratory disorder treatment instruction speed is APP This is because the indicated atrial rate and the intrinsic (sensed) atrial rate are higher. Third, fourth, fifth and sixth atrial beats are sensed intrinsic beats that are sensed during the shorter of either APP or the sensor-instructed pacing interval. is there. Beats seventh but is paced at APP indicated rate, because, APP indicated rate is higher than the disordered breathing therapy indicated rate, and any intrinsic beats in APP indicated interval T ₆ senses Because it is not done. In this embodiment, the range of both the sensor indication speed and the APP indication speed is limited so that they do not become higher than the URL or lower than the LRL. In one embodiment, the above equation for the filter 3515 indicates that the disordered disorder treatment indication is when the sensor indication rate is greater than the APP indication rate indicated by the first to third and eighth to fifteenth beats of FIG. Works to increase APP instruction speed towards speed. However, in another embodiment, if AA _n is determined by an APP-directed paced beat, then T _n = b ● w ● AA _n + (1-w) ● T _n-1 and , If AA _n is determined by a breathing disorder treatment instruction paced beat, then T _n = T _n-1 so that the APP indication speed is , Will remain unchanged. In one embodiment, the LRL and URL are programmable by the user, such as by using a remote programmer.

In one embodiment, the filter 3515 includes a variable coefficient, such as a coefficient that is a function of the heart rate (or its corresponding time interval), for example. In one example, the operation of the filter 3515, AA _n is, when it is determined by the intrinsic beats is described by _{T n = a ● w ● AA} n + (1-w) ● T n-1, AA _{If n} is determined by the paced beat, it is otherwise described by T _n = b ● w ● AA _n + (1-w) ● T _n-1 where a and b At least one is a linear, piecewise linear, or non-linear function of one or more previous AA intervals (eg, most recent AA interval AA _n etc.).

FIG. 42 illustrates an exemplary embodiment using at least one of the coefficients a and b as a function of one or more previous AA intervals (eg, the most recent AA interval AA _n etc.). By way of example, but not by way of limitation, FIG. In one such example, a is AA _n is the speed limit (e.g., 1000 millisecond interval or 60 beats / minute) If as or close to it, becomes smaller than 1.0, and, a is the AA _n, If it is at or near the upper speed limit (eg 500 ms interval or 120 beats / min), it will be greater than 1.0. For the constant b, using a smaller value of a at a lower rate would increase the pacing rate faster for the perceived phenomenon, and using a larger value of a at a higher rate would be perceived. For the phenomenon, the pacing rate increases slower. In another example, b is close to 1.0 when AA _n is at or near the lower speed limit, and b is greater than 1.0 when AA _n is at or near the upper speed limit. . For the constant a, using a smaller value of b at a lower rate will cause the pacing rate to decrease slower for a paced phenomenon, and using a larger value of b at a higher rate will result in pacing. The pacing rate decreases faster for the phenomenon.

  The above systems provide, inter alia, a cardiac rhythm management system that includes an atrial pacing priority (APP) filter to facilitate atrial pacing. The APP filter controls the timing of delivery of the atrial pacing pulse. Atrial pacing priority pacing may be initiated upon detection or prediction of a disordered breath, for example, to provide overdrive pacing to terminate or alleviate the occurrence of the disordered breathing.

  Atrial pacing pulses are generally delivered at a first indicated pacing rate (ie, an APP indicated rate) that is less than the intrinsic atrial heart rate. For sensed beats, the APP indicated pacing rate is increased until it is slightly faster than the intrinsic atrial heart rate. The APP directed pacing rate is then gradually reduced to search for the underlying intrinsic atrial heart rate. Then, after sensing atrial beats, the APP filter again increases the APP indicated pacing rate by a small amount faster than the intrinsic atrial rate. As a result, most atrial heart beats are paced rather than sensed.

  While the above description is intended to provide atrial overdrive pacing for the treatment of respiratory disorders, a similar process for performing ventricular overdrive pacing or biventricular overdrive pacing may be performed. The pacing rate may be adjusted based on the characteristics of the disordered breathing experienced by the patient. For example, overdrive pacing may be modulated by the type of disorder, severity, frequency, and / or duration.

  Further, the smoothed pacing rate may be limited. For example, the pacing rate may be capped or limited before the treatment is delivered. In another implementation, the interval between endogenous inputs may be limited to some predetermined value. The predetermined value may be set by the doctor or may be determined from other variables. By limiting the interval between endogenous inputs, the output pacing rate is limited. Limiting the smoothed pacing rate may be useful, for example, in managing atrial fibrillation or flutter.

Sleep Quality Monitoring According to various embodiments of the present invention, conditions related to sleep quality (e.g., among other sleep quality factors, sleep fragmentation and / or other wake-based measures, patients Reported calm sleep, and patient-reported discomfort during treatment delivered during sleep) may be used to assess the impact of treatment on the patient. Referring again to FIG. 2, the treatment assessment processor 260 uses the information acquired by the sleep quality monitor 254 to assess the impact of treatment based on sleep quality. For example, if a patient is being treated for effective breathing disorder and has low sleep fragmentation and reports quiet sleep with no discomfort, the effectiveness of treatment is relative The adverse effects of treatment on the patient may be relatively low. If sleep fragmentation unrelated to the development of respiratory disorders is relatively high, or if the patient reports discomfort or post-sleep fatigue, these conditions may indicate that treatment of the respiratory disorder is sleep disorder and / or It may indicate that other undesirable effects are occurring.

  Because breathing disorders generally occur during sleep, it may be particularly important to assess sleep quality during the delivery of treatment for breathing disorders. It would be undesirable to provide a treatment that eliminates breathing problems but increases sleep fragmentation. In such situations, the disordered breathing therapy may exacerbate the adverse effects caused by the disordered breathing. Accordingly, it may be preferable to assess the impact of the treatment on the patient and to adjust the treatment to improve sleep quality. If treatment of breathing disorders is ineffective and breathing problems occur during sleep, sleep fragmentation and sleep disruption may occur. Thus, treatment impact assessments based on detected sleep quality and / or patient-reported tranquil sleep may be preferable to consider an assessment of treatment effectiveness.

  Sleep quality assessment depends on the acquisition of sleep-related data, including the patient's typical sleep pattern and the physiological, environmental, contextual, emotional, and other conditions that affect the sleeping patient . Diagnosis of sleep disorders and assessment of sleep quality often involve the use of polysomnographic sleep studies in dedicated sleep facilities. However, such studies are expensive and inconvenient for the patient and may not accurately represent the patient's typical sleep behavior. In polysomnographic sleep studies, patients are instrumented for data acquisition and observed by trained personnel. Sleep assessment in a laboratory setting presents numerous obstacles in obtaining an accurate picture of a patient's typical sleep pattern. For example, when a patient spends the night in a sleep laboratory, they generally experience a condition called “First Night Syndrome” that involves sleep disruption during the first few nights in an unfamiliar place. Also, sleep while worn and observed may not be a realistic view of a patient's normal sleep pattern.

  In addition, polysomnographic sleep studies provide incomplete data sets for the analysis of certain sleep disorders, including, for example, sleep disordered breathing. A number of physiological conditions associated with sleep disordered breathing (eg, decreased heart rate variability, increased sympathetic activity, increased norepinephrine levels, and increased blood pressure variability) can be detected during periods of wakefulness. . Data collection during sleep and / or during arousal may provide a more complete picture of the patient's sleep quality.

  Various aspects of sleep quality, including the number and severity of arousal, the onset of sleep breathing disorders, nighttime limb movements, and the work of the heart, breathing, muscle, and nervous system, are useful for diagnostic and / or therapeutic delivery. May provide important information. The initial stage leading to sleep quality assessment is an accurate and reliable method for discriminating between sleep and awake periods. In addition, the acquisition of data about the patient's sleep state or stage (including sleep onset, termination, REM, and NREM sleep states) and wakefulness events (including autonomic arousal) is linked to sleep quality assessment. You can go. For example, the most tranquil sleep occurs during stages 3 and 4 of non-REM sleep. One indicator of sleep quality is the percentage of time a patient spends in these sleep stages. Knowledge of the patient's sleep pattern can be used to diagnose sleep disorders and / or adjust patient therapy including, for example, cardiac or respiratory therapy. Trends in the development of respiratory disorders, onset of wakefulness, and other sleep quality aspects are useful in determining and maintaining appropriate treatment for patients suffering from disorders ranging from snoring to chronic heart failure. There may be.

  The present invention includes a method and system for acquiring sleep quality data and using the sleep quality data to assess the effectiveness and / or impact of treatment of respiratory disorders delivered to a patient. The methods of the present invention include sensing conditions associated with a patient's sleep quality, including physiological and / or non-physiological conditions. Data related to the patient's sleep quality is collected based on the sensed condition. Sensing the condition affecting the patient and related to the quality of sleep may occur during periods of wakefulness and / or during periods of sleep. Sensing the state associated with sleep quality and / or collecting sleep quality data may be performed using a device having at least a partially implantable component.

  Table 1 lists a representative set of conditions associated with sleep quality. Patient conditions used to assess sleep quality may include, for example, both physiological and non-physiological (ie, contextual) conditions.

  Each of the states listed in Table 1 may be used for various purposes when assessing sleep quality. For example, a subset of states may be used to detect whether a patient is asleep and to track various stages of sleep and wake events. Another subset of conditions may be used to detect the onset of respiratory disorders. Another subset may be used to detect abnormal limb movements. In one implementation, some or all of the described conditions are collected over a relatively long period of time and used to analyze long-term sleep quality trends. Good. Trending may be used in conjunction with an overall assessment of sleep quality and the diagnosis and treatment of sleep disordered breathing, movement disorders, and / or other sleep disorders.

  Table 6 shows examples of how certain physiological and non-physiological conditions are combined with sleep quality assessment.

  FIG. 23 illustrates a patient 2310 wearing a sleep quality monitor according to an embodiment of the present invention. The sleep quality monitor 2320 uses a number of sensors 2311 to 2319 to collect sleep quality data from the patient. In one configuration, the collected data is analyzed by a treatment assessment processor that may be a component integral to the implantable respiratory disorder treatment system. The collected sleep quality data may be downloaded to an external device 2330 for storage, analysis, or display.

  In the implementation shown in FIG. 23, the implantable sleep quality monitor 2320 is coupled to a number of sensors 2311-2319. In this example, the sensor includes an EGM sensor 2316 for detecting heart rate and heart rate variability. Transthoracic impedance sensor 2317 is used to detect a patient's respiratory condition, including, for example, minute ventilation, respiratory rate, and tidal volume. An activity detector (eg, accelerometer) 2315 can be used to detect patient activity status. The sleep quality monitor 2320 senses the patient's condition including the patient's body position and site using the body position sensor 2314 and the bed proximity sensor 2313, respectively. The sleep quality monitor 2320 detects the brain activity of the patient using the EEG sensor 2311 and the eye movement of the patient using the EOG sensor 2312. Mandibular and limb movements are sensed using accelerometers attached to the patient's lower jaw 2318 and leg 2319.

  In this application, sleep quality monitor 2320 can be used for patient heart rate, heart rate variability, minute ventilation, respiratory rate, tidal volume, body position, bed access, brain activity, eye movement, mandibular movement. And configured to track leg movements. Sleep quality monitor 2320 samples the signal from the sensor at periodic intervals and stores data about the detected condition in a memory circuit within sleep quality monitor 2320. Sleep quality monitor 2320 may additionally access an external input unit 2330 for detecting patient-reported conditions (eg, recent use of cigarettes and medications by the patient). Further, sleep quality data monitor 2320 may monitor status using one or more external sensors. In this example, a thermometer 2335 is coupled via an external programmer 2330 and a contaminated website 2340 can access the sleep quality monitor 2320 via the Internet 2350.

  Sleep quality monitor 2320 can be operated to acquire data during both sleep and awake periods. For example, it may be advantageous to track changes in specific conditions measured during periods of wakefulness associated with sleep disordered breathing. For example, certain patients suffering from sleep apnea experience heart rate variability, blood pressure variability, and / or changes in sympathetic nerve activity during periods of wakefulness. The detection and analysis of physiological changes that should be attributed to sleep disorders and can be measured during the time the patient is awake provides a more complete picture of sleep quality.

  In another example, a patient's sleep quality can be assessed by determining the patient's activity level while the patient is awake. The patient's activity level during the day may provide important information about the quality of the patient's sleep. For example, if the patient is very inactive during the period of wakefulness, this may indicate that the patient's sleep is insufficient in terms of quality or duration. Such information may be used in conjunction with assessing the effectiveness of a particular sleep disorder treatment and / or adjusting the patient's sleep disorder treatment. Patient activity information can be sensed, for example, using an accelerometer and / or transthoracic impedance sensor disposed in or on the implantable device. The collection of patient activity information may be performed over a period of time. The assessment of patient activity data can indicate changes in the patient's health status as changes indicated by decreased activity levels.

  The sleep quality monitor 2320 may calculate one or more sleep quality metrics that quantify the patient's sleep quality. A typical set of sleep quality metrics includes, for example, sleep efficiency, sleep fragmentation, and number of awakenings per hour (represented by an awakening index (AI)).

  The sleep quality monitor 2320 also quantifies the patient's respiratory disturbances, such as the apnea hypopnea index (AHI), which shows the number of apneas and hypopneas per hour, and the percent breathing time (% PB) One or more metrics can also be calculated.

  In addition, metrics associated with sleep movement disorders can be determined by sleep quality monitor 2320. Such metrics include, for example, the general sleep movement disorder index (MDI), which represents the number of hourly abnormal movements resulting from movement disorders such as restless leg syndrome, periodic limb movement disorders and bruxism. May be. In addition, each type of movement disorder, for example, a bruxism index (BI) characterized by the number of times of mandibular movement, an RLS index (RLSI) characterized by the number of onset of restless leg syndrome, and patients For a PLM index (PLMI) characterized by the number of periodic limb movements experienced per hour, a specific index can be calculated.

  Also, the percentage of sleep time (% MD) during which the patient experiences movement disorders during that time can be calculated. Percentage of time during which a patient experiences bruxism (% B), Percentage of time that experiences restless leg syndrome (% RLS), and Percentage of time that a patient experiences periodic leg movement disorders (% PLMD) Specific metrics for can also be determined.

In addition, sleep summary metrics can be calculated directly from the collected patient condition data or by combining the sleep quality metrics and sleep disorder metrics described above. In one embodiment, a combined sleep disordered breathing metric (SDRM) can be calculated by combining the apnea hypopnea index AHI and the arousal index AI. The complex sleep breathing disorder metric (SDRM) can be calculated as a linear combination of AHI and AI as follows: That is,
SDRM = c ₁ * AHI + c ₂ * AI [6]
Where c ₁ and c ₂ are constants selected to balance the relative contributions of respiratory and wakefulness to sleep disturbances. AHI can be monitored by detecting respiratory disturbances based on the previously described transthoracic impedance measurements. AI can be estimated, for example, by monitoring patient activity indicating end of sleep or arousal, minute ventilation, and body behavior posture sensors. A more sensitive measurement of arousal can be made using the EEG signal. In this implementation, the constant c ₂ can be adjusted to reflect the increased sensitivity to arousal.

  In another embodiment, undisturbed respiratory sleep time (URST) or undisturbed respiratory sleep efficiency (URSE) can be calculated based on the amount of time a patient spends sleeping in bed without a respiratory disorder.

  URST or URSE metrics are determined using three parameters: total bedtime (TIB), total sleep time (TA), and combined sleep duration (STDR) in breathing disorders can do. Bedtime can be determined by a combination of body position sensing and sensing of the patient's approach to the bed. The patient's posture can be determined, for example, using an implantable multi-axis accelerometer sensor.

  The patient's total bedtime (TIB) can be determined using a proximity sensor to the bed. The bed proximity sensor may use a receiver in the sleep quality monitor 2320 to receive signals transmitted from a beacon 2370 located in the patient's bed 2360. If the bed proximity receiver detects a sufficiently strong signal from the bed proximity beacon 2370, the receiver detects that the patient is in bed 2360.

  The total sleeping time (TA) can be determined using the sleep detection method described in more detail above. The total sleep time (STDR) in a disordered breathing can be determined based on the detection of sleep and disordered breathing using, for example, the sleep and disordered breathing detection method described above.

The patient's undisturbed respiratory sleep time (URST) is calculated as follows: That is,
URST = TA − STDR [7]
Here, TA = total sleeping time, and STDR = sleeping time due to respiratory disorder.

Percentage undisturbed respiratory sleep efficiency (URSE) is calculated as follows: That is,
URSE = 100 * URST / TIB [8]
Where URST = no obstacle breathing sleep time and TIB = total bedtime.

  Similar metrics can be calculated for movement disorders in general or for specific movement disorders such as RLS, PLMD, or bruxism. For example, the composite RLS, PLMD, and brux metric, RLSM, PLMDM, and BM can be calculated using the following equations similar in form to Equation 6 above, respectively.

RLSM = c ₁ * RLSI + c ₂ * AI [9]
Here, RLSI = number of hourly development of restless leg movement syndrome, AI = hourly number of awakenings, and, c ₁ and c ₂ are fishing the relative contributions of the effects on sleep disorders abnormal movement and arousal A constant selected to match.

PLMDM = c ₁ * PLMDI + c ₂ * AI [10]
Here, the onset times hourly PLMDI = periodic leg movement syndrome, AI = hourly number of awakenings and,, c ₁ and c ₂ are fishing the relative contributions of the effects on sleep disorders abnormal movement and arousal A constant selected to match.

BM = c ₁ * BMI + c ₂ * AI [11]
Select Here, BMI = bruxism onset number of hourly movement, AI = hourly number of awakenings, and, c ₁ and c _2, in order to balance the relative contributions of the effects on sleep disorders abnormal movement and arousal Constant.

  The patient's undisturbed exercise sleep time (UMST) and unaffected exercise sleep efficiency (UMSE) can be calculated separately for each exercise-related disorder or in combination with equations 2 and 3 above. Can be used to calculate.

  The combined sleep disorder index SDI, which quantifies the combined effects of both respiratory and movement disorders, combines the apnea hypopnea index (AHI), the movement disorder index (MDI), and the arousal index (AI). Can be calculated.

Sleep disorders index (SDI) may be computed and AHI, the combined disorder index DI _C, as a linear combination. The combined disorder index may include both abnormal respiratory and motor components. For example, the sleep disorder index SDI is characterized by the following equation.

SDI = c ₄ * DI _C + c ₃ * AI [12]
Here, DI _C is a combination failure indicator in the form below.

DI _C = c ₄₁ * DI ₁ + c ₄₂ * DI ₂ [13]
In Equation 12, c ₄ and c ₃ are constants selected to balance the relative contributions of the combined impairment and wakefulness effects, respectively. The disorder index, DI _C , can be used to characterize the effects of one or more sleep disorders, including, for example, disordered breathing and / or disorders associated with abnormal movement. The combined disability index may represent only the disability index, or a linear combination of two or more sleep disorder indices, eg, apnea / hypopnea index (AHI) and abnormal movement disorder index (MDI) Can be. Constants c ₄₁ and c ₄₂ can be used as a weighting factor associated with a particular disorder indices.

  The patient's undisturbed sleep time (UST) can be calculated as follows:

UST = TA − STSD [14]
Here, TA = total sleeping time and STSD = sleeping time spent in sleep disorder.

  Non-disturbed sleep efficiency (USE) in percent can be calculated as follows:

USE = 100 * UST / TIB [15]
Here, UST = no obstacle sleep time and TIB = total bedtime.

  Sleep quality metrics, such as the metrics described above, or other metrics can be obtained and analyzed using sleep quality data collection and analysis unit 2320. Sleep quality metrics can be determined and stored or sent to another device on a regular basis, for example, daily, in addition to raw or processed data based on physiological and non-physiological conditions. it can. Such data can be provided to the patient's health care professional in real time or as a trend of daily measurements over a long period of time, eg, monthly or yearly.

Healthcare professionals can use automated or “on-demand” methods through implanted device programmer questions, through occasional or periodic trans-telephonic device questions, or in the context of advanced patient management systems. To access data during clinical visits. Healthcare professionals use sleep quality indicator trends alone or in combination with other devices or with clinical data to diagnose disorders and / or require patient devices or medical treatments Can be adjusted accordingly to improve patient sleep quality.

Sleep Detection Various embodiments of the present invention include the use of sleep information to adapt treatments for breathing disorders. A respiratory disorder treatment system such as the cardiac electrical treatment system shown in FIG. 2 may include a sleep detector 256. The sleep detector may include circuitry for detecting wakefulness from sleep, including sleep onset and sleep, sleep stages including REM and non-REM sleep stages, and autonomic wakefulness.

  The collection of information related to sleep involves discriminating between sleep states and wakefulness states. One method of detecting sleep includes comparing one or more detected physiological conditions to a threshold indicative of sleep. If the detected state matches the threshold value indicating sleep, the start of sleep is declared. For example, decreased patient activity is a condition associated with sleep. If the patient's activity falls below a predetermined threshold, the system declares the start of sleep. If the patient's activity increases above a threshold, the system declares the end of sleep. In a similar manner, multiple patient conditions such as heart rate, respiratory rate, electroencephalographic activity, etc. can be individually compared with thresholds or other indicators for detecting sleep, or can be compared together. .

  A method for detecting sleep according to an embodiment of the present invention includes adjusting a sleep threshold associated with a first patient condition using a second patient condition. The first patient condition is compared to the adjusted threshold to determine whether the patient is asleep or awake.

  FIG. 24 shows a sleep detector that may be used in conjunction with a treatment system for breathing disorders. Sleep detector 2436 uses a number of sensors 2401, 2402, 2403 to sense sleep-related patient conditions. A typical set of sleep-related conditions is, for example, patient activity, patient location, body position, heart rate, QT interval, eye movement, respiratory rate, transthoracic impedance, tidal volume, minute ventilation, brain activity , Muscle tone, body temperature, time of day, and blood oxygen level.

  According to the embodiment of the present invention, the first sleep-related state detected using the sleep detection sensor 2401 is compared with the sleep threshold value to detect the start and end of sleep. The second sleep related condition detected using the threshold adjustment sensor 2402 is used to adjust the sleep threshold. The example described here includes one sleep detection sensor 2401 and one threshold adjustment sensor 2402, but any number of thresholds or other indicators corresponding to multiple sleep detection sensors may be used. Furthermore, the threshold value or index | indication of several sleep detection signals can be adjusted using the state detected using arbitrary number of adjustment sensors. Additional sleep-related signals from one or more confirmation sensors 2403 can optionally be used to confirm the onset or termination of the sleep state.

  Signals from the sensors 2401, 2402, 2403 are received by an interface circuit 2431 that may include, for example, an amplifier for processing each sensor signal, a signal processing circuit, and / or an A / D conversion circuit. The interface circuit 2431 may further include a sensor drive circuit necessary for operating the sensors 2401, 2402, and 2403.

  The sleep detector 2436 adjusts the level of the first sleep-related state detected using the sleep detection sensor 2401 according to the second sleep-related state detected using the threshold adjustment sensor 2402. Configured to compare with. The sleep start or end of sleep can be determined by the sleep detector 2436 based on the comparison. The start or end of sleep can optionally be confirmed using the patient condition derived using sleep confirmation sensor 2403.

  FIG. 25 is a flowchart illustrating a method for detecting sleep according to an embodiment of the present invention. A sleep threshold associated with the first sleep-related patient condition is established 2505. The sleep threshold may be determined, for example, from clinical data on the sleep threshold acquired using a group of subjects. The sleep threshold may also be determined using historical data taken from the particular patient whose sleep state is to be detected.

  First and second sleep related conditions are detected 2510, 2520. The first and second sleep-related conditions are, for example, implanted in the patient, attached externally to the patient, or located remotely from the patient, as previously described with reference to FIG. It can be detected using a sensor. The first and second sleep-related states may include any state associated with sleep and are not limited to the representative sleep-related states listed above.

  The sleep threshold determined for the first sleep related state is adjusted using the second sleep related state 2530. For example, if the second sleep-related state shows a high level of activity that does not match the sleep state, the first sleep-related state is adjusted by adjusting the sleep threshold of the first sleep-related state downward. If a reduced level of is not sensed, the sleep state may not be detected.

  If the first sleep-related condition matches sleep with the adjusted sleep threshold 2540, sleep is detected 2550. If the first sleep-related condition does not match sleep using the adjusted sleep threshold, sleep is not detected 2560. After sleep is detected or not detected, the first and second sleep-related states are subsequently detected 2510, 2520 and the threshold is subsequently adjusted 2530 to further evaluate the sleep state. enable.

  The flowchart of FIG. 26 illustrates a method for detecting sleep using an accelerometer and a minute ventilation (MV) signal according to an embodiment of the present invention. In the method shown in FIG. 26, an accelerometer and a minute ventilation sensor are used to detect patient activity and patient respiratory status, respectively. A preliminary sleep threshold is determined 2610 for the patient activity state sensed by the accelerometer. The preliminary sleep threshold may be determined from clinical data from a group of subjects or from historical data taken from the patient over a period of time.

  Patient activity is monitored 2620 using an accelerometer that may be incorporated into an implantable cardiac rhythm management system, as described with reference to FIG. Alternatively, the accelerometer may be externally attached to the patient. The patient's MV status is monitored 2625 using, for example, a transthoracic impedance sensor. The transthoracic impedance sensor may be implemented as a component of an implantable CRM device.

  In this embodiment, patient activity represents a sleep detection state and is compared to a sleep threshold. The patient's MV is used as a threshold adjustment state for adjusting the sleep threshold. Also in this example, the patient's heart rate is monitored 2630 and used to obtain sleep confirmation status.

  The sleep threshold adjustment is performed using the patient's MV state for adjusting the active sleep threshold. If the patient's MV status is low relative to the expected MV level associated with sleep, the active sleep threshold is increased. Similarly, if the patient's MV level is higher than the expected MV level associated with sleep, the active sleep threshold is lowered. Thus, if the patient's MV level is high, a determination that the patient is asleep is not made unless the activity is lower. Conversely, if the patient's MV level is relatively low, sleep may be detected at a higher activity level. If two sleep-related states are used to determine a patient's sleep state, the accuracy of sleep detection will be enhanced compared to previous methods.

  Various signal processing techniques can be used to process the raw sensor signal. For example, a moving average of multiple samples of the sensor signal may be calculated. In addition, the sensor signal may be amplified, filtered, digitized, or otherwise processed.

  If the MV level is 2635 higher than the expected MV level associated with sleep, the active sleep threshold is lowered 2640. If the MV level is 2635 lower than the expected MV level associated with sleep, the active sleep threshold is increased 2645.

  If the patient's activity level is below or equal to the adjusted sleep threshold 2650, and if the patient is not currently in sleep 2665, to confirm that the patient is asleep, 2680 where the heart rate is checked. If the patient's heart rate is 2680 compatible with sleep, the start of sleep is determined 2690. If the patient's heart rate is incompatible with sleep, the patient's sleep-related condition is subsequently monitored.

  If the patient's activity level is less than or equal to the adjusted sleep threshold 2650, and if the patient is currently in sleep 2665, the continuing sleep state is determined 2675 and the patient's sleep The associated condition is subsequently monitored to cause sleep termination.

  If the patient's activity level is greater than the adjusted sleep threshold 2650 and the patient is not currently sleeping 2660, the patient's sleep-related condition is continuously monitored until 2690 when the onset of sleep is detected. The If the activity level is greater than the adjusted sleep threshold 2650 and the patient is currently sleeping 2660, the end of sleep is detected 2670.

  The graphs of FIGS. 27-30 show the adjustment of the active sleep threshold. The relationship between patient activity and accelerometer and MV signals is trended over a period of time to determine the relative signal levels associated with sleep. The graph of FIG. 27 shows patient activity as directed by the accelerometer. The graph of FIG. 28 shows the patient's heart rate (HR) and sensor indicated heart rate (SIR) during the same period. The accelerometer signal indicates a period of sleep associated with a relatively low level of activity that begins shortly before 23:00 and continues until 6:00. The patient's heart rate appropriately tracks the activity level indicated by the accelerometer showing a similar period of reduced heart rate corresponding to sleep. The accelerometer signal level during a known period of sleep may be used to establish a sleep detection threshold.

  FIG. 29 is a graph of patient minute ventilation signal versus time. Historical data of average minute ventilation is graphed to show the variation over a 24-hour period. MV data is shown on average from 1 to 8 months. The minute ventilation data may be used to determine a minute ventilation signal level associated with sleep. In this example, the composite minute ventilation graph using historical data is an approximately sinusoidal shape with a relatively low minute ventilation level occurring during a period of approximately 21:00 to 8:00. Presents. Reduced minute ventilation levels are associated with periods of sleep. The minute ventilation level associated with sleep is used to perform sleep threshold adjustment.

  FIG. 30 shows adjustment of the active sleep threshold using MV data. The initial sleep threshold 3010 is determined as described above using the acquired baseline activity data. If the patient's MV level is lower than the expected MV level associated with sleep, the active sleep threshold is increased 3020. If the patient's MV level is high relative to the expected MV level associated with sleep, the active sleep threshold is lowered 3030. If the patient's MV level is high, the activity detected by the accelerometer is not lower, and no determination is made that the patient is sleeping. However, if the patient's MV level is relatively low, sleep may be detected at a higher activity level. If two sleep-related signals are used to determine and adjust the patient's sleep threshold, the accuracy of sleep detection will be enhanced compared to previous methods.

  Additional sleep-related conditions may be sensed and used to improve the sleep detection method described above. For example, the posture sensor can be used to detect the patient's posture and can be used to confirm sleep. If the position sensor signal indicates an upright position, the position sensor signal can be used to override sleep determination using sleep detection and threshold adjustment states. Other states may be used in conjunction with sleep determination or confirmation that includes a representative set of sleep-related states shown above. In another example, a bed proximity sensor, alone or in combination with a position sensor, may be used to detect that a patient is lying and lying down and resting.

  The sleep detection system can detect sleep stages including sleep initiation, termination, awakening, and REM and non-REM sleep. REM sleep can be distinguished from NREM sleep, for example, by examining one or more signals indicative of REM (eg, muscle tone, rapid eye movement, or EEG signal). Various sensors that indicate sleep state are coupled to a sleep detection circuit via a wired or wireless connection, such as an electroencephalogram (EEG), electro-oculogram (EOG), or electromyogram (EMG) sensor. Can be detected. The sleep detection circuit can analyze various patient states sensed by the sensor and track the patient's sleep via various sleep states including REM and NREM stages.

Sleep Stage Detection Sleep and its various states have so far been associated with increased respiratory and cardiac disorders, especially in patients with cardiopulmonary disorders. For example, some epidemiological studies have focused on the peak incidence of sudden cardiac death around 5-6 am. One explanation for this peak suggests an association between the incidence of sudden death and the onset of rapid eye movement (REM) sleep, morning awakening or arousal. The mechanism of inducing lethal arrhythmia may be related to severe phasic sympathetic modulation of the cardiovascular system during REM status or morning awakening.

  Non-REM sleep may be associated with an increased likelihood of cardiac arrhythmias. Some patients may have nocturnal heart attacks associated with surges in vagal activity. When non-REM sleep is involved in these arrhythmias because it is associated with a “vagal predominance” state characterized by lower heart rate and low to high frequency power ratio There is also.

  Sleep may be associated with increased respiratory destruction. Disease variation, drug application, etiology, and phenotype can all contribute to the patient's propensity for sleep to cardiac or respiratory disorders. Sleep stage classification can be used to provide more effective treatment, better diagnostic information, and improved prognostic and preventive treatment functions. Sleep stage classification in harmony with treatment may result in improved treatment management for both heart and respiratory conditions as described above. Tracking physiological changes during sleep may provide a mechanism for improved diagnosis of sleep-related disorders.

  Diagnostic tests or therapeutic device tests can be advantageously performed during sleep or during specific sleep stages. Diagnostic tests may include, for example, assessing the patient's autonomic integrity during sleep and possible use of REM onset as a surrogate for stress testing. Performing diagnostic procedures during sleep recognizes the opportunity afforded by sleep or a specific sleep stage to routinely disrupt the cardiovascular system under controlled conditions to assess the patient's autonomic response It is to be.

  Treatment device tests such as AVI search, capture threshold, and heart template acquisition can also be performed during sleep. Sleep tests such therapeutic devices while patient activity is low, thus providing a period of time during which a more effective and consistent test state occurs.

  Various embodiments of the present invention include sensing a physiological state associated with REM sleep and using the REM state to classify a patient's sleep stage. The REM modulation state represents a group of physiological states that change during REM sleep and can be used to distinguish between REM sleep periods and non-REM periods. As its name suggests, REM sleep is characterized by a rapid burst of eye movement, intense brain activity, and a general state of tensionlessness, or skeletal muscle paralysis.

  Various embodiments of the present invention utilize a significant decrease in skeletal muscle tone during REM to produce a REM modulated signal. In this implementation, sensing the REM modulation signal includes sensing the patient's skeletal muscle tone. Other REM modulated signals can be used, for example, to detect REM sleep, including eye movements and brain wave activity. Typical sets of sensors that can be used to sense REM modulated signals include, for example, electroencephalogram (EEG) electrodes for detecting brain activity, electro-oculogram (EOG) sensors for detecting eye movements, Sensors (including electromyogram (EMG) sensors), strain gauge sensors, piezoelectric sensors, mechanical force sensors, or other transducers for detecting muscle tension are included.

  Sensing conditions associated with REM sleep can be used to distinguish between REM sleep periods and non-REM periods. Sleep stage classification is further enhanced by detecting a state associated with the patient's sleep wakefulness, ie, a state associated with the sleep wakefulness that indicates whether the patient is asleep or awake. can do.

  According to an embodiment of the present invention, the sleep stage classification method includes sensing at least one state modulated by a patient's sleep-wake state and a sleep-related state including a REM modulation state. The state modulated by the patient's sleep wake state represents a state that can be used to distinguish between sleep periods and wake or wake periods. The discrimination between the sleep period and the awakening period can be performed, for example, by sensing patient activity. According to this method, if the activity level of the patient is relatively low (for example, below the sleep threshold), the patient is determined to be asleep. The level of patient activity can be detected using, for example, an accelerometer, a heart rate sensor, a respiratory minute ventilation (MV) sensor, or other type of sensor.

  Information derived from the REM modulation state may be used in combination with information related to the patient's sleep / wake state. This approach can be used, for example, to determine the onset and cessation of sleep, the duration and extent of arousal from sleep, and classify sleep stages including REM and non-REM states.

  According to embodiments of the present invention, the sleep stage classification processor receives the output of one or more sensors configured to sense signals associated with sleep related conditions. The sleep stage processor can perform sleep stage classification in a real-time manner, or previously acquired in batch mode to retrospectively classify sleep stages of one or more sleep periods, and Stored sensor data can be processed.

  Sleep stage classification may include an adaptive way in which the sleep stage processor learns the patient's physiological responses at various sleep stages. The learned response can be used to enhance the accuracy and / or sensitivity of sleep stage classification. Adaptive sleep stage classification is the monitoring of changes in one or more physiological signals over a period of time and determining sleep onset, sleep cessation, and various sleep stages to determine the sleep-related signal drift or It may include adjustment of the threshold used to adapt to other changes.

  In one configuration, one or more of the sensors used to detect a sleep-related condition, eg, a REM modulation condition, and / or a condition associated with the patient's sleep-wake state is implantable. Alternatively, an implantable component may be utilized. In another configuration, the sleep stage processor may be partially or fully implantable. In other configurations, both the sensor and the sleep stage processor may be implantable or may use implantable components.

  As mentioned above, sleep stage classification may be useful in coordinating sleep stage informed therapy delivery for treating various disorders and for performing sleep stage informed testing and monitoring. is there. In one example implementation, cardiac therapy can be triggered during specific sleep stages to reduce the likelihood of cardiac arrhythmias during fragile sleep periods. In a similar manner, sleep stage classification can be used to trigger a disordered breathing treatment to prevent or reduce the onset of sleep / breathing disorders.

  The flow graph of FIG. 50A illustrates a method for classifying sleep stages according to an embodiment of the present invention. REM modulation status (eg, brain activity, eye movement, and / or muscle tone) is detected 5010 and used to classify 5020 the patient's sleep stage. Using the detected REM modulation state, the system can determine, for example, that the patient is in the REM sleep phase or in a non-REM period.

  The flow graph of FIG. 50B illustrates another method for classifying sleep stages according to an embodiment of the present invention. The method includes detection of a sleep-related condition that includes at least one REM modulation state 5030 and at least one condition 5040 associated with the patient's sleep-wake state. Sleep stage classification is performed 5050 based on the detected condition.

  FIG. 51 shows a block diagram of a system 5100 suitable for performing a sleep stage classification method according to an embodiment of the present invention. Sleep stage classification system 5100 may include one or more sensors 5130 that are used to sense physiological signals associated with the patient's sleep-wake state. In one example implementation, sleep wake sensor 5130 may be responsive to patient activity. If patient activity falls below a threshold, the patient should be considered asleep. If patient activity rises above the activity threshold, the patient should be considered awake. Other methods of detecting whether the patient is sleeping or awake are possible.

  The system further includes a REM sensor 5120 that is sensitive to a REM modulated physiological condition. The detection of REM sleep may be performed, for example, by comparing the output of the skeletal muscle tone sensor with a threshold value. If the REM sensor output indicates a decrease in muscle tone that matches the REM sleep threshold, the patient is determined to be in REM sleep. Sleep wake sensor 5130 and REM sensor 5120 may optionally be used in conjunction with additional sensors used to detect additional sleep related conditions. Additional sleep-related conditions may be used to enhance the accuracy, sensitivity, or other functional capabilities of the sleep stage classification system 5100. For example, a patient position or torso orientation sensor may be used in combination with a patient activity sensor to provide enhanced detection of the patient's sleep-wake state. If patient activity is low (this is indicated by the output of the patient activity sensor) and the patient is lying down and resting (this is indicated by the output of the torso orientation sensor) The combination may enable more accurate detection of sleep onset.

  Coupled to the sleep / sleep stage detector 5110 that detects and processes the sensor output is a REM sensor 5120, a sleep wakefulness sensor 5130, and any additional sensors. The sleep / sleep stage detector 5110 uses the outputs from the sleep wake sensor 5130 and REM sensor 5120 to determine, for example, whether the patient is awake or sleeping, and the wake from sleep is sustained. Time and degree can be determined, sleep stages including REM and non-REM states can be classified, and the duration of various sleep stages can be determined.

  In one embodiment, one or both of the REM sensor 5120 and sleep / wake sensor 5130 are located external to the patient and the sleep / sleep stage detector 5110 is implantable or is an implantable configuration. Contains elements. In another embodiment, one or both of the REM sensor 5120 and the sleep wake sensor 5130 are implantable in whole or in part, and the sleep / sleep stage detector 5110 is located outside the patient. Has been. In yet another embodiment, the REM sensor 5120, sleep / wake sensor 5130, and sleep / sleep stage detector 5110 all include implantable components or are entirely implantable.

  The components of the sleep stage classification system 5100 can use wireless communication. For example, the REM sensor 5120 and the sleep / wake sensor 5130 can be coupled to the sleep / sleep stage detector 5110 using a wireless communication link. In one example, some or all of the sensors 5120, 5130 use a remote communication function such as a wireless proprietary or wireless Bluetooth communication link.

  The sleep / sleep stage detector 5110 can classify sleep stages adaptively by connecting to various sleep stages and learning patient responses. In one example, the sleep / sleep stage detector 5110 may perform sleep stage classification by comparing sensor signal levels to a predetermined threshold. The initial threshold can be determined using clinical data from a group of individuals or using patient specific data. After the initial threshold is established, the sleep / sleep stage detector 5110 updates the threshold to make it more sensitive and / or more accurate based on data obtained from the patient over time. The sleep stage can be classified. The sleep stage threshold can be updated with a recent history of sensor output levels associated with a particular sleep stage. This process may include collecting data over a period of time to determine a patient's sleep pattern and adjusting a threshold based on the sleep pattern. Through this process, an initially established threshold (eg, sleep start threshold for accelerometer output or REM sleep threshold for EMG sensor output) is As additional data is acquired, it can be modified.

  FIG. 52 is a block diagram illustrating a sleep stage discrimination circuit 5200 that utilizes the sleep stage classification system implemented in accordance with an embodiment of the present invention. The sleep stage discrimination circuit 5200 can be used, for example, to perform sleep stage informed diagnostic monitoring and / or diagnostic tests to assess the function of the patient's physiology. Such diagnostic monitoring or testing may include one or more physiological systems, including, for example, the heart and respiratory system. Additionally or alternatively, sleep stage discrimination circuit 5200 provides the patient with sleep stage informed therapy (eg, cardiac rhythm therapy, respiratory therapy, or other types of therapy enhanced by sleep stage classification). Can be used to Further, the sleep stage discrimination circuit 5200 can be used to test sleep stage informed therapy devices. The use of sleep stage discrimination circuit 5200 may be purely or primarily diagnostic, purely or primarily therapeutic, or a combination of therapeutic and diagnostic actions. May contain.

  Sleep stage discrimination circuit 5200 includes a medical device 5201 coupled to various sensors 5205, 5210, 5215. Sensors 5205, 5210, 5215 provide physiological information used in connection with sleep stage classification and therapeutic and / or diagnostic actions performed by medical device 5201. The sleep stage sensor 5205 includes a sensor that can detect a REM modulation state such as skeletal muscle tension. Additional sleep stage sensors can also be used, including one or more sensors (eg, patient activity sensors) that indicate the patient's sleep wakefulness.

  The medical device 5201 may be coupled to sensors 5210, 5215 configured to detect one or more aspects of the patient's physiology including, for example, the patient's heart and / or respiratory function. In various configurations, the medical system 5200 can monitor, test, or perform treatment on a patient, including cardiac and / or respiratory therapy. In one implementation, a cardiac sensor 5215 (eg, a cardiac electrode) may be used to sense cardiac electrical activity. The cardiac system sensor may include, for example, an internal or external cardiac electrode that is electrically coupled to the patient's cardiac tissue.

  The medical device 5201 can be coupled to one or more respiratory system sensors 5210 that can detect conditions associated with the patient's respiratory function. In one embodiment, the patient's respiratory function can be monitored using a transthoracic impedance sensor. Transthoracic impedance tracks the patient's respiratory effort that increases during respiratory inspiration and decreases during respiratory exhalation. The transthoracic impedance signal can be used, for example, to determine a patient's respiratory tidal volume (TV), minute ventilation (MV), and / or other respiratory parameters. Sensors other than or in addition to the heart and respiratory system sensors described herein can be used to detect a patient's heart and / or respiratory function.

  Sleep / sleep stage detector 5220 uses information from sleep stage sensor 5205 to determine a patient's sleep state, including, for example, sleep onset, termination, REM and non-REM states. The information generated by the sleep / sleep stage detector 5220 can be used by other components of the medical device 5201 to perform therapy, testing, and / or monitoring coordinated with the patient's sleep stage.

  Sleep stage information may be provided to a therapy module 5230 coupled to sleep / sleep stage detector 5220. The therapy module 5230 controls the delivery of sleep stage informed therapy to the patient. For example, the cardiac therapy can be coordinated to use the sleep stage classification information to provide cardiac arrhythmia therapy during REM or other proarrhythmic sleep periods. Sleep stage classification can also be used, for example, in connection with the delivery of sleep informed therapy to prevent or reduce the development of breathing disorders while the patient is asleep. Other types of treatment can also be enhanced using sleep stage classification.

  Sleep / sleep stage detector 5220 collects historical data obtained from sleep stage sensor 5205, respiratory system sensor 5210, cardiac system sensor 5215, and / or other components of medical device 5201, and Can be coupled to a monitoring unit 5250 configured to store. Monitoring unit 5250 can track one or more patient conditions and provide data used in the analysis of various physiological processes. The monitoring module 5250 can collect data useful in assessing various physiological trends. Data trending can be used to identify sleep stage classifications in combination with gradual changes in the patient's physiological state, particularly those that change with sleep or with specific sleep stages.

  The test module 5240 may be implemented within the medical device 5201 to control diagnostic tests and / or control device tests to maintain or improve the operation of the medical device 5201. Information from sleep / sleep stage detector 5220 ensures that diagnostic tests and / or device tests are adequately matched to the patient's sleep or wakefulness, or properly matched to a particular state of sleep Used by test module 5240.

  Diagnostic tests can be used to investigate the work of one or more of the patient's physiology. A diagnostic test may include changing one or more parameters of a patient's treatment (eg, cardiac rhythm treatment) and assessing the impact of the change on the patient. For example, the patient's treatment regime may be changed during sleep or during a particular sleep stage to determine the impact of the change on the patient.

  Diagnostic test methodologies may use sleep stage classification to determine the general behavior of a patient's physiological response in connection with various sleep stages. Such a process may include determining the patient's intrinsic response to normal variations in physiological processes. A patient's induced physiological response to device-based stimulation may also be determined.

  In one implementation of a sleep-coordinated diagnostic test, non-REM sleep can provide an opportunity to run an automated or physician-activated diagnostic test under relatively controlled conditions. The medical device 5201 can perform a diagnostic test during non-REM sleep when patient activity is low. In one configuration, the medical device 5201 can modify or perform certain cardiac pacing measures during non-REM periods to determine the impact of such modifications on the patient's heart system.

  In addition to diagnostic tests, various device test procedures can be preferably performed while patient activity is low, such as during non-REM sleep. For example, a medical device 5201 providing cardiac rhythm management therapy can perform device tests during non-REM sleep states to improve or modify pacing measures. In one implementation, the pacemaker can perform tests during non-REM sleep to optimize pacing escape intervals such as dual chamber or dual chamber AV delay. In another example, the pacemaker can adjust the pacing energy level based on a capture threshold test performed during non-REM sleep. In yet another embodiment, the cardiac rhythm management system uses a non-REM sleep as a convenient period of low patient activity to generate a morphological template of the cardiac waveform used to identify various cardiac arrhythmias. Can be acquired or updated.

  The flow graph of FIG. 53 illustrates a method for performing sleep stage classification according to an embodiment of the present invention. The method includes detecting 5310 at least one REM modulated signal (e.g., a signal modulated by muscle tone). If the REM modulated signal matches a predetermined REM sleep threshold 5320, the system determines if the REM sleep start is a previously declared 5330. If REM sleep onset was not previously declared 5330, the system declares 5350 REM sleep onset.

  If the REM modulation signal does not match the predetermined REM sleep threshold 5320 and REM sleep start was previously declared 5360, REM sleep stop is declared 5370. If the REM modulation signal does not match the REM sleep threshold 5320 and the REM start was not previously declared 5360, the system will continue to sense the REM modulation signal to detect the start of REM sleep 5310.

  The flow graph of FIG. 54 illustrates a method of using a sleep wake state in combination with a REM modulation state for classifying sleep stages according to an embodiment of the present invention. According to this implementation, the system determines sleep onset and sleep stop by comparing the patient activity signal to a threshold. Various methods of sleep onset and sleep stop detection (eg, the methodology described herein) may be used in conjunction with the sleep stage classification method of the present invention.

  The method shown in FIG. 54 includes determining the start and stop of REM sleep using the REM modulation signal. The REM sleep period can be classified as the interval between the start and stop of REM. The non-REM sleep period can be classified as the interval between the start and stop of sleep that is not classified as REM sleep.

  A signal related to the patient's activity level (eg, an accelerometer signal) is detected 5405 and compared 5410 to a predetermined sleep threshold. If the patient's activity level is 5410 consistent with the sleep threshold, and if sleep onset was previously declared 5415, the system detects 5425 the REM modulated signal. If the patient's activity level did not match the sleep threshold 5410 and if sleep onset was previously declared 5445, sleep cessation is declared 5450. If the patient's activity level matches the sleep threshold 5410, and if sleep onset has not been previously declared 5415, the system declares sleep onset 5420 and senses the REM modulated signal 5425.

  If the REM modulation signal level matches the REM sleep threshold 5430 and REM sleep start 5435 has not been previously declared, REM sleep start is declared 5440. If the REM modulation signal level does not match the REM sleep threshold 5430 and REM start was previously declared 5455, REM sleep stop is declared 5460.

  Using the method shown in FIG. 54, sleep start, stop, REM, and non-REM sleep can be detected. REM and / or non-REM sleep periods can be advantageously combined with a number of diagnostic and therapeutic operations, as described above. FIG. 55 is a process flow diagram illustrating a process for using sleep stage classification in cooperation with therapy delivery and testing, according to an embodiment of the present invention.

  As shown in the process flow diagram of FIG. 55, the system detects 5560 the heart signal and analyzes 5550 the heart signal in a beat-to-beat manner. Beat-to-beat cardiac signal analysis 5550 can be used to perform arrhythmia detection 5565, for example, based on velocity and / or morphological analysis techniques. Depending on the type of arrhythmia detected (if any), an appropriate treatment 5575 can be delivered to the heart. In one implementation, bradycardia pacing therapy can be delivered to the heart to maintain the patient's rhythm at a hemodynamically sufficient rate. In other examples, various stages of tachyarrhythmia therapy including, for example, anti-tachycardia pacing, cardioversion, and / or defibrillation to treat detected cardiac tachyarrhythmia May be available.

  The exemplary system utilizes REM modulation and sleep / wake state signal 5505 to perform sleep stage classification 5510. Sleep stage classification 5510 can be used in conjunction with heart-to-beat heart signal analysis 5550 to perform sleep stage informed arrhythmia analysis 5555 and thus enhance delivery of cardiac arrhythmia therapy 5575 . In one example, bradycardia pacing therapy can be enhanced by the ability to switch to a lower pacing rate if it is determined that the patient is asleep. Such a procedure may be advantageous for both purposes, for example, to increase the lifetime of the device and reduce stress on the heart. In a further example, prophylactic arrhythmia therapy 5575 can be delivered during sleep or based on prediction of future arrhythmia events, eg, upon detection of proarrhythmic sleep stage 5565. In one example, prophylactic arrhythmia therapy can be delivered to prevent tachyarrhythmias that are known to occur more frequently during REM sleep or during arousal from sleep.

  The classification of the sleep stage may be used in combination with a treatment for terminating or preventing sleep / breathing disorders. Various therapies include, for example, maintaining continuous positive air pressure to prevent tissue collapse into the respiratory passageway, electrical stimulation of nerves or muscles, and cardiac pacing therapy to treat sleep / breathing disorders You can do it. Because breathing disorders are more likely to occur when a patient is sleeping, breathing disorder detection or prediction 5532 may be enhanced by using sleep stage informed breath analysis 5525 according to embodiments of the present invention. it can.

  Respiratory disturbances can be detected by detecting a respiratory signal 5530 representing the patient's respiratory cycle and analyzing each breath 5520. In one implementation, disordered breathing including, for example, hypopnea and apnea can be detected by monitoring the respiratory waveform output produced by the transthoracic impedance sensor 5532.

  A hypopnea event is declared if the tidal volume (TV) of the patient's breath (indicated by the transthoracic impedance signal) falls below the hypopnea threshold. For example, a hypopnea event may be declared when a patient's tidal volume falls below about 50% of a recent average tidal volume or other baseline tidal volume value. An apnea event is declared if the patient's tidal volume further falls to an apnea threshold (eg, about 10% of the recent average tidal volume or other baseline value).

  Another method for detecting breathing disorders is to analyze a patient's breathing pattern. According to this method, a patient's breathing cycle is divided into several periods, including periods of inspiration, expiration, and non-breathing. Inspiratory, expiratory and non-breathing breathing periods are analyzed for patterns consistent with various breathing disorders.

  As described herein, sleep / breathing disorders can be predicted based on a number of patient conditions that increase the likelihood of a disordered breathing. Conditions that cause the patient to suffer from breathing problems include, for example, air pollution, alcohol use, and pulmonary congestion, among other conditions. In addition to the more etiological conditions that are more likely to cause breathing problems, various precursor states can be used to determine that the onset of breathing problems is imminent. For example, blood chemistry, hyperventilation, and regular periodicity of previous onset of breathing disorders can be used to predict the impending onset of breathing disorders. If a disordered breathing is detected or predicted 5532, appropriate treatment 5534 can be given to terminate or prevent the disordered breathing.

  Sleep stage classification 5510 can also be used to identify preferred time periods for performing 5585 various test procedures including, for example, diagnostic tests and / or therapeutic device parameter tests. In various implementations, the sleep stage informed diagnostic test may be capable of testing to assess the patient's autonomic integrity. Sleep stage classification may also allow the use of REM onset as a surrogate for stress testing and may recognize opportunities to routinely disrupt the cardiovascular system under controlled conditions. is there.

  Sleep stage classification also provides an opportunity to test one or more parameters of the treatment device while patient activity is low. Such tests may include, for example, a cardiac pacing device capture threshold test and acquisition of a cardiac signal morphology template to be used in conjunction with cardiac arrhythmia detection. Thus, sleep stage classification can be used to obtain more effective treatments, better diagnostic information, and improved diagnostic and predictive functions.

  FIG. 56 illustrates a medical system that can be used to perform sleep stage informed therapy according to embodiments of the present invention. The block diagram of FIG. 56 shows a medical system 5600 divided into functional blocks. Those skilled in the art will appreciate that there are many possible configurations that can and can implement these functional blocks. The example shown in FIG. 56 is one possible functional configuration.

  FIG. 56 shows an implantable cardiac pulse generator 5601 housed within the housing 5690 and configured to provide treatment for cardiac arrhythmias. Various cardiac electrical therapies, including treatment for respiratory disorders, and cardiac rhythm therapy including bradycardia pacing, anti-tachycardia pacing, defibrillation, and cardioversion, and the classification of sleep stages according to embodiments of the invention Can be realized in cooperation.

  As an option, the medical device 5600 may be configured to detect a respiratory disorder (eg, sleep / respiratory disorder) and to perform a treatment to alleviate the respiratory disorder. Respiratory disorder therapies, including cardiac pacing and / or other types of respiratory disorder therapies such as continuous positive air pressure (CPAP), neural stimulation, muscle stimulation or other therapies to treat respiratory disorders, It can be controlled or provided by the components of the cardiac pulse generator 5601.

  FIG. 56 shows a sleep stage classification system implemented with a cardiac pulse generator 5601, but it will be appreciated that the configurations, features, and combinations of features described in this disclosure can be implemented with multiple medical devices. Sleep stage classification can be performed in connection with various diagnostic and therapeutic devices, and such embodiments and features are not limited to the specific devices described herein.

  Further, while various embodiments include a device or system having an implantable control system and an implantable sensor, a treatment or diagnostic system that utilizes the sleep stage classification methodology of the present invention is a control system or control. It can be seen that the components of the system can be configured to be placed externally to the patient. Sensors and control systems (particularly patient sleep stage classification systems) may include patient-external components, patient-internal components, or a combination of patient-external and patient-internal components.

  In the embodiment shown in FIG. 56, implantable pulse generator 5601 includes circuitry for performing cardiac rhythm therapy 5642 to treat various arrhythmia conditions. Cardiac arrhythmia therapy is performed by detecting electrical signals produced by the heart, analyzing the arrhythmia signal, and providing appropriate therapy to terminate or reduce the arrhythmia. The pulse generator 5601 is coupled to a cardiac lead system having sensing and treatment electrodes 5650, 5622 electrically coupled to the patient's heart. The sensing and treatment electrodes 5650, 5622 of the cardiac lead system may include one or more electrodes disposed within or around the heart and one or more electrodes disposed on the housing 5690 or header of the pulse generator 5601. More electrodes may be included. In one configuration, the electrodes used for sensing are also used for therapeutic delivery. In another configuration, a set of therapeutic electrodes that are different from the sensing electrodes are used.

  The cardiac signal sensed by sensing electrode 5650 of the cardiac lead system is coupled to an arrhythmia analysis unit 5656 configured to identify cardiac arrhythmias. Arrhythmia analysis unit 5656 uses sleep stage processor 5562 information to perform sleep stage informed arrhythmia detection. If a cardiac arrhythmia is detected, the therapy unit 5642 can perform a number of therapies to treat the detected arrhythmia.

  Cardiac therapies may include pacing therapies that are controlled to treat too late heart rhythms. In this situation, the treatment unit 5642 controls the delivery of periodic low energy pacing pulses to one or more heart chambers via the pacing electrodes of the cardiac lead system 5650. The pacing pulse ensures that the periodic contraction of the heart is maintained at a hemodynamically sufficient rate.

  Cardiac treatment may include treatment to terminate tachyarrhythmia if the heart rhythm is too fast. The arrhythmia analysis unit 5656 detects and treats the occurrence of tachyarrhythmias including tachycardia and / or fibrillation. The arrhythmia analysis unit 5656 recognizes a cardiac rhythm that matches various tachyarrhythmias. If a tachyarrhythmia is identified, the treatment unit 5622 can terminate the arrhythmia by delivering high energy electrical stimulation to the heart via the defibrillation electrode of the cardiac lead system 5650.

  Appropriate cardiac therapy implementations can be enhanced using sleep stage classification determined by sleep stage processor 5562 according to embodiments of the present invention. As described above, sleep stage classification can be used to determine the optimal arrhythmia treatment. In one example implementation, cardiac therapy can be triggered by a signal from sleep stage processor 5562 to prevent cardiac arrhythmia during a REM or other proarrhythmic sleep period. In another example, the lower rate limit for bradycardia pacing measures can be modified if sleep stage processor 5562 indicates that the patient is sleeping.

  Sleep stage processor 5562 performs sleep stage classification based on one or more sleep-related signals including at least one REM modulated signal. In the exemplary embodiment of FIG. 56, muscle in tension sensor 5648 (eg, an EMG sensor) provides a REM modulated signal to sleep stage processor 5562. Additionally, signals responsive to patient activity can be used in combination with REM modulated signals to enhance sleep stage classification. In the example implementation shown in FIG. 56, the patient activity signal is provided by an accelerometer 5646.

  The medical device 5600 may optionally include components for performing respiratory system analysis 5654. In one embodiment, knowledge of the patient's sleep stage is suitable for analyzing the patient's breathing pattern to mitigate the detected onset of breathing disorders or to prevent the occurrence of sleep / breathing disorders. The right treatment may be determined.

  Transthoracic impedance sensor 5644 may be implemented to produce a signal representative of the patient's respiratory cycle. The respiratory analysis unit 5654 uses the sleep stage information provided by the sleep stage processor 5562 when analyzing the patient's breathing pattern to detect the onset of sleep / breathing disorders. Cardiac electrical stimulation therapy is based on sleep stage classification, respiratory analysis, and optionally heart system analysis, to alleviate or prevent respiratory disorders, including sleep apnea, hypopnea, or other forms of respiratory disorder May be delivered to the patient via cardiac electrode 5622. According to one embodiment, prophylactic treatment for breathing disorders may be initiated when the sleep stage classification processor indicates that the patient is asleep or upon detection of a particular sleep stage.

  57A-D illustrate muscles mechanically coupled to an implanted medical device 5700, such as an implantable pacemaker or an implantable cardioverter / defibrillator, according to embodiments of the present invention. Various configurations of tensionless sensors are shown. The implantable medical device 5700 may include a housing 5720 that houses the medical device circuitry and a header 5710 for coupling the lead system 5760 to the medical device 5700 circuitry.

The myotonic sensor can be implemented, for example, using an electromyogram (EMG) electrode 5730 or force responsive sensor 5740 disposed on the housing 5720 of the medical device 5700, as shown in FIGS. 57A and 57B, respectively. It's okay. FIG. 57C shows a muscle tension sensor 5750 disposed on the header 5710 of the medical device 5700. FIG. Alternatively, a muscle tension sensor 5770, such as an EMG electrode or strain gauge, may be placed on the lead system 5760 or via a catheter or lead system 5760 as shown in FIG. 57D. May be combined with 5700.

Detection of Autonomic Arousal The cardiac stimulation therapy system of FIG. 2 may include a sleep quality monitor 254. Sleep quality assessment may include detecting and assessing waking from sleep, the development of sleep-disordered breathing, nighttime limb movements, and the work of the heart, breathing, muscle, and nervous system . Sleep quality monitoring can provide important information related to various sleep disorders and can be used to tailor the therapy used to treat sleep disorders.

  On the surface, sleep can be viewed as an integrated phenomenon characterized by periods of loss of consciousness. When examined in more detail, a sleep period can be said to include a series of phenomena or stages. For example, sleep is generally divided into various stages of sleep, including rapid eye movement (REM) sleep and non-REM (NREM) sleep. Non-REM sleep is further subdivided into, for example, stage 1, stage 2 and stage 3 non-REM sleep.

  One indicator of sleep quality is the number of wakefulness experienced during sleep. Arousal is a phenomenon that occurs during sleep and can be identified based on changes in EEG signals during non-REM sleep and changes in EEG and EMG signals during REM sleep. The arousal phenomenon may or may not result in arousal. Patients may experience wakefulness during sleep and may not wake up.

  In one implementation, awakening from sleep is, for example, a transition to a higher frequency during a specified time period of a patient's EEG signal during non-REM sleep, assuming sleep was previously detected. Have been identified on the basis. Arousal during REM sleep has been identified by EEG arousal as defined above, in addition to changes in electromyogram (EMG) signals or body movements. Arousal identified based on changes in the EEG signal involves activation of the patient's autonomic nervous system.

  Activation of the patient's autonomic nervous system during sleep can be used to identify an arousal phenomenon, referred to herein as an autonomic arousal phenomenon. Autonomic wakefulness can be identified by an autonomic wakefulness response that includes transient activation of the patient's autonomic nervous system. Autonomic wakefulness responses may or may not be detectable changes to the patient's EEG signal.

  Autonomic wakefulness is, inter alia, transient during sleep affecting autonomic physiological parameters such as heart rate, blood pressure, cardiac output, peripheral stenosis, sympathetic traffic, and arteriole size. Changes are included. For example, autonomic wakefulness can be detected based on changes in systolic blood pressure increase of about 4 mm Hg and / or heart rate increase of about 4 beats / minute. As previously mentioned, the autonomic wakefulness phenomenon may begin during sleep and may or may not become wakeful. Thus, a patient can experience a number of autonomic wakefulness events while asleep without achieving wakefulness. On the contrary, these autonomic arousal events destroy the patient's sleep and degrade the quality of sleep.

  Information about the autonomic arousal phenomenon can be stored in memory and / or transmitted to a separate printing or display device. Information about autonomic wakefulness to diagnose sleep disorders and / or adjust patient therapy such as cardiac stimulation therapy, drug therapy, neural stimulation therapy, and / or respiratory therapy Can be used. Investigating trends in sleep information, including autonomic arousal phenomena, and correlating sleep information with sleep disorder phenomena, determine appropriate treatment for patients suffering from a range of sleep disorders, And may help to maintain.

  Many sleep disorder phenomena (eg, respiratory disorder phenomena and movement disorder phenomena) are accompanied by autonomic arousal phenomena. These autonomic arousals disrupt normal sleep patterns and may be involved in the development of chronic hypertension. Autonomic wakefulness responses may be visible in signals generated by electroencephalogram (EEG) sensors, electromyogram (EMG) sensors, and / or other sensors that are sensitive to changes in the autonomic nervous system.

  According to embodiments of the present invention, information related to the patient's autonomic arousal response can be collected and / or analyzed. Identification of autonomic arousal phenomenon includes detection and / or verification of sleep disorder phenomenon, trend survey of number of awakenings every night, and various indicators such as wakefulness index and / or composite index based on wakefulness and sleep disorder phenomenon Can be used for various purposes, including the creation of indicators. Awakening information can be collected and used when evaluating sleep and / or sleep disorders.

  Frequent wakefulness indicates a number of medical disorders, including sleep disorders such as sleep breathing disorders. Frequent waking of the sympathetic nervous system can lead to chronic hypertension or other medical problems. The ability to detect individual and / or aggregated arousals can be used in diagnosing various medical disorders, including respiratory disorders. If a patient is receiving treatment to treat a disorder of breathing, the ability to count and trend the number of arousals also provides information about the effectiveness of the treatment. For example, if the number of arousals decreases after the therapy is delivered, it can be assumed that the therapy provided an effective treatment for breathing problems. Furthermore, detection of arousal following treatment delivery can be used to provide feedback to treatment control.

  The methodology described here includes using arousal information in combination with respiratory disorder information. For example, the system may have a function of discriminating a respiratory disorder phenomenon that causes arousal and a respiratory disorder phenomenon that does not cause arousal. Detection of arousal may make it possible to investigate the tendency of arousal that occurs during sleep. The disorder of breathing with arousal is considered the most destructive. This is because the patient cannot take a quiet sleep due to repeated awakening. Some patients continue to experience breathing disorders during wakefulness. It may be desirable to ignore the disordered breathing phenomenon that occurs during wakefulness. The ability to detect wakefulness and ignore the subsequently detected dyspnea phenomenon during wakefulness may improve the accuracy of the disordered breathing index, eg, apnea / hypopnea index.

  The arousal detection system may include, for example, a sensor that generates a signal modulated by a change in muscle tone associated with autonomic arousal. Such a signal can be generated using, for example, an electromyogram sensor or a strain gauge placed in contact with or near a skeletal muscle such as a pectoral muscle. The sensor may be disposed on an implantable device, such as an implantable cardiac rhythm management system such as a pacemaker, defibrillator, heart monitor, cardiac resynchronizer, and the like.

  In addition to or instead of the muscle tone sensor, other sensors may be used in conjunction with wake detection. For example, an accelerometer may be used to detect patient motion correlated with arousal. An electrogram or other cardiac sensor may be used to detect various cardiac parameters associated with arousal. For example, heart rate increases upon awakening, AV delay decreases upon awakening, and heart rate variability is modified by changes in autonomic tone associated with arousal. Cardiac output increases during wakefulness, which can be measured via an impedance sensor. For example, blood pressure measured by a lead-based pressure gauge is modulated by wakefulness and can be used in detecting wakefulness. Peripheral artery tonography can be used for arousal detection. Arteriole size (which can be measured by photoplethysmography) decreases upon arousal due to activation of the sympathetic nervous system. Sympathetic traffic modulated by arousal can be sensed using a microelectrode coupled to an implantable device.

  According to one embodiment, the wakefulness detector includes circuitry configured to detect changes in the patient's nervous system. Changes may include sympathetic and / or parasympathetic nervous system changes. The wakefulness detector may be configured to detect the presence of individual wakefulness events, the presence of aggregated wakefulness, or the presence of both individual and aggregated wakefulness.

  For example, in one implementation, the sensor can sense a condition that is modulated contemporaneously with the arousal phenomenon. In this implementation, the system can detect individual wakefulness events, for example, during or slightly after the occurrence of wakefulness events. In another implementation, the sensor may be sensitive to conditions modulated by the aggregated effects of multiple wakefulness events that occur over a period of time. In such an implementation, detection of individual arousals may or may not be performed. The sensor can detect changes in physiological conditions caused by the occurrence of multiple arousals. The change in physiological state is used by the arousal detector to determine that multiple wakefulness events have occurred over a period of time. A typical set of states of multiple wakefulness that show an occurrence over a period of time may include, inter alia, heart rate variability, blood pressure, AV delay, arteriole size, and sympathetic activity. This list is not limiting and other conditions may be sensed by the system to determine the occurrence of multiple wakefulness events.

  The therapy assessment processor 260 (FIG. 2) may include functionality for evaluating wakefulness information and / or determining values or indicators using the wakefulness information. For example, the treatment assessment processor 260 can determine the number of arousals that occur within a sleep period or other specified time period. The treatment assessment processor 260 determines a wakefulness index (the number of wakefulness detected per unit time), apnea / hypopnea index (the number of apneas or hypopnea detected per unit time), or other index be able to. Further, the treatment assessment processor 260 evaluates the sleep disorder phenomenon to determine whether wakefulness is associated with the sleep disorder phenomenon. For example, if arousal is detected within a predetermined time period after the sleep disorder phenomenon is detected, the awakening may be associated with the sleep disorder phenomenon. This process can be used to discriminate between sleep arousals associated with sleep disturbance phenomena and sleep awakenings not associated with sleep disorder phenomena.

  In one application, for example, by counting and using the number of wakefulnesses, the wakefulness index can be calculated to quantify the number of wakefulness experienced by the patient per unit time. Awakening information can be used to determine a number of sleep quality indicators. The awakening information can be used when diagnosing and treating various disorders including nighttime sleep disorders such as sleep breathing disorders and other conditions. The ability to count and trend these wakefulness provides diagnostic information about the patient's condition for the disorder. For example, autonomic arousal is associated with causing hypertension. The presence of high blood pressure can be determined or predicted based on awakening information such as a tendency of arousal phenomenon over a certain period of time. Arousal trend surveys can be used to improve the therapies used to treat sleep disorders.

  Awakening disrupts sleep staging, leading to sleep disruption and, consequently, daytime sleepiness. Awakening takes the patient out of REM sleep or deep sleep (stages 3-4) and temporarily places the patient in the awake state. As a result, the amount of REM and deep sleep is limited because the patient must go back to stage 1-2 sleep before entering REM or deep sleep.

  In one configuration, the awakening information can be used by a therapy controller 265 (FIG. 2) disposed within the implantable therapy device to initiate, terminate, or adjust therapy. Alternatively, the awakening information can be sent to an APM system or other remote device for automated or physician-guided analysis. The APM system can send control signals to the implanted device to initiate, terminate or modify the therapy delivered by the implanted device. For example, awake feedback information can be used by an APM system, an implantable heart device to provide closed loop control of therapy using awake information feedback.

  In one implementation, detection of wakefulness includes an evaluation of the signal generated by a sensor for signature signatures of autonomic wakefulness. The graph of FIG. 31 shows the autonomic wakefulness response detected using the EEG sensor and the EMG sensor.

  Returning now to FIG. 31, the sleep study sensor array output is shown, including apneic events that result in arousal. Arousal detection can be performed using an implantable sensor that can detect changes in the sympathetic or parasympathetic nervous system. These changes can be either short-term (ie, changes associated with individual arousals) or long-term (ie, the aggregated effects of multiple arousals). Short-term effects of arousal include, for example, activation of sympathetic nerve activity. Sympathetic or parasympathetic changes, or changes in autonomic balance, for example, heart rate variability (HRV) (which senses heart activity, changes in heart rate, and / or changes in AV conduction) Can be detected using a device configured to do this).

  In the graph of FIG. 31, the horizontal axis of all graphs is the same time period during patient sleep analysis. The vertical axis of each graph represents the signal amplitude of each sensor. Traces 3181, 3182, 3183, and 3184 are the top, second, third, and fourth traces, respectively, plotted from sensors that are adapted to produce an electroencephalogram (EEG) signal. Although all four traces are obvious, traces 3181 and 3182 point out characteristic signatures of the EEG signal indicating arousal 3194. Trace 3185 is an electrocardiogram (EKG) of heart beats during the time period of the graph. Trace 3186 is an electromyogram that defines muscle movement during the time period of the graph. In trace 3186, the characteristic signature of the EMG signal indicating wakefulness 3192 is particularly evident.

  Traces 3187, 3188, 3191, and 3189 show various parameters related to respiration. Trace 3187 is nasal pressure, 3188 is chest effort, 3191 is abdominal effort, and 3189 is the sum of chest effort and abdominal effort. Trace 3193 represents the patient's blood oxygen saturation level. Lung activity can be sensed through the use of internal sensors, such as impedance sensors and / or minute ventilation sensors, further described below.

  According to aspects of the present invention, arousal detection may be used in conjunction with detection of sleep disorders such as respiratory disorders. Sleep disordered breathing may cause the patient to wake up from sleep frequently during the sleep period. Therefore, the sleep disorder phenomenon is followed by awakening from sleep. In one configuration, arousal detection can be used as a proxy for direct detection of fault conditions. For example, in systems that do not have a respiratory sensor that can detect respiratory disruption, wake detection can be used as a surrogate to detect respiratory disruption. Information from the wakefulness detector can be used to separate sleep disorder phenomena, such as apnea with wakefulness, hypopnea, from those that terminate without wakefulness. The sleep disturbance phenomenon accompanied by arousal is considered to be the most destructive. This is because these arousals interrupt the normal course of sleep and prevent the patient from undergoing a complete sleep cycle every night. The detection of these types of sleep disorder phenomena enhances the specificity of the detection of sleep disorder phenomena and guides diagnosis and / or treatment.

  Awakening information can be used to correct treatments for sleep disorder phenomena such as breathing disorders. In various implementations, the arousal information and / or the disordered breathing information can be used by the therapy controller 265 (FIG. 2) to modify the disordered breathing therapy delivered to the patient.

  For example, cardiac electrical therapy can be provided by an implanted therapeutic device. Respiratory disorder detection can be used to initiate cardiac electrotherapy. Detection of arousal indicating the end of the disordered breathing phenomenon can be used, for example, to terminate electrical stimulation therapy.

  In another example, cardiac electrotherapy can be performed and the number of arousals can be monitored. If cardiac electrotherapy results in an excessive number of wakefulness, the cardiac electrotherapy may be adjusted or terminated.

  In another example, the APM system can receive information about a sleep disorder phenomenon from the respiratory disorder detector / predictor 258 (FIG. 2) and / or receive wake information from the wake detector. Can do. The information can be automatically evaluated by the APM system or can be evaluated by the patient's physician. The APM system can be used to send control signals to the therapy controller 265 of the implanted device to initiate, terminate, or modify therapy delivered to the patient.

  In various configurations, the EMG sensor can be placed on the housing or header of an implantable device, such as a cardiac rhythm management device, or can be located on a catheter or lead coupled to the cardiac rhythm management device. An EMG sensor positioned on a device placed in the pectoral muscle region approaches skeletal muscle that can be used to detect arousal.

  FIG. 32 shows a flow diagram illustrating various optional processes that can be performed in connection with wake detection according to embodiments of the present invention. Sleep detection 3220 can be used to notify wake detection process 3210 and sleep disorder phenomenon detection process 3240. Information about sleep, sleep disturbances, and arousal from sleep is being monitored 3270. Information diagnoses sleep-related disorders and / or other disorders 3275, calculates wakefulness and sleep disorder indices, generates trend information 3280, correlates wakefulness and sleep disorder phenomenon 285, and / or Can be used to adjust the treatment delivered to the patient, 3290. Treatment to alleviate the sleep disorder phenomenon may be initiated 3250 upon detection of the sleep disorder phenomenon 3240, eg, sleep disordered breathing. Arousal detection 3210 may signal the end of the sleep disorder event and may terminate treatment following detection of arousal from sleep 3260.

  FIG. 33 illustrates a cardiac rhythm management (CRM) such as a pacemaker and / or cardioverter / defibrillator with functionality for delivering cardiac electrical stimulation to a respiratory disorder according to an embodiment of the present invention. ) Is a block diagram of an arousal detector implemented in cooperation with the system. The system may be partially or fully implantable.

  Cardiac sensing circuit 3342, cardiac electrical stimulation control and delivery circuit 3350, respiratory disturbance detector 3320, wakefulness detector 3360, and sleep detector 3330 are sealed and within the patient, such as in the pectoral muscle region of the patient's chest. Located in a housing suitable for implantation. An accelerometer 3333 configured to detect patient activity may also be incorporated within the housing. The EMG sensor is realized as an arousal sensor 3335, and is disposed on the housing so that the EMG sensor 3335 is placed in contact with or close to the skeletal muscle such as the pectoral muscle. . The intracardiac lead system includes a cardiac electrode 3341 for electrical coupling to the patient's heart and one or more transthoracic impedance electrodes 3342 for generating respiratory signals.

  The sleep detector 3330 uses the patient activity signal generated by the accelerometer 3333 and the respiratory signal generated by the transthoracic impedance electrode 3342 to determine whether the patient is sleeping or awake.

  The disordered breathing detector 3320 detects a disordered breathing event based on the patient's breathing pattern as described more fully above. The arousal detector 3360 compares the EMG signal with a characteristic arousal signature and detects arousal based on the comparison. Breathing disorder detection and wakefulness detection can be enhanced using sleep / wakefulness information provided by sleep detector 3330.

  In one embodiment, CRM provides cardiac electrical stimulation to one or more heart chambers as a treatment for breathing disorders. The cardiac electrical therapy control unit 3350 uses the signals from the sleep detector 3330, the respiratory disorder detector 3320, and the wakefulness detector 3360 to initiate, terminate, and / or perform cardiac electrical stimulation therapy for the respiratory disorder. Can be adjusted. For example, the therapy control unit 3350 may initiate a process for treating a respiratory disorder episode if the sleep detector 3330 determines that the patient is sleeping.

  In one scenario, the therapy control unit 3350 may initiate cardiac electrical stimulation (eg, cardiac overdrive pacing) to treat a respiratory disorder upon detection of a respiratory disorder phenomenon during sleep. In another scenario, the therapy control unit 3350 can initiate cardiac electrical stimulation to treat a respiratory disorder when sleep is detected. The therapy control unit 3350 can adjust the cardiac electrical stimulation when a disordered breathing event is detected during sleep. If arousal is detected, the therapy control unit 3350 can terminate or coordinate the cardiac electrical stimulation therapy for the respiratory disorder.

Adaptation of treatment for respiratory logbook breathing disorders may include monitoring symptoms, effects, and / or physiological changes associated with breathing disorders. The respiratory monitoring system 254 shown in FIG. 2 can be used to collect information related to respiratory impairment and / or respiratory quality and is useful in developing therapeutic measures to improve respiratory quality. There is a case.

  According to one embodiment, the respiratory monitor performs a phenomenon-based process for collecting and organizing medical information associated with respiratory phenomena. FIG. 43 is a flowchart illustrating a method including collection of medical information associated with a respiratory event according to an embodiment of the present invention. Information can be stored in a respiratory logbook accessible to the user, or respiratory events can be detected and / or predicted 4322. The phenomenon may include any detectable or predictable respiratory phenomenon such as disordered breathing (apnea, hypopnea, tachypnea), cough and / or other irregularities in respiratory rhythm. .

  In response to the detection or prediction 4322 of the respiratory event, collection 4324 of medical information for the respiratory event logbook entry is initiated 4324. Medical information can be collected 4326 during the event and / or during a time period close to the event. Information can be collected during a phenomenon, during a time period preceding the phenomenon, and / or during a time period following the phenomenon. In certain embodiments, information can be collected prior to the prediction or detection of a respiratory phenomenon.

  To facilitate the collection of medical information prior to the prediction or detection of respiratory events, the medical information can be monitored, for example, in a continuous or periodic manner and stored in a temporary buffer. can do. Temporary storage is necessary to obtain information, eg, start data, prior to predicting or detecting the phenomenon. The duration of temporary storage may vary depending on the breathing phenomenon that requires start data.

  Due to the various types of starting data conditions and the limited storage reality in the system, the system can provide different starting data lengths and different sampling rates for temporarily stored data. In a preferred embodiment, the system uses a circular buffer to store temporary data so that the newest data replaces the oldest data.

  Collection of respiratory information (which may include storing information in long-term memory) can be performed in a substantially continuous manner once initiated, or during a separate interval. Long-term data collection in a periodic manner may be advantageous when the phenomenon is relatively prolonged, such as in the case of a disease or disorder that may last several days or weeks. Various collection parameters such as the type of data collected, data collection frequency, and / or data collection interval may be selectable by the user. Further, the system may be programmable to use different data collection measures under different conditions over the course of the phenomenon. For example, the system can be programmed to collect data more frequently during sleep or, for example, during certain stages of disease progression. The system can be programmed to collect data, for example, in a substantially continuous manner during certain time intervals and periodically during other time intervals.

  The collection of medical information that precedes the respiratory phenomenon facilitates the identification of enhanced conditions that can be used to detect or predict the occurrence of future phenomena. For example, the acquisition of information that precedes the phenomenon affecting the patient's breathing allows the identification and assessment of the physiological condition that exists immediately before the phenomenon and leads to the phenomenon. In one scenario, the patient may experience a period of hyperventilation prior to an apnea event. Collection of respiratory information prior to an apnea event allows the identification of hyperventilation as a precursor condition. Identification of a precursor condition for apnea facilitates increased sensitivity and / or accuracy in detecting or predicting the future occurrence of apnea.

  Additionally or alternatively, medical information that precedes a respiratory event can provide insight into a condition that afflicts a patient with certain respiratory events. Acquisition of information that precedes the phenomenon may allow the identification of the trigger or cause of the phenomenon. For example, a disordered breathing phenomenon may follow the onset of snoring or a change in patient position. The collection of medical information that precedes the disordered breathing phenomenon allows the identification of factors that induce the disordered breathing phenomenon. Such information can be used to enhance the detection and / or prediction of future phenomena.

  Information gathered following the phenomenon can be used to assess the acute effects of the phenomenon. The development of respiratory disorders may be associated with acute physiological effects including, for example, negative pressure in the thoracic cavity, hypoxia, and arousal from sleep. Such an effect may be detectable for a period of time following the breathing event.

  For example, obstructive sleep apnea is typically terminated by sleep awakening that occurs a few seconds after the apnea peak, allowing airflow to resume. Simultaneously with arousal from sleep and for a period of time after the end of the phenomenon, sympathetic nerve activity, blood pressure, and heart rate surges occur.

  During an obstructive apnea event, efforts to generate airflow are increased. Inhalation attempts in the presence of an obstructed airway result in a rapid decrease in intrathoracic pressure. Repeated futile inspiratory efforts associated with obstructive sleep apnea may trigger a series of secondary reactions including mechanical, hemodynamic, chemical, neurological, and inflammatory reactions. Data collection following an obstructive sleep apnea event can be used to determine the presence and / or severity of a secondary response to the obstructive apnea event. Post-phenomenon information enhances the ability to assess the impact of secondary reactions on the patient.

As explained previously, the obstructive sleep apnea phenomenon is generally terminated by arousal from sleep. However, arousal is generally not necessary for resumption of breathing in the central sleep apnea event. In the case of a central apnea event, arousal follows the onset of breathing. Arousal following a central apnea event may stimulate hyperventilation recurrently and facilitate the occurrence of fluctuations in ventilation by reducing PaCO ₂ below the apnea threshold. Alternating hyperventilation and apnea patterns, once triggered, increase apnea drive, pulmonary congestion, wakefulness, and apnea-induced hypoxia resulting in PaCO ₂ fluctuations above and below the apnea threshold May be maintained by combination. Changes in the patient's state of consciousness, particularly those due to repeated wakefulness, can further destabilize breathing. Collecting information during and before and / or after the occurrence of central apnea may allow identification of variations associated with central apnea.

  The collected medical information (which can be stored, transmitted, printed, and / or displayed in long-term memory) is organized as a respiratory logbook entry 4328. The medical information may include various physiological and non-physiological data. For example, respiratory data, cardiovascular data, nervous system data, body position, activity, treatment history data, environmental data (temperature, altitude, air quality) and other types of medical information are organized as respiratory logbook entries can do. Respiratory logbook entries can be stored, transmitted, printed, and / or displayed.

  The respiratory event logbook includes a number of entries, and each entry may correspond to a separate respiratory event. The phenomenon entries included in the medical phenomenon log can be organized by various categories including, for example, the type of phenomenon, the time / date of the phenomenon, the order of occurrence of the phenomenon, and the treatment given to treat the phenomenon. it can. The selection of categories used to organize information may be programmable by the user. Systematized information can be stored in long-term memory, displayed, printed, and / or sent to a separate device.

  Collected information about the phenomenon may be 4330 that may be optionally accessed via an interactive user interface. An interactive user interface can access one or more log entries, for example, by activation of a selection process that includes a hierarchical selection menu or other selection method. In one implementation, the user can select a log entry from a menu by activating the input mechanism. Upon selection of a log entry, the user interface can display a chart or text of collected respiratory information associated with the medical phenomenon.

  The phenomenon information of the respiratory phenomenon log book can be stored in the long-term memory using various storage methodologies. For example, the respiratory event logbook can utilize a flat file system, a hierarchical database, a relational database, or a distributed database. A group of data of a phenomenon may be analyzed and / or summarized in various formats. Charts and / or text summary information may be displayed at the user interface and / or otherwise communicated to the user. For example, histograms, trend graphs, and / or other analysis tools or formats may be generated based on event entries in the respiratory event logbook. Respiratory Phenomenon Logbook display includes patient apnea / hypopnea index trends, histograms of night apnea / hypopnea and / or obstructive / central events, sleep stage diagrams (night sleep May indicate the stage), the heart rate trend during the night, and the oxygen saturation trend during the night.

  FIG. 44 is a block diagram of a respiratory event logbook system 4400 according to an embodiment of the present invention. The respiratory logbook system 4400 implements an event-driven method of collecting and organizing data related to the phenomenon affecting the patient's breathing.

  Various patient conditions can be monitored via sensor 4422, patient input device 4423, and / or information system 4424. Data associated with the patient condition may be stored in the short-term memory 4440. One or more of the patient conditions can be used by the phenomenon detection circuit 4436 to detect or predict the occurrence of a breathing phenomenon. When a phenomenon affecting breathing is detected or predicted, storage of information related to the phenomenon in the long-term memory 4460 by the phenomenon information processor 4432 is started. For example, the phenomenon information processor 4432 may receive information provided by one or more of the sensor 4422, the patient input device 4423, and the information system 4424 during and before the detection and / or prediction of the phenomenon. And / or can be collected later. The collected information associated with each phenomenon is organized as a respiratory logbook entry in the respiratory logbook. The respiration log book, or portion thereof, can be stored in the long-term memory 4460, transmitted to the remote device 4455, and / or displayed on the display device 4470.

  The embodiment shown in FIG. 44 includes a respiration sensor 4445 that senses a physiological condition modulated by the patient's respiration. In one embodiment, the respiratory sensor may include a transthoracic impedance sensor. Other ways of sensing respiration are possible. Such methods include, for example, the use of respiratory bands external to the patient, respiratory flow meter measurements, detection of implantable or external patient breathing sounds, blood oxygen levels, and / or other processes. Good. The respiration sensor 4445 can be used, for example, to acquire a respiration waveform before, during and / or after a phenomenon affecting the patient. A respiration waveform may be a component of a respiration log entry for a phenomenon.

  Information about various conditions affecting the patient and associated with the phenomenon can be obtained using sensor 4422, patient input device 4423, and / or other information system 4424. Sensor 4422 may include patient internal and / or patient external sensors coupled to interface 4431 of respiratory logbook system 4400 via leads or wirelessly. The sensor can sense various physiological and / or non-physiological conditions affecting the patient's respiratory system or other physiological systems. The patient input device 4423 allows the patient to input information related to the condition affecting the patient that may be useful in creating a respiratory event log. For example, the patient input device 4423 obtains information known to the patient, such as information related to the patient's smoking, drug use, recent exercise level, and / or other patient activity, perception and / or symptoms. May be particularly useful. The information provided by the patient input device may include information known to the patient related to the respiratory event that is not automatically sensed or detected by the respiratory logbook system 4400.

  The respiratory logbook system 4400 may include one or more information systems 4424, such as remote computing devices and / or network-based servers. The phenomenon information processor 4432 accesses the information system 4424 and is stored on a remote computing device and / or server, or is generated by a remote computing device and / or server, and / or the like. Information can be obtained from other sources. The information obtained from the information system 4424 can be recorded in a respiration log book along with other information related to the phenomenon affecting the breath. In one preferred implementation, the respiratory logbook system 4400 can access an air quality server connected to the Internet to collect data related to environmental conditions, such as ambient pollution indicators. In another implementation, the respiratory logbook system 4400 can access a patient's treatment history via a patient information server.

  Sensor 4422, patient input device 4423, and information system 4424 are coupled to other components of respiratory logbook system 4400 via interface circuit 4431. Interface 4431 may include circuitry for energizing sensor 4422 and / or for detecting and / or processing signals generated by the sensor. The interface 4431 may include, for example, a driver circuit, an amplifier, a filter, a sampling circuit, and / or an A / D converter circuit for adjusting the signal generated by the sensor.

  Interface 4431 may include circuitry 4450 for communicating with patient input device 4423, information system 4424, device programmer 4455, APM system (not shown), or other remote devices. Communication with the patient input device 4423, the information system 4424 and / or the remote device programmer 4455 and / or other remote devices using a wired connection or a wireless communication link such as Bluetooth or other wireless link Can be run through. The communication circuit 4450 may have the function of communicating wirelessly with various sensors, including implantable, subcutaneous, cutaneous, and / or non-implanted sensors.

  The respiratory logbook system 4400 can optionally be implemented as a component of a medical device that includes a treatment system such as a cardiac rhythm management system 4401. The cardiac rhythm management system 4401 may include a cardiac electrode 4425 that is electrically coupled to the patient's heart. The cardiac signal sensed by the cardiac sensing circuit 4420 can be used in the detection and treatment of various heart rhythm abnormalities. Abnormal heart rhythms include, for example, rhythms that are too slow (bradycardia), cardiac rhythms that are too fast (tachycardia), and / or poorly synchronized contractions of the atria and / or ventricles. (Ie, symptoms of congestive heart failure).

  If an arrhythmia is detected by the cardiac rhythm management system, the cardiac therapy circuit 4415 delivers cardiac therapy to the heart in the form of pacing and / or electrical stimulation pulses such as cardioversion / defibrillation pulses. be able to. The cardiac signal and / or cardiac condition, eg, an arrhythmia condition derived from or detected by use of the cardiac signal, may be associated with a respiratory event. Cardiac information associated with the phenomenon can be acquired and organized by the respiratory logbook system 4400.

  The user interface can be used to view and / or access respiratory logbook information. FIG. 45 shows a preferred depiction of the user interface display 4500. The display area 4505 can be used to show text or chart information about the respiratory phenomenon. As shown in FIG. 45, a menu of respiratory events 4510 can be presented, and the user can thereby access additional information related to the respiratory events. Menu 4510 may provide a summary of parameters associated with the events included in the respiratory logbook. One or more summary parameters such as onset number 4521, date / time 4522, type 4523, duration 4524, sleep stage 4525, and / or environment 4526, among other parameter headings, as shown in FIG. Headings can be presented at the top of menu 4510 or in another convenient location. Summary parameter headings 4521-4526 may be programmable, and for example, parameter headings in addition to or different from the parameter headings shown in FIG. 45 may be selected.

  Type parameter 4523 may include abbreviations for various respiratory phenomena. For example, AP-C and AP-O can simplify central and obstructive apnea, respectively, HP is an abbreviation for hypopnea, CS is an abbreviation for Chain Stokes breath, and RSB is an abbreviation for rapid and shallow breathing.

  The breathing phenomenon displayed as a menu item in menu 4510 can be selected by the user by onset number, date / time, duration, type, number, or by other criteria. Menu items may be selected and displayed based on various criteria ranges and / or threshold values. For example, in the example screen shown in FIG. 45, a different phenomenon group selected as a menu item can be selected by activating the Modify Query button 4531. The Modify Query button 4531 and other buttons shown on the display device can be activated, for example, by voice, by touching the screen of the display device, or by operating a keyboard or pointing device.

  In one implementation, when the Modify Query button 4531 is activated, the breaths that the user should present in the menu according to various criteria, such as by date / time, duration, type, number, or other criteria range or threshold An interactive session is started where the phenomenon can be selected. In one example, the user can select all apnea events to be presented as menu items. In another example, the user can select all phenomena that occurred between the first date and the second date. In yet another example, the user can select all phenomena that occurred while the patient experienced certain environmental conditions (eg, ambient temperature range and / or humidity range). In another example, the user can select all events in the respiratory logbook. The selection criterion can be displayed in the onset query selection area 4532 of the display device. The onset query selection region 4532 in the depiction of the respiratory logbook display device shown in FIG. 45 indicates that all onsets are selected and displayed as menu items.

  In menu 4510, the user can select a breathing event for which additional text and / or chart information is displayed. The additional information provides more detailed information about the selected phenomenon than the summary information presented in menu 4510. In the preferred example diagram shown in FIG. 45, the selection is indicated by a check mark 4507 beside the selected respiratory event. For convenience, the display device may include a select all button 4551 and / or a select none button 4552. When the select all button 4551 is activated, all phenomena in the menu 4510 are selected. When the select none button 4552 is activated, all phenomena in the menu 4510 are deselected.

  Upon activation of the detail button 4542 following selection of one or more episodes in the menu, detailed text information associated with the selected phenomenon is presented on the display screen. Detailed information can be displayed, for example, in the area of screen 4505 previously occupied by menu 4510. The user can use the prev button 4541 and the next button 4543 to scroll back and forth through the text information about one or more selected phenomena. The text information can be printed when the print button 4544 is activated, or can be saved to a disk or other storage medium by activation of the save to disk button 4555.

  Chart information associated with the selected phenomenon can be displayed when the signals button 4562 is activated. In one implementation, the respiratory waveform acquired during, before, and / or during the selected phenomenon is displayed in the display area 4505 previously used for menu 4510. Can be displayed. Other parameters, such as heart rhythm, patient activity waveforms, may additionally or alternatively be displayed. In one implementation, marked waveforms can be displayed. For example, a marked respiration waveform may be registered with a respiration waveform to indicate the occurrence of one or more conditions before, during, and later with the acquired respiration waveform. It may contain one or more symbols. The symbol may have a numerical or textual description associated with respiratory characteristics, eg, average respiratory rate, expiratory slope, etc. In one example, various characteristics of the disordered breathing phenomenon are also displayed with the respiratory waveform, including quantifiable characteristics such as duration of onset, blood oxygen saturation, type of disordered breathing, and / or other detected characteristics. can do. The user can use the prev and next buttons 4541, 4543 to scroll through the entire waveform associated with the selected phenomenon.

  FIG. 46A is a timing diagram illustrating the acquisition of respiratory logbook information for a detected respiratory disorder event, according to an embodiment of the present invention. The respiratory logbook system senses and stores a sliding scale window 4610 for one or more patient conditions, such as the patient conditions listed in Table 1, in a temporary buffer. The selection of information to be sensed and stored may be programmable by the physician. The selection of information to be acquired may be based on the patient's treatment history. For example, if the patient is suffering from central sleep apnea, the respiratory logbook may preferably be programmed to sense a condition associated with respiratory disorder. Conversely, if the patient is suffering from obstructive sleep apnea, it will be able to sense something different from the set of conditions used for central respiratory disorder.

  In the case of 4615 in which a phenomenon affecting breathing is detected, pre-phenomenon information 4630 acquired before the phenomenon is stored. Information is collected and stored 4640 during the event. When it is detected that the phenomenon has ended 4645, post-phenomenon information 4650 is collected and stored for a period of time after the end of the phenomenon. The phenomenon and post-phenomenon information 4640, 4650 can be acquired in a continuous manner, or the information can be acquired during individual intervals. After the post-phenomenon information 4650 is collected, the acquired information 4630, 4640, 4650 is organized as a respiratory phenomenon logbook entry. The respiratory logbook system starts sensing for the next phenomenon.

  FIG. 46B is a timing chart illustrating the acquisition of respiratory logbook information for a predicted phenomenon affecting breathing, according to an embodiment of the present invention. The respiratory logbook system senses and stores a sliding scale window 4610 for one or more patient conditions, such as the patient conditions listed in Table 1, in a temporary buffer. The sensed and stored conditions are programmable and may be selected based on the patient's treatment history. For example, the sensed and stored information may include information that is effectively used to predict one or more types of phenomena affecting the patient's breathing. If the event affecting breathing is predicted 4612, pre-prediction information 4620 is obtained and stored. If a phenomenon affecting breathing is detected 4615, pre-phenomenon information 4630 acquired before the phenomenon is stored. Information 4640 is collected and stored during the phenomenon. When it is detected that the phenomenon has ended 4645, information 4650 is collected and stored for a period of time after the end of the phenomenon. Pre-phenomenon, phenomenon and post-phenomenon information 4630, 4640, 4650 can be acquired in a continuous fashion, or information can be acquired during individual intervals. After the post-phenomenon information 4640 is collected, the acquired information 4620, 4630, 4640, 4650 is organized as a respiratory phenomenon logbook entry. The respiratory logbook begins sensing for the next phenomenon.

  As described above with reference to FIG. 45, the respiratory logbook display device may include information presented in a chart format. In one embodiment, the user can select and view a marked respiratory waveform, for example. 47A and 47B show examples of marked respiration waveforms that can be acquired and organized in a respiration logbook.

  FIG. 47A shows a marked respiration waveform according to an embodiment of the present invention. In one embodiment, information related to the marked respiration waveform can be obtained continuously as a moving snapshot of the respiration related state. In another embodiment, information related to the marked respiration waveform can be obtained in response to one or more triggering events. In one example, the triggering phenomenon may include a command from a physician to initiate data collection or a command through an advanced patient management system. In another example, the triggering phenomenon may include detection of various respiratory conditions such as detection of a disordered breathing or detection of sleep. In this scenario, the triggering event initiates the collection of breathing related data during a time interval that may include a time period before, during, and / or following the disordered breathing event. Can do.

  As shown in FIG. 47A, the marked respiration waveform 4710 may include a respiration symbol placed at a location relative to the respiration waveform to indicate when the respiration event occurs or the time at which the characteristic is calculated. In this example, the respiration waveform 4710 represents a respiration symbol 4720 representing the time between peaks on the waveform, 4730 when the hypopnea was detected, and 4735 (after 22 seconds) when the hypopnea ended. It is marked with a hypopnea symbol. In addition, other symbols indicating the respiratory characteristics and / or respiratory disorder characteristics described above may be superimposed on the respiratory waveform. The marked respiratory waveform may be displayed on a display device so that the patient's physician can view respiratory disorders and / or other characteristics.

  In addition to displaying the respiration waveform 4710, the display device can show other measurements and / or other waveforms. In FIG. 47B, an electrocardiogram (ECG) 4750 is shown above the respiratory waveform 4710. The ECG 4750 is timed with the respiration waveform 4710 and can be indexed corresponding to, for example, the occurrence of respiration and / or cardiac events. On the ECG of FIG. 47B, a marker 4760 is displayed indicating the sensed ventricular phenomenon (Vs) and the paced ventricular phenomenon (Vp). The display of marked breathing waveforms and other waveforms associated with the patient condition allows the patient's physician to verify that, for example, a disordered breathing event has been correctly detected. This confirmation can be used to guide diagnosis and / or treatment. Symbols that annotate cardiac and respiratory events provide the physician with further diagnostic information.

A phenomenon-based sleep log book can be created using a technique similar to the respiratory log book described on the sleep log book. The sleep logbook information can be used to assess the impact of a disordered breathing or disordered breathing treatment on the patient's sleep quality. Such an assessment can be performed by the treatment assessment processor 260 of FIG. The treatment impact assessment can now be used to develop an appropriate treatment for the patient.

  Appropriate treatment development involves automatic access by the device to sleep logbooks, respiratory logbooks, and / or other information acquired and / or stored by treatment devices and / or diagnostic devices. May be included. The device can use the information to develop an appropriate treatment and communicate this information to the treatment device. In another example, the physician can access sleep logbooks, respiratory logbooks, and / or information elsewhere, and communicate remotely with the treatment device to treat the patient. Can be started, modified, or terminated.

  The sleep logbook represents a system for organizing sleep-related data. According to one embodiment, each sleep logbook entry may include data associated with a particular sleep related phenomenon. The phenomenon may include various types of phenomena related to sleep. The type of information obtained and the type of sleep-related phenomenon represented in the sleep logbook may be programmable by the user.

  According to one embodiment of the present invention, the information is transmitted throughout the sleep period, for example, throughout the patient's typical sleep time, or during one or more specific sleep stages. Can be collected continuously or periodically. The system may begin collecting information before, during and / or after sleep detection or detection of a particular sleep stage. In this example, each sleep period for which data is collected can be organized as a sleep logbook entry.

  The system can begin acquiring information responsive to detecting or predicting a phenomenon that occurs during sleep. In this example, data associated with each phenomenon that occurs during sleep can be organized as a sleep logbook entry. The system can collect data during and near the phenomenon. For example, data can be collected before, during and / or after a detected or predicted phenomenon.

  The sleep logbook obtains information about one or more conditions associated with sleep and / or sleep quality. Table 1 lists a representative set of conditions associated with sleep and / or sleep quality. In various embodiments, the collection of information can be controlled in response to a triggering phenomenon. In this embodiment, in response to the triggering phenomenon, the system can start acquiring information related to sleep, stop acquiring information, or continue to acquire information. Triggering phenomenon can be, for example, a physiological phenomenon, a non-physiological phenomenon, a cardiovascular phenomenon, a respiratory phenomenon, a nervous system phenomenon, a muscular phenomenon, a sleep related phenomenon, a respiratory disorder phenomenon, a sleep stage, or other One or more of the phenomena may be included.

  FIG. 48 is a block diagram of a sleep logbook system 4800 according to an embodiment of the present invention. In this preferred embodiment, the system includes sleep logbook functionality provided with cardiac rhythm management. This embodiment is particularly useful for patients who would benefit from cardiac pacing and / or defibrillation support via an implantable cardiac pulse generator.

  Various patient conditions associated with sleep can be monitored via sensor 4822, patient input device 4823, and / or information system 4824. Sleep detection circuit 4836 can detect the start and / or stop of sleep using one or more of the patient conditions. When sleep onset is detected, the data acquisition unit 4833 of the sleep logbook processor 4832 starts collecting information associated with the sleep period. For example, data acquisition unit 4833 was provided by one or more of sensor 4822, patient input device 4823, and information system 4824 before, during, and / or after a sleep period. Information can be collected. The collected information associated with each sleep period is organized as a sleep logbook entry in the sleep logbook. The sleep log book, or portion thereof, can be stored in the memory 4860, transmitted to the remote device 4855, and / or displayed on the display device 4870.

  The embodiment shown in FIG. 48 may include, for example, a respiration sensor that senses a physiological condition modulated by the patient's respiration. In one embodiment, the respiratory sensor may include an implantable transthoracic impedance sensor. Other ways of sensing respiration are possible. Such methods include, for example, the use of respiratory bands external to the patient, respiratory flow meter measurements, detection of implantable or external patient breathing sounds, blood oxygen levels, and / or other processes. Good. The respiration sensor can acquire information used in detecting sleep start and stop, as described in more detail below. Additionally or alternatively, respiration sensing can be used to acquire a respiration waveform, for example, before, during, and / or after a phenomenon affecting the patient. The respiratory waveform may be a component of a sleep log book entry.

  Information about various conditions associated with and / or occurring during sleep can be obtained using sensors 4810, 4822, patient input device 4823, and / or other information systems 4824. The sensors may include patient internal 4810 and / or patient external sensor 4822 coupled to interface 4831 of sleep logbook system 4800 via leads or wirelessly. The sensor can sense various physiological and / or non-physiological conditions. The patient input device 4823 allows the patient to input information related to the condition affecting the patient that may be useful in creating a sleep log. For example, the patient input device 4823 may include patient smoking, drug use, recent exercise levels, and / or other patient activity, symptoms, or patient perceptions of daytime sleepiness and / or sleep quality. It may be particularly useful in obtaining information known to the patient, such as information related to Information provided by the patient-input device may include patient known information that is not automatically sensed or detected by the sleep logbook system 4800.

  Sleep logbook system 4800 may include one or more information systems 4824, such as remote computing devices and / or network-based servers. Phenomenon information processor 4832 accesses information system 4824 and is stored on a remote computing device and / or server, or generated by a remote computing device and / or server, and / or the like. Information can be obtained from other sources. Information obtained from the information system 4824 can be recorded in a sleep logbook, along with other information related to the phenomenon affecting sleep. In one preferred implementation, the sleep logbook system 4800 can access an air quality server connected to the Internet to collect data related to environmental conditions, such as ambient pollution indicators. In another implementation, the sleep logbook system 4800 can access a patient's treatment history via a patient information server.

  Sensor 4822, patient input device 4823, and information system 4824 are coupled to other components of sleep logbook system 4800 via interface circuit 4831. Interface 4831 may include circuitry for energizing sensor 4822 and / or for detecting and / or processing signals generated by the sensor. The interface 4831 may include, for example, a driver circuit, an amplifier, a filter, a sampling circuit, and / or an A / D converter circuit for adjusting the signal generated by the sensor.

  Interface 4831 may include circuitry 4850 for communicating with patient input device 4823, information system 4824, device programmer 4855, APM system (not shown), or other remote devices. Communication with patient input device 4823, information system 4824 and / or remote device programmer 4855 and / or other remote devices can be done using a wired connection or a wireless communication link such as Bluetooth or other wireless link Can be run through. The communication circuit 4850 may have a function of wirelessly communicating with various types of sensors including an implantable, subcutaneous, skin, and / or external sensor.

  The functionality of the sleep logbook may optionally be provided by a medical device that includes a treatment system, such as an implantable cardiac rhythm management system 4801. The cardiac rhythm management system 4801 may include a cardiac electrode 4825 that is electrically coupled to the patient's heart. The cardiac signal sensed by the cardiac sensing circuit 4820 can be used in detecting and treating various abnormal heart rhythms. Abnormal heart rhythms include, for example, rhythms that are too slow (bradycardia), cardiac rhythms that are too fast (tachycardia), and / or poorly synchronized contractions of the atria and / or ventricles. (Ie, symptoms of congestive heart failure).

  If an arrhythmia is detected by the cardiac rhythm management system, the cardiac therapy circuit 4815 delivers cardiac therapy to the heart in the form of pacing and / or electrical stimulation pulses such as cardioversion / defibrillation pulses. be able to. A cardiac signal and / or cardiac condition (eg, an arrhythmia condition derived or detected by use of the cardiac signal) may be associated with sleep. Cardiac information associated with sleep can be acquired and organized by sleep logbook system 4800.

  The user interface can be used to view and / or access sleep logbook information. FIG. 49 shows a preferred depiction of a user interface display 4900. Display device area 4905 can be used to show text or chart information about sleep. As shown in FIG. 49, a sleep period menu 4910 can be presented, and the user can thereby add additional information related to sleep disturbances that occur during and / or during the sleep period. Can be accessed. Menu 4910 may provide a summary of parameters associated with sleep periods included in the sleep logbook. 49, one of the other parameter headings, such as sleep period 4921, start date / time 4922, stop date / time 4923, apnea / hypopnea index 4924, uninterrupted sleep efficiency 4925, etc. Or, more summary parameter headings can be presented at the top of menu 4910 or in another convenient location. The summary parameter headers 4921-4925 may be programmable, and additional or different parameter headers may be selected for the parameter headers shown in FIG.

  The sleep period displayed as a menu item in menu 4910 can be selected by the user by onset number, date / time, duration, or by other criteria such as one or more sleep quality indicators. is there. Additionally or alternatively, the menu items can reflect one or more sleep disorder phenomena, eg, movement disorder events and / or respiratory disorder events. Menu items may be selected and displayed based on various criteria ranges and / or threshold values. For example, in the example screen shown in FIG. 49, a different phenomenon group selected as a menu item can be selected by activating the Modify Query button 4931. In another scenario, different sleep disorder phenomenon groups selected as menu items can be selected by activating the Modify Query button 4931. The Modify Query button 4931 and other buttons shown on the display device can be activated by voice, activated by touching the screen of the display device, or activated by operating a keyboard or pointing device, for example.

  In one implementation, activation of the Modify Query button 4931 enables a systematic menu for various criteria by date / time, duration, type, sleep quality metrics, etc., or by other criteria parameters. A session is initiated in which the user can select the sleep period and / or sleep disturbance phenomenon to be presented to the user. In one example, the user can select all sleep periods that have uninterrupted sleep efficiency (USE) metrics below a threshold to be presented as a menu item. In another example, the user can select all sleep periods between the first date and the second date. In yet another example, the user can select all sleep disorder phenomena of a particular type that occurred while the patient experienced certain environmental conditions, eg, ambient temperature range and / or humidity range. In yet another example, the user can select all sleep periods or all sleep disorder events represented in the sleep logbook. Selection criteria can be displayed in the onset query selection area 4932 of the display device. The onset query selection area 4932 in the depiction of the sleep logbook display device shown in FIG. 49 indicates that all onsets are selected and displayed as menu items.

  In menu 4910, the user can select a sleep period in which additional text and / or chart information is displayed. The additional information provides more detailed information for the selected time period than the summary information presented in menu 4910. In the preferred example diagram shown in FIG. 49, the selection is indicated by a check mark 4907 beside the selected sleep period. For convenience, the display device may include a select all button 4951 and / or a select none button 4952. When the select all button 4951 is activated, all sleep periods in the menu 4910 are selected. When the select none button 4952 is activated, all sleep periods in the menu 4910 are deselected.

  Following the selection of one or more sleep periods in the menu, the detail button 4942 is activated and detailed text information associated with the selected sleep periods is presented on the display device screen. Detailed information can be displayed, for example, in the area of screen 4905 previously occupied by menu 4910. The user can use the prev button 4941 and the next button 4943 to scroll back and forth through the text information for one or more selected sleep periods. The text information can be printed when the print button 4944 is activated, or can be saved to a disk or other storage medium by activation of the save to disk button 4955.

  Chart information associated with the selected sleep period can be displayed when the signals button 4962 is activated. In one implementation, respiratory waveforms acquired during all or a portion of the selected sleep period can be displayed in the display area 4905 previously used for the menu 4910. In one implementation, the respiratory waveform can be acquired before, during, and / or after a respiratory event that occurs during sleep. Other parameters, such as heart rhythm, patient activity waveforms, may additionally or alternatively be displayed. In one implementation, marked waveforms can be displayed. For example, the marked respiration waveform may include one or more symbols aligned with the respiration waveform to indicate the occurrence of one or more conditions along with the respiration waveform. The symbol may have a numerical or textual description associated with respiratory characteristics, eg, average respiratory rate, expiratory slope, etc. In one example, various characteristics of the disordered breathing phenomenon are also displayed with the respiratory waveform, including quantifiable characteristics such as duration of onset, blood oxygen saturation, type of disordered breathing, and / or other detected characteristics. can do. The user can use the prev and next buttons 4941 and 4943 to scroll through the entire waveform associated with the selected phenomenon.

Some adapted patients based on treatment interactions may receive electrical cardiac stimulation therapy for both respiratory disorders and cardiac disorders such as bradycardia and / or CHF. There may be an interaction between cardiac electrical therapy to alleviate respiratory disturbances and the patient's cardiac pacing procedure, such as pacing for bradycardia or cardiac resynchronization. Such interactions can be factored into the assessment of the impact of the disordered breathing treatment on the overall treatment delivered to the patient.

  There may be an interaction between cardiac therapy and disordered breathing therapy, and detection of the interaction can be used to adjust the therapy. In some cases, cardiac pacing therapy directed to alleviate cardiac dysfunction such as bradycardia or CHF can be enhanced by cardiac electrical therapy to alleviate respiratory disturbances. For example, non-excitable electrical stimulation of the left ventricle during the absolute refractory period may be beneficial to treat both CHF and respiratory disorders.

  In other examples, cardiac electrotherapy for respiratory disorders may serve the opposite purpose as the patient's cardiac pacing procedure. A pacing therapy delivered for the treatment of breathing disorders may increase the percentage of heart beats initiated by atrial pacing. However, if the intrinsic atrial phenomenon is relaxed to initiate a heart beat, simultaneous cardiac resynchronization therapy may be optimal. In this situation, the disordered breathing therapy, cardiac resynchronization therapy, or both therapies can be adjusted to reduce undesirable therapeutic interactions.

  FIG. 58 is a block diagram of a sleep / breathing disorder treatment system 5800 arranged according to an embodiment of the present invention. The treatment system 5800 includes a transthoracic impedance sensor 5880 that provides signals related to the breathing of the patient 5850. The output of the transthoracic impedance sensor is coupled to a respiratory pattern detector 5840. The patient's breathing pattern is analyzed by the breathing disorder detector 5811 to detect sleep / breathing disorders as described in more detail above.

  Sleep / breathing disorder detector 5811 is coupled to treatment control module 5810. If the sleep disorder detector 5811 detects sleep / breathing disorder, the therapy control module 5810 signals the therapy module 5820 to deliver the disordered breathing therapy to the patient 5850. The treatment control module 5810 performs a treatment assessment and allows the effectiveness of the treatment to be enhanced, the treatment delivered to reduce the impact of the treatment on the patient, or a combination of these therapeutic goals. Adapt treatment to be achieved.

  The therapy control module 5820 may include, for example, a circuit 5813 for evaluating the effectiveness of the therapy and a circuit 5812 for assessing the impact of the therapy on the patient. In one embodiment, the effectiveness of the treatment is assessed by analyzing the patient's breathing pattern following treatment delivery to detect and further classify the development of a disordered breathing. The therapy control module 5810 adapts the therapy so that a more effective therapy is delivered if the disorder continues to develop or if the severity of the disorder has not been sufficiently mitigated by the therapy be able to.

  In another example implementation, as described above, treatment can be adapted based on one or more of acute and / or chronic physiological responses to respiratory disorders. . For example, treatment can be tailored based on hypoxia levels, intrathoracic pressure, or heart rate surges that a patient experiences during or immediately after the onset of a respiratory disorder. Furthermore, treatment can be tailored based on various chronic conditions including heart rate variability or increased blood pressure or sympathetic activity. A number of chronic physiological responses to respiratory disorders can be detected, for example, after the onset of one or more respiratory disorders during the period of arousal. Furthermore, treatment adaptation can be performed based on a combination of acute and chronic effects.

  One method of assessing the impact of treatment on a patient involves determining the number of hourly wakefulness experienced by the patient. In one example, an accelerometer 5890 coupled to a patient activity detector 5830 can be used to generate a signal indicative of patient activity. If the treatment is effective but the number of waking hours experienced by the patient is unacceptably high, the treatment control module 5810 can adapt the treatment to reduce the impact of the treatment. it can.

  FIG. 59 is a flow graph showing a method of performing cardiac electrical therapy to alleviate sleep / respiratory disturbance according to an embodiment of the present invention. As described above with reference to the block diagram of FIG. 58, a transthoracic impedance sensor may be used to provide a signal that characterizes the patient's breathing pattern. The accelerometer signal is used to assess treatment impact based on the number of hourly arousals experienced by the patient. More sensitive techniques for detecting arousal, such as EEG, can be used instead of or in addition to the accelerometer method.

  The signal from the transthoracic impedance sensor is sensed 5905 and used to detect 5910 of the patient's respiratory waveform pattern. If the patient's breathing pattern is consistent with severe respiratory distress (DB) 5915, cardiac therapy is first delivered to the patient at a relatively aggressive level 5920. If cardiac electrotherapy includes a relatively high rate of pacing, the patient may wake up from sleep with the treatment, thereby ending the development of severe respiratory distress. Cardiac electrotherapy may be modified over the night to reduce the level of shock.

  If the patient's breathing pattern did not show severe respiratory disturbance 5915 matched the respiratory disorder (DB) breathing pattern 5925, the initial level, eg, the patient's intrinsic rate, or the patient's normal Cardiac electrotherapy is delivered 5930 at a pacing rate that exceeds sleep rate by about 5 to about 15 bpm.

  For example, the patient's respiratory pattern is analyzed to detect the occurrence of a respiratory disorder, if any, and further assessed to assess the effectiveness of the treatment 5935. If treatment is not effective 5940, e.g., if breathing disturbances continue, or if additional events of breathing disorders are detected, the level of treatment is pre-determined, e.g. for cardiac pacing treatments, Modify treatment 5945 by raising it by about 5 bpm. If the treatment was effective 5940, the treatment level may be lowered 5950 by a predetermined amount, eg, about 5 bpm for cardiac pacing treatment. The level of adapted therapy may be limited by upper and lower limits, eg, upper and lower limits for respiratory disorder pacing therapy.

  The impact of treatment on the patient can be determined based on the quality of the patient's sleep. One measure of sleep quality includes calculating the number of hourly wakefulness experienced by the patient. Patient activity increases during waking from sleep. The level of patient activity is determined by sensing the accelerometer signal in response to patient motion 5955, or by analyzing the patient's minute ventilation signal, or a combination of the accelerometer signal and the minute ventilation signal. Can be detected. Based on the detected patient activity indicated by the activity signal, the number of hourly arousals (A / H) experienced by the patient can be calculated 5960. If the number of hourly wakefulness experienced by the patient is less than or equal to a predetermined threshold, it is determined that the patient's sleep quality is acceptable and the treatment impact 5965 is acceptable. If the treatment impact is determined to be acceptable 5965, the treatment level is not corrected. If the number of awakenings per hour exceeds a threshold, the treatment impact 5965 is unacceptable and may therefore reduce the treatment level, eg, pacing rate 5970.

  It will be appreciated that the components and functionality shown in the figures and described herein can be implemented in hardware, software, or a combination of hardware and software. Components and functionality shown in the figures as separate or individual blocks / elements can be implemented in combination with other components and functionality, and such components and functionality can be implemented individually or integrally. It is further understood that the depiction in the form is intended for clarity of explanation and not for the purpose of limitation.

  Various modifications and additions can be made to the various embodiments described above without departing from the scope of the present invention. Accordingly, the scope of the invention should not be limited by the particular embodiments described above, but should be defined only by the claims set forth below and equivalents thereof.

4 is a flowchart illustrating a method for treating a respiratory disorder according to an embodiment of the present invention. 1 is a block diagram of a system configured to perform cardiac electrotherapy to treat a respiratory disorder according to an embodiment of the present invention. FIG. FIG. 3 is a partial view of an implantable device that may include circuitry for implementing cardiac stimulation therapy for a respiratory disorder, according to an embodiment of the present invention. 1 is a diagram of a transthoracic heart sensing and / or stimulation device implanted in a patient according to an embodiment of the present invention. FIG. FIG. 6 is a block diagram illustrating various components of a transthoracic heart sensing and / or stimulation device according to an embodiment of the present invention. FIG. 6 illustrates various components of a transthoracic heart sensing and / or stimulation device positioned in accordance with an embodiment of the present invention. FIG. 6 illustrates various components of a transthoracic heart sensing and / or stimulation device positioned in accordance with an embodiment of the present invention. FIG. 6 illustrates various components of a transthoracic heart sensing and / or stimulation device positioned in accordance with an embodiment of the present invention. FIG. 3 shows the placement of an electrode subsystem relative to the heart, according to an embodiment of the invention. FIG. 3 shows the placement of an electrode subsystem relative to the heart, according to an embodiment of the invention. FIG. 3 shows the placement of an electrode subsystem relative to the heart, according to an embodiment of the invention. Fig. 5 shows a normal breathing pattern represented by a transthoracic impedance sensor signal. FIG. 4 shows a flowchart of a methodology for adapting treatment of a disordered breathing according to an embodiment of the present invention. Fig. 4 shows a breathing interval used for detection of a disordered breathing according to an embodiment of the present invention. FIG. 4 illustrates breath intervals used in detecting sleep apnea and severe sleep apnea according to embodiments of the present invention. FIG. 4 is a graph of a respiration pattern that can be detected according to an embodiment of the present invention. 4 is a graph of a respiration pattern that can be detected according to an embodiment of the present invention. 4 is a flowchart illustrating a method for apnea and hypopnea detection according to an embodiment of the present invention. 4 is a graph showing breathing intervals according to an embodiment of the present invention. 4 is a graph illustrating a method for detecting hypopnea according to an embodiment of the present invention. FIG. 5 is a chart showing nomenclature of individual respiratory disturbance events and respiratory disorder combinations that can be addressed in accordance with embodiments of the present invention. FIG. FIG. 5 is a chart showing nomenclature of individual respiratory disturbance events and respiratory disorder combinations that can be addressed in accordance with embodiments of the present invention. FIG. It is a graph which shows the disordered-breathing phenomenon including the mixture of an apnea respiratory cycle and a hypopnea respiratory cycle. It is a graph which shows the disordered-breathing phenomenon including the mixture of an apnea respiratory cycle and a hypopnea respiratory cycle. It is a graph which shows the disordered-breathing phenomenon including the mixture of an apnea respiratory cycle and a hypopnea respiratory cycle. It is a graph which shows the disordered-breathing phenomenon including the mixture of an apnea respiratory cycle and a hypopnea respiratory cycle. It is a graph which shows the disordered-breathing phenomenon including the mixture of an apnea respiratory cycle and a hypopnea respiratory cycle. It is a graph which shows the periodic breathing respiration pattern which can be detected by the embodiment of the present invention. 4 is a graph showing a chain Stokes breathing pattern that can be detected according to an embodiment of the present invention. 4 is a flowchart of a method for detecting a respiratory disorder according to an embodiment of the present invention. 5 is a flowchart of a method for classifying a disordered breathing phenomenon according to an embodiment of the present invention. FIG. 4 is a block diagram of a respiratory disorder classification circuit that can be realized by an embodiment of the present invention. 4 is a graph of accelerometer signals representing chest wall behavior of central and obstructive respiratory disorders according to an embodiment of the present invention. 4 is a graph of accelerometer signals representing chest wall behavior of central and obstructive respiratory disorders according to an embodiment of the present invention. 4 is a flowchart of a method for classifying a disordered breathing phenomenon as a central, obstructive or mixed phenomenon according to an embodiment of the present invention. 4 is a flowchart of a method for classifying a disordered breathing phenomenon as a central, obstructive or mixed phenomenon according to an embodiment of the present invention. 4 is a flow chart illustrating a method for triggering a disordered breathing treatment based on a predicted disordered breathing according to an embodiment of the invention. 1 is a block diagram of a disordered breathing treatment system including a disordered breathing prediction functionality according to an embodiment of the present invention. FIG. FIG. 3 is a diagram conceptually illustrating a method for predicting a respiratory disorder using a state affecting a patient according to an embodiment of the present invention. It is a figure which shows notionally the operation | movement of the prediction engine of a respiratory disorder according to embodiment of this invention. 4 is a flowchart illustrating a method for determining and updating a prediction judgment reference library according to an embodiment of the present invention. 4 is a flowchart illustrating a method for adapting treatment of a disordered breathing according to an embodiment of the present invention. 6 is a flow chart illustrating a method for adapting treatment of respiratory disorders, taking into account both treatment effectiveness and treatment impact, according to embodiments of the present invention. Fig. 4 illustrates a patient wearing a sleep quality monitor according to an embodiment of the present invention. 1 is a block diagram of a sleep detector that can be used with a treatment system for breathing disorders according to embodiments of the present invention. FIG. 5 is a flowchart illustrating a method for detecting sleep according to an embodiment of the present invention. 6 is a flowchart illustrating a method for detecting sleep using an accelerometer and a minute ventilation signal according to an embodiment of the present invention. It is a graph which shows a patient's activity and a heart rate, respectively. It is a graph which shows a patient's activity and a heart rate, respectively. It is a graph of a patient's minute ventilation signal. Fig. 6 illustrates adjustment of an active sleep threshold according to an embodiment of the present invention. 4 is a graph of an autonomic arousal response that can be utilized by a respiratory disorder treatment system according to an embodiment of the present invention. FIG. 5 is a flow diagram illustrating various optional processes that can be performed in conjunction with wake detection in accordance with embodiments of the present invention. 1 is a block diagram of a wakefulness detector that can be implemented in cooperation with a cardiac electrotherapy system according to an embodiment of the present invention. FIG. FIG. 4 is a block diagram illustrating a controller configured to receive one or more inputs for modifying the rate at which cardiac pacing is delivered for a respiratory disorder, according to an embodiment of the present invention. FIG. 4 is a block diagram illustrating a controller configured to receive one or more inputs for modifying the rate at which cardiac pacing is delivered for a respiratory disorder, according to an embodiment of the present invention. FIG. 5 is a signal flow diagram illustrating pacing rate adjustment according to an embodiment of the present invention. FIG. 5 is a signal flow diagram illustrating pacing rate adjustment according to an embodiment of the present invention. FIG. 5 is a signal flow diagram illustrating pacing rate adjustment according to an embodiment of the present invention. FIG. 6 is a block diagram illustrating a controller including several different inputs for modifying pacing or other treatment rates delivered based on detection of a disordered breathing according to embodiments of the present invention. 6 is a graph illustrating pacing speed correction according to an embodiment of the invention. 6 is a graph illustrating pacing speed correction according to an embodiment of the invention. FIG. 4 is a graph illustrating a method of using at least one of coefficients a and b as a function of one or more previous intermittent cardiac periods according to an embodiment of the present invention. 4 is a flowchart illustrating a method that includes collecting medical information associated with a respiratory phenomenon, according to an embodiment of the present invention. FIG. 3 is a block diagram of a respiratory logbook according to an embodiment of the present invention. Fig. 2 shows a preferred depiction of a respiratory logbook user interface display device according to an embodiment of the present invention. 4 is a timing chart illustrating acquisition of respiratory logbook information according to an embodiment of the present invention. 4 is a timing chart illustrating acquisition of respiratory logbook information according to an embodiment of the present invention. Fig. 6 shows a marked respiration waveform according to an embodiment of the present invention. Fig. 6 shows a marked respiration waveform according to an embodiment of the present invention. FIG. 3 is a block diagram of a sleep log book according to an embodiment of the present invention. Fig. 4 illustrates a preferred depiction of a sleep logbook user interface display device, according to an embodiment of the present invention. 4 is a flowchart illustrating a method for classifying sleep stages according to an embodiment of the present invention. 4 is a flowchart illustrating a method for classifying sleep stages according to an embodiment of the present invention. 1 is a block diagram of a system suitable for performing a sleep stage classification method according to an embodiment of the present invention. FIG. It is a block diagram which shows the sleep stage discrimination circuit comprised by embodiment of this invention. 3 is a flowchart illustrating a method for performing sleep stage classification according to an exemplary embodiment of the present invention. 3 is a flowchart illustrating a method for performing sleep stage classification according to an exemplary embodiment of the present invention. FIG. 4 is a process flow diagram illustrating a process for performing sleep stage classification and testing in cooperation with therapeutic delivery, according to an embodiment of the present invention. 1 illustrates a medical system that can be used to perform sleep stage informed therapy according to embodiments of the present invention. Fig. 5 illustrates various configurations of a muscle tension sensor mechanically coupled to an implanted medical device, according to embodiments of the present invention. Fig. 5 illustrates various configurations of a muscle tension sensor mechanically coupled to an implanted medical device, according to embodiments of the present invention. Fig. 5 illustrates various configurations of a muscle tension sensor mechanically coupled to an implanted medical device, according to embodiments of the present invention. Fig. 5 illustrates various configurations of a muscle tension sensor mechanically coupled to an implanted medical device, according to embodiments of the present invention. FIG. 6 is a block diagram of a sleep / breathing disorder treatment system 5800 arranged according to an embodiment of the present invention. 5 is a flowchart illustrating a method for performing cardiac electrical therapy for alleviating sleep / respiratory disorder according to an embodiment of the present invention.

Claims (56)

An automated treatment method,
Receiving sensory information indicative of one or more physiological or non-physiological conditions;
Adapting cardiac electrotherapy to treat a respiratory disorder based on the sensory information;
Generating an output signal deliverable to the heart based on the adapted cardiac electrotherapy, receiving the sensory information, adapting the cardiac electrotherapy, A method wherein at least two of the steps of generating an output signal are performed using an implantable component. Receiving the sensory information comprises:
Receiving sensory information indicative of a physiological condition;
Receiving sensory information indicative of a non-physiological condition;
The method of claim 1, comprising: Adapting the cardiac electrical therapy comprises:
Detecting a respiratory disorder based on the received sensory information;
Adapting the cardiac electrotherapy based on the detected respiratory disorder;
The method of claim 1, comprising: Receiving the sensory information comprises receiving sensory information associated with a breathing pattern; and
Adapting the cardiac electrical therapy comprises:
Detecting a respiratory disorder based on the respiratory pattern;
Adapting the cardiac electrotherapy based on the detection of a respiratory disorder;
including,
The method according to claim 1. Receiving the sensory information comprises:
Receiving perceptual information indicative of sleep;
Receiving perceptual information indicative of a respiratory disorder;
Including, and
Adapting the cardiac electrical therapy comprises:
Detecting sleep based on the received sensory information indicative of sleep;
Detecting a respiratory disorder during sleep based on the received sensory information indicating a respiratory disorder;
Adapting the detection of a disordered breathing during a time indicative of sleep;
including,
The method according to claim 1. Adapting the cardiac electrotherapy based on the respiratory disorder comprises:
Detecting a respiratory disorder based on the received sensory information;
Determining the severity of the detected respiratory disorder;
Adapting the cardiac electrotherapy based on the severity of the detected respiratory disorder;
5. The method of claim 4, comprising: Adapting the cardiac electrical therapy comprises:
Predicting respiratory impairment based on the received perceptual information;
Adapting the cardiac electrotherapy to treat the predicted respiratory disorder;
The method of claim 1, comprising: Adapting the cardiac electrical therapy comprises:
Determining the effectiveness of a treatment based on the received sensory information;
Adapting the cardiac electrical therapy based on the effectiveness of the therapy;
The method of claim 1, comprising: The step of determining the effectiveness of the treatment comprises
Detecting a respiratory disorder based on the received sensory information;
Determining the effectiveness of the treatment based on the detected respiratory disorder;
The method of claim 9, comprising: Adapting the cardiac electrical therapy comprises:
Determining an impact of the cardiac electrotherapy based on the received sensory information;
Adapting the cardiac electrical therapy based on the therapeutic impact;
The method of claim 1, comprising: Adapting the cardiac electrical therapy comprises:
Assessing sleep quality based on the received perceptual information;
Adapting the cardiac electrotherapy based on the assessed sleep quality;
The method of claim 1, comprising: The step of assessing the quality of sleep comprises
Detecting sleep based on the received perceptual information;
Acquiring information related to sleep; and
Systematizing the acquired information as a sleep logbook;
Assessing the quality of sleep using the systematized information;
The method of claim 12, comprising: Adapting the cardiac electrical therapy comprises:
Assessing respiratory quality based on the received perceptual information;
Adapting the cardiac electrotherapy based on the assessed respiratory quality;
The method of claim 1, comprising: The step of assessing the quality of breathing comprises
Detecting or predicting a respiratory phenomenon based on the received perceptual information;
Initiating collection of medical information associated with the respiratory phenomenon in response to the detection or prediction of the respiratory phenomenon;
Collecting the medical information associated with the respiratory phenomenon;
Systematizing the medical information as a respiratory phenomenon log entry;
Assessing the quality of the breath using the systematic medical information;
15. The method of claim 14, comprising: Adapting the treatment comprises
Determining a sleep stage based on the received sensory information;
Adapting the cardiac electrotherapy based on the sleep stage;
The method of claim 1, comprising: Adapting the treatment comprises
Determining a sleep stage based on the received sensory information;
Carrying out sleep stage informed monitoring of breathing;
Adapting the cardiac electrical therapy based on the sleep stage informed breath monitoring;
The method of claim 1, comprising: Receiving the sensory information comprises receiving sensory information associated with a physiological condition modulated by autonomic arousal response information; and
Adapting the cardiac electrical therapy comprises:
Detecting an autonomic arousal phenomenon that occurs during sleep based on the perceptual information;
Adapting the cardiac electrotherapy based on the detected autonomic arousal phenomenon;
The method of claim 1, comprising: Adapting the cardiac electrical therapy comprises:
Determining a therapeutic interaction based on the received sensory information;
Adapting the cardiac electrical therapy to modify the interaction of the therapy;
The method of claim 1, comprising: A medical treatment device,
A sensing system configured to sense a condition affecting the patient;
A therapy control system coupled to the sensing system and configured to adapt cardiac electrotherapy to treat a respiratory disorder;
A medical treatment device coupled to the treatment control system and configured to deliver the adapted treatment to the patient, wherein the sensing system, the treatment control system, and At least two of the pulse generators include an implantable component.   The at least one of the sensing system, the therapy control system, and the cardiac electrical stimulation generator is disposed within an implantable housing of an implantable transthoracic heart stimulator. Item 20. The apparatus according to Item 20.   The at least one of the sensing system, the therapy control system, and the cardiac electrical stimulation generator is disposed within an implantable housing of an implantable cardiac rhythm management system. The apparatus according to 20.   21. The device of claim 20, wherein the sensing system includes a sensor configured to sense a physiological condition.   21. The apparatus of claim 20, wherein the sensing system includes a sensor configured to sense a non-physiological condition.   21. The disordered breathing detector coupled to the sensing system and the therapy controller, wherein the disordered breathing detector is configured to detect a disordered breathing based on the sensed condition. The apparatus, wherein the therapy controller is configured to adapt the cardiac electrical therapy based on the detected respiratory disorder.   The sensing system is configured to sense a condition modulated by patient breathing, and the disordered breathing detector is configured to detect a disordered breathing based on a breathing pattern. 26. The apparatus of claim 25.   21. The respiratory disorder prediction circuit coupled to the sensing system and the therapy controller, wherein the respiratory disorder prediction circuit is configured to predict a respiratory disorder based on the sensed condition. The apparatus, wherein the therapy controller is configured to adapt the therapy based on the predicted respiratory disorder.   The sensing system is configured to sense one or more conditions that cause the patient to suffer from a disordered breathing, and the disordered breathing prediction circuit is configured to detect a disordered breathing based on the etiological condition. 28. The apparatus of claim 27, wherein the apparatus is configured to predict.   The sensing system is configured to sense one or more precursor conditions for a disordered breathing, and the disordered breathing prediction circuit is configured to predict a disordered breathing based on the precursor conditions. 28. The apparatus of claim 27.   The treatment assessment processor further coupled to the sensing system and the treatment controller, wherein the treatment assessment processor is configured to determine the effectiveness of a treatment based on the sensed condition. The apparatus, wherein the therapy controller is configured to adapt the cardiac electrical therapy based on the effectiveness of the therapy.   31. A respiratory disorder detector coupled to the sensing system and the therapy assessment processor, wherein the respiratory disorder detector is configured to detect a respiratory disorder based on the sensed condition. The apparatus of claim 1, wherein the treatment assessment processor is configured to determine the effectiveness of the treatment based on the detected respiratory disorder.   31. A respiratory disorder detector coupled to the sensing system and the therapy assessment processor, wherein the respiratory disorder detector is configured to detect a respiratory disorder based on the sensed condition. The treatment assessment processor is configured to determine the severity of the detected respiratory disorder and to assess the effectiveness of the therapy based on the severity of the detected respiratory disorder. A device characterized by comprising.   And further comprising a treatment assessment processor coupled to the sensing system and the treatment controller, wherein the treatment assessment processor is configured to determine an impact of the cardiac electrotherapy on the patient based on the sensed condition. Item 20. The device of item 20, wherein the therapy controller is configured to adapt the cardiac electrical therapy based on the therapy impact.   A sleep quality monitor coupled to the sensing system and the therapy controller, the sleep quality monitor configured to collect sleep related information and to assess sleep quality based on the sensed condition 21. The apparatus of claim 20, wherein the therapy controller is configured to adapt the cardiac electrical therapy based on the assessment of sleep quality.   The sensing system includes a sensor configured to sense a physiological condition modulated by a sleep stage, and the sleep quality monitor determines a sleep stage based on the sensed physiological condition. 21. The apparatus of claim 20, wherein the apparatus is configured to collect sleep stage information and assess sleep quality using the sleep stage information.   21. The apparatus of claim 20, further comprising a sleep stage detector configured to determine a sleep stage, wherein the therapy controller is configured to deliver a sleep stage informed therapy based on the sleep stage. A device characterized by that.   The sensing system is configured to sense one or more physiological states modulated by autonomic arousal response information, and the modulation system is modulated by the autonomic arousal response information. 21. The autonomic wakefulness detector configured to detect an autonomic wakefulness phenomenon based on one or more sensed physiological states, 21. apparatus.   38. The apparatus of claim 37, wherein the therapy controller is configured to adapt the cardiac electrical therapy based on the detected autonomic arousal phenomenon.   21. A treatment assessment processor coupled to the sensing system and the treatment controller, wherein the treatment assessment processor is configured to determine a treatment interaction based on the sensed condition. The apparatus, wherein the therapy controller is configured to adapt the cardiac electrical therapy to modify the therapy interaction.   21. The device of claim 20, wherein the cardiac electrical therapy includes a biventricular pacing therapy.   21. The apparatus of claim 20, wherein the cardiac electrical therapy includes non-excitable cardiac electrical therapy.   21. The apparatus of claim 20, wherein the therapy controller is configured to switch to an overdrive pacing therapy based on the sensed condition.   21. The apparatus of claim 20, wherein the therapy controller is configured to switch to a bichamber pacing therapy based on the sensed condition.   21. The device of claim 20, wherein the cardiac electrical therapy includes an overdrive pacing therapy. The therapy controller is configured to obtain an intermittent cardiac interval between beats and to determine a first indicated pacing interval based at least on the intermittent interval duration and a previous value of the first indicated pacing interval. And
45. The apparatus of claim 44, wherein the cardiac electrical stimulation generator is configured to deliver the overdrive pacing therapy using the first indicated pacing interval.   21. The apparatus of claim 20, further comprising a sleep quality monitor configured to assess sleep quality based on the sensed condition, wherein the therapy controller is based on the assessed sleep quality. An apparatus configured to adapt the cardiac electrotherapy. A sleep detector configured to detect sleep;
47. A sleep logbook configured to acquire information related to sleep based on the sensed state and to organize the acquired information as a sleep logbook. The apparatus wherein the sleep quality monitor is configured to assess the sleep quality using the systematized information.   21. The apparatus of claim 20, further comprising a respiratory monitor configured to monitor respiratory quality based on the sensed condition, wherein the therapy controller adapts the cardiac electrical therapy based on the respiratory quality. A device characterized in that it is configured to A circuit configured to detect or predict a respiratory event;
49. A respiratory logbook circuit configured to collect medical information associated with the detected or predicted respiratory event and to organize the medical information as a respiratory event log entry. The apparatus of claim 1, wherein the respiratory monitor is configured to assess the quality of the breath using the systematic medical information. A therapeutic device,
Means for receiving sensory information indicative of one or more physiological or non-physiological conditions;
Means for adapting cardiac electrotherapy to treat a respiratory disorder based on the sensory information;
Means for generating an output signal deliverable to the heart based on the adapted cardiac electrotherapy;
At least two of the means for receiving sensory information, the means for adapting the cardiac electrotherapy, and the means for generating an output signal based on the adapted cardiac electrotherapy Comprises a implantable component. Means for detecting a respiratory disorder based on the perceptual information;
Means for adapting the cardiac electrotherapy based on the detected respiratory disorder;
51. The treatment device of claim 50, further comprising: Means for predicting respiratory impairment based on the perceptual information;
Means for adapting the cardiac electrotherapy based on the predicted respiratory disorder;
51. The treatment device of claim 50, further comprising: Means for assessing the effectiveness of the respiratory disorder treatment based on the perceptual information;
Means for adapting the cardiac electrical therapy based on the assessment of therapeutic effectiveness;
51. The treatment device of claim 50, further comprising: Means for assessing treatment impact based on the perceptual information;
Means for adapting the cardiac electrical therapy based on the assessment of therapeutic impact;
51. The treatment device of claim 50, further comprising: Means for determining sleep quality based on the perceptual information;
Means for adapting the cardiac electrotherapy based on the assessment of sleep quality;
51. The treatment device of claim 50, further comprising: Means for detecting an autonomic arousal phenomenon based on the perceptual information;
Means for determining sleep quality based on the detected autonomic arousal phenomenon;
55. The treatment device of claim 54, further comprising: Means for classifying respiratory disorder events based on the perceptual information;
Means for adapting the cardiac electrotherapy based on the classification of respiratory disorder events;
51. The treatment device of claim 50, further comprising:

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