Classifications

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  • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
  • A61B5/6867—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive specially adapted to be attached or implanted in a specific body part
  • A61B5/6869—Heart
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  • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
  • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
  • A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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  • A61B5/25—Bioelectric electrodes therefor
  • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
  • A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
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  • A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
  • A61B5/293—Invasive
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  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/369—Electroencephalography [EEG]
• • A—HUMAN NECESSITIES
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  • A61B5/6813—Specially adapted to be attached to a specific body part
  • A61B5/6814—Head
• • A—HUMAN NECESSITIES
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  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
  • A61B5/6813—Specially adapted to be attached to a specific body part
  • A61B5/6822—Neck
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
  • A61B5/6867—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive specially adapted to be attached or implanted in a specific body part
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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  • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
  • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
  • A61B5/0006—ECG or EEG signals
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
  • A61B5/026—Measuring blood flow
  • A61B5/029—Measuring or recording blood output from the heart, e.g. minute volume
• • A—HUMAN NECESSITIES
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  • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
  • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
• • A—HUMAN NECESSITIES
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  • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
  • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
• • A—HUMAN NECESSITIES
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  • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
  • A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
  • A61B5/4064—Evaluating the brain
• • A—HUMAN NECESSITIES
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  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7271—Specific aspects of physiological measurement analysis
  • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

• This disclosure relates to systems and methods for detecting a stroke and stroke risk management in mechanical circulatory support device patients.
• Heart disease is one of the leading causes of death and hospitalization among the elderly.
• the number of patients that reach an advanced phase of heart disease e.g., end stage heart failure, refractory heart failure, or terminal heart failure
• Patients with end stage heart failure fall into stage D of the ABCD classification of the American College of Cardiology (ACC)/ American Heart Association (AHA), and class III-IV of the New York Heart Association (NYHA) functional classification and may be registered with the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS).
• ACC American College of Cardiology
• AHA American Heart Association
• NYHA New York Heart Association
• INTERMACS Interagency Registry for Mechanically Assisted Circulatory Support
• Treatment of end stage heart failure may include implant of a mechanical circulatory support device (e.g., a ventricular assist device, such as a left ventricular assist device) to aid the heart in pumping blood to the body.
• a mechanical circulatory support device e.g., a ventricular assist device, such as a left ventricular assist device
• a ventricular assist device may be used to sustain life until a heart transplant procedure may be performed (e.g., as a bridge to transplant), as a permanent solution to reduce the symptoms of heart disease (e.g., destination therapy), or as a temporary measure to treat a reversible condition (such as, e.g., myocarditis).
• ventricular assist devices may be effective in the treatment or management of symptoms of heart failure, ventricular assist patients may experience an increased risk of a stroke.
• An embolus may be ingested into a mechanical circulatory support (MCS) device pump from the left atrium or left ventricle of a heart of an MCS device patient. Such an embolus may become lodged in the pump or be passed through the pump, which may increase a risk that the MCS device patient may suffer a stroke.
• MCS mechanical circulatory support
• Stroke is a serious medical condition that can cause permanent neurological damage, complications, and death. Stroke may be characterized as the rapidly developing loss of brain functions due to a disturbance in the blood vessels supplying blood to the brain.
• the loss of brain functions can be a result of ischemia (lack of blood supply) caused by thrombosis, embolism, or hemorrhage (ruptured blood vessel).
• ischemia lat of blood supply
• embolism embolism
• hemorrhage ruptured blood vessel
• Stroke is the number two cause of death worldwide and the number one cause of disability.
• Speed to treatment is the critical factor in stroke treatment as 1 9M neurons are lost per minute on average during stroke. Stroke diagnosis and time between event and therapy delivery are the primary barriers to improving therapy effectiveness.
• a recent study of MCS device patients showed that 21% of MCS device patients suffered at least one stroke, with 13% suffering an acute ischemic stroke and 10.3 % suffering an intracerebral hemorrhage.
• a clinician may administer a tissue plasminogen activator or undertake intravascular interventions such as thrombectomy procedures to treat ischemic stroke.
• intravascular interventions such as coil embolization to treat an intracerebral hemorrhagic stroke.
• the disclosure describes systems and techniques for detection of stroke and an increased risk of stroke in mechanical circulatory support (MCS) device patients.
• MCS mechanical circulatory support
• the power draw of the pump of the MCS device may increase as the pump is attempting to move embolic matter through the MCS device while maintaining a specific pump speed.
• the signals recorded by the MCS device can show abnormalities related to ingestion and passing of embolic material.
• These passed emboli can result in stroke of variable clinical significance and may be detectable using an electroencephalogram (EEG) from an EEG device.
• EEG electroencephalogram
• Such an increase in pump power draw may be indicative of an increase in likelihood the MCS device patient will have a stroke.
• the described systems and techniques may detect the occurrence of a stroke, either clinical or sub-clinical, in the MCS device patient.
• Sub-clinical strokes may be indicative of an increased risk for a larger, more debilitating stroke.
• a sub-clinical stroke may be a precursor to a more debilitating stroke. Therefore, it may be desirable to detect a stroke, including a sub-clinical stroke, or increased stroke risk in a MCS device patient so that early treatment may be provided.
• the EEG device, the MCS device, a computing device such as a computer, a server, a device monitor, a device programmer, a device controller, or the like
• a computing device such as a computer, a server, a device monitor, a device programmer, a device controller, or the like
• the EEG device may analyze an EEG signal taken by the EEG device and a pump power signal from the MCS, and based on an increase in the pump power being within a predetermined time period of one or more meaningful changes in the EEG signal to determine the patient may have experienced a stroke.
• the disclosure describes a stroke risk detection system including memory configured to store an indication of a first feature of a pump signal and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: receive the pump signal, wherein the pump signal is indicative of one or more operational parameters of a mechanical circulatory support (MCS) device; receive an electroencephalogram (EEG) signal; determine the first feature in the pump signal; determine a second feature in the EEG signal; determine whether the second feature is within a predetermined time period of the first feature; and based on the second feature being within the predetermined time period of the first feature, determine an indication of a stroke risk.
• MCS mechanical circulatory support
• EEG electroencephalogram
• the disclosure describes a stroke risk detection method including receiving, by processing circuitry, a pump signal, wherein the pump signal is indicative of an operational parameter of a mechanical circulatory support (MCS) device; receiving, by the processing circuitry, an electroencephalogram (EEG) signal; determining, by the processing circuitry, a first feature in the pump signal; determining, by the processing circuitry, a second feature in the EEG signal; determining, by the processing circuitry, whether the second feature is within a predetermined time period of the first feature; and based on the second feature being within the predetermined time period of the first feature, determining, by the processing circuitry, an indication of a stroke risk.
• MCS mechanical circulatory support
• EEG electroencephalogram
• the disclosure describes a computer-readable storage medium comprising instructions that, when executed by processing circuitry of a medical device system, cause the processing circuitry to receive the pump signal, wherein the pump signal is indicative of an operational parameter of a mechanical circulatory support (MCS) device; receive an electroencephalogram (EEG) signal; determine a first feature in the pump signal; determine a second feature in the EEG signal; determine whether the second feature is within a predetermined time period of the first feature; and based on the second feature being within the predetermined time period of the first feature, determine an indication of a stroke risk.
• MCS mechanical circulatory support
• EEG electroencephalogram
• FIG. l is a conceptual diagram illustrating an example stroke risk detection system according to the techniques of this disclosure.
• FIG. 2 is a conceptual diagram illustrating a 10-20 map for electroencephalography (EEG) sensor measurements according to the techniques of this disclosure.
• EEG electroencephalography
• FIG. 3 is a functional block diagram illustrating another example stroke risk detection system according to the techniques of this disclosure.
• FIGS. 4A-4C are conceptual diagrams illustrating example EEG devices according to the techniques of this disclosure.
• FIG. 5 is a conceptual diagram illustrating example signals which may be used to with a stroke risk detection system according to the techniques of this disclosure.
• FIG. 6 is a flow diagram illustrating example techniques of detecting a stroke risk.
• Example devices include a mechanical circulatory support (MCS) device, an electroencephalogram (EEG) device configured to capture an EEG signal from a patient, a computing device, or a server (such as a server in the cloud).
• MCS mechanical circulatory support
• EEG electroencephalogram
• a computing device may include a device monitor, a programmer, an off-the-shelf computing device such as a smart phone, a tablet, a laptop computer, or a desktop computer, or the like.
• the techniques of this disclosure may be performed by any combination of such devices.
• Processing circuitry may monitor a pump signal of the MCS device and the EEG signal for features, such as spikes, deviations, or meaningful or significant deviations from baseline, in both signals occurring within a predetermined period of time of each other.
• the processing circuitry may cause a user interface or communication circuitry communicatively coupled to the processing circuitry to alert a user, such as, for example, a clinician, a caregiver, the patient in which the MCS device is implanted, or a remote server system, of the indication of the stroke risk.
• the processing circuitry may send an email, short message service (text), phone call or other alert.
• the alert may include a portion or one or both signals.
• an alert may reduce the time before treatment may be given or changed compared to other systems and methods. For example, upon receiving the alert, a clinician may make adjustments in patient management, such as changes in blood pressure management, anti coagulation medication, etc., which may reduce the risk of additional strokes or larger, more damaging strokes in the patient.
• an alert may direct the patient to seek emergency medical assistance and/or an alert may be delivered to an emergency medical system.
• FIG. l is a conceptual diagram illustrating an example stroke risk detection system according to the techniques of this disclosure.
• Stroke detection system 100 includes computing device 104, MCS device 106, and EEG device 108.
• Stroke risk detection system 100 is configured to detect a stroke, in part, by monitoring a signal from
• the monitored signal may be a combination of one or more pump signals, such as a power signal, current signal, voltage signal, or the like.
• stroke detection system 100 may include
• EEG device 108 which is configured to monitor an EEG of patient 112.
• device 108 may be an implantable medical device (IMD) which may be implanted on a cranium, or otherwise on or near the head, of patient 112.
• IMD implantable medical device
• device 108 may be a wearable device, such as a patch, hat, headband, or other device configured to be attached to the head of patient 112.
• MCS device 106 includes inflow cannula 114, pump 116, outflow cannula 118, and driveline 120.
• MCS device 106 may be the same or substantially similar to the sealless rotary blood pump as described in United States Patent Number 6,688,861 B2 by Wampler, titled SEALLESS ROTARY BLOOD PUMP.
• inflow cannula 114 may be the same or substantially similar to the conduit device as described in United States Patent Number 8,870,739 B2 by LaRose et al., titled CONDUIT DEVICE FOR USE WITH A VENTRICULAR ASSIST DEVICE.
• a first end of inflow cannula 114 may be fluidically coupled to inlet 124 of pump 116 and a second end of inflow cannula 114 may be grafted to heart 110, e.g., the left ventricle of heart 110.
• the second end of inflow cannula 114 may connect to a ventricular connector, such as described in United States Patent Number 8,403,823 B2 by Yu et al., titled VENTRICULAR CONNECTOR.
• a first end of outflow cannula 118 is fluidically coupled to outlet 126 of pump 116, and a second end of outflow cannula 118 is grafted or otherwise fluidically coupled to an artery of patient 112, e.g., aorta 128.
• Pump 116 is configured to draw blood from a chamber of heart 110 and pump the blood to other portions of the body of patient 112.
• Pump 116 may include any suitable biocompatible pump such as, for example, an axial flow pump, a centrifugal pump, a diaphragm pump, a pulsatile pump, a peristaltic pump, a screw pump, or a scroll pump.
• pump 116 may be the same or substantially similar to the blood pump as described in U. S. Patent Number 7,699,586 B2 by LaRose et al., entitled WIDE BLADE, AXIAL FLOW PUMP.
• pump 116 may be similar to an HVAD® Pump is further discussed in U. S. Patent Nos.
• pump 116 may be similar to an MV AD® Pump is further discussed in U. S. Patent Nos. 8,007,254 B2 by LaRose et al., entitled AXIAL FLOW PUMP WITH MULTI-GROOVED ROTOR; 8,419,609 B2 by Shambaugh et.
• Pump 116 includes a motor powered by driveline 120.
• driveline 120 may provide electrical and/or mechanical power to the motor of pump 116.
• the power supplied by driveline 120 is controlled by MCS controller 122.
• MCS device 106 may be communicatively coupled to MCS controller 122 via driveline 120.
• MCS device 106 may communicate data associated with an operation of MCS device 106 to MCS controller 122 via driveline 120. While not depicted as such in FIG.
• MCS device 106 may be fully implanted and be configured to wirelessly communicate data associated with the operation of the MCS device with external devices, such as an unattached MCS data recorder, alert system, phone, or peripheral monitor or programmer.
• MCS controller 122 may be powered by one or more batteries 130, which may be separately housed from MCS controller 122 and electrically coupled to MCS controller 122 by power cord 132.
• MCS controller 112 and one or more batteries 130 are removably attached to a carrier 134.
• One or more batteries 130 and carrier 134 allow patient 112 to remain ambulatory while using MCS device 106.
• one or more batteries 130 may be housed within MCS controller 112.
• MCS controller 122 may monitor the pump signal, which may be a power signal, voltage signal, or current signal, indicative of the power draw of pump 116.
• MCS controller 122, EEG device 108, and computer device 104 may each include a communications interface, such as an Ethernet card, a radio frequency transceiver, cellular transceiver, a Bluetooth® interface card, USB interface, or any other type of device that can send and receive information.
• a communications interface such as an Ethernet card, a radio frequency transceiver, cellular transceiver, a Bluetooth® interface card, USB interface, or any other type of device that can send and receive information.
• EEG device 108 is located in a rear portion of a user’s neck or a rear portion of the skull. In other examples, EEG device 108 may be located at other positions of patient, such as near the user’s temple(s) (e.g., above the ear(s)) and/or over the temporal portion of the skull. In some examples, EEG device 108 may be located at a location generally centered with respect to the head or neck. In some examples, EEG device 108 may be located in an off-center location in order to obtain desired vectors from the electrodes carried on the housing of EEG device 108. EEG device 108 can be disposed in patient 112 either via implantation (e.g., subcutaneously) or by being placed over the patient’s skin with one or more electrodes of EEG device 108 being in direct contact with the patient’s skin.
• implantation e.g., subcutaneously
• EEG electrodes While conventional EEG electrodes are placed over the patient’s scalp, the present technology advantageously enables recording of clinically useful brain activity data via electrodes positioned at the rear of the patient’s neck or head, or other cranial locations, such as temporal locations, described herein.
• This anatomical area is well suited to suited both to implantation of EEG device 108 and to temporary placement of an EEG device over the patient’s skin, such as a patch, a hat, or a headband.
• EEG device 108 takes the form of a LINQTM Insertable Cardiac Monitor (ICM), available from Medtronic pic, of Dublin, Ireland.
• ICM Insertable Cardiac Monitor
• the example techniques may additionally, or alternatively, be used with a medical device not illustrated in FIG. 1 such as another type of sensor device, a patch monitor device, a wearable device (e.g., smart watch), or another type of external medical device.
• EEG device 108 includes a plurality of electrodes.
• the plurality of electrodes of EEG device 108 are configured to detect a signal indicative of an electric potential of the tissue surrounding the EEG device 108.
• EEG device 108 may additionally or alternatively include one or more optical sensors, accelerometers, impedance sensors, temperature sensors, chemical sensors, light sensors, pressure sensors, and/or acoustic sensors, in some examples. Such sensors may detect one or more physiological parameters indicative of a patient condition.
• computing device 104 when computing device 104 is configured for use by the clinician, computing device 104 may be used to transmit instructions to EEG device 108.
• Example instructions may include requests to set electrode combinations for sensing and any other information that may be useful for programming into EEG device 108.
• the clinician may also configure and store operational parameters for EEG device 108 within EEG device 108 with the aid of computing device 104.
• computing device 104 assists the clinician in the configuration of EEG device 108 by providing a system for identifying potentially beneficial operational parameter values.
• EEG device 108 may include a memory, a plurality of electrodes carried by the housing of EEG device 108, sensing circuitry configured to sense, via at least two electrodes of the plurality of electrodes, signals from patient 112 and monitor the EEG signal for features.
• the processing circuitry may be configured to then store the occurrence of the features in the memory.
• the housing of EEG device 108 carries the plurality of electrodes and contains, or houses, both of the sensing circuitry and the processing circuitry. In this manner, EEG device 108 may be referred to as a leadless sensing device because the electrodes are carried directly by the housing instead of by any leads that extend from the housing.
• EEG device 108 may include one or more sensing leads extending therefrom and into the tissue of the patient. Such lead(s) may be employed instead of or in addition to the electrodes of EEG device, and may perform any of the functions attributed herein to the electrodes.
• the plurality of electrodes are configured to detect brain activity data corresponding to activity in at least one of a P3, Pz, or P4 brain region, which is at the back of the head or upper neck region as shown in FIG. 2.
• the housing of EEG device 108 may be configured to be disposed at or adjacent a rear portion of a neck or skull of patient 112 or above the ear(s) of patient 112.
• the housing of EEG device 108 may be configured to be implanted within patient 112, such as implanted subcutaneously.
• the housing of EEG device 108 may be configured to be disposed on an external surface of skin of patient 112.
• computing device 104 may be a dedicated hardware device with dedicated software for the detecting a stroke risk.
• computing device 104 may be an off-the-shelf computing device, e.g., a desktop computer, a laptop computer, a tablet, or a smartphone running an application that enables computing device 104 to determine whether a second feature, such as one or more significant deviations from baseline, in the EEG signal is within a predetermined time period of a first feature, such as one or more increases, in the pump signal.
• Computing device 104 may be a consumer device configured to perform the techniques of this disclosure executing program instructions, or may be a special purpose device provided by, for example, the manufacturer of MCS device 106.
• the processing circuitry of computing device 104 may determine an indication of a stroke risk based on the first feature of the pump signal and the second feature of the EEG signal.
• Computing device 104 may include a data storage to store one or more signals.
• stroke risk detection system 100 may determine an indication of a stroke risk.
• computing device 104 is communicatively coupled (e.g., connected) to EEG device 108 via wireless link 136 and to MCS controller 122 via wireless link 138.
• Wireless links 136 and 138 may include a cellular link, a Bluetooth® link, a wireless local area network link, or the like.
• computing device 104 and MCS controller 122 may be part of the same device.
• computing device 104 may control an operation of MCS device 106 via MCS controller 122.
• computing device 104 may initiate an intervention.
• the intervention may include adjusting an operational state of MCS device 106.
• the operational state of MCS device 106 includes a speed (e.g., revolutions per minute) of pump 116 or an on/off state of pump 116 (e.g., pulsing pump 116).
• computing device 104 may receive from MCS controller 122 data associated with MCS device 106 (e.g., MCS data).
• MCS data includes, but is not limited to, the age and model type of one or more components of MCS device 106, the age and usage of one or more batteries 130, the power consumption of pump 116, flow data associated with blood flow through pump 116, MCS device 106 temperature, revolutions per minute of pump 116 or the motor, and user input.
• the MCS data includes the pump signal.
• Computing device 104 may include a user interface.
• the user interface may include a graphical user interface (GUI), a display, a keyboard, a touchscreen, a speaker, a microphone, or the like.
• Computing device 104 may include one or more output components that generate tactile output, audio output, video output, or the like that is received by user interface 108 to communicate information to a user (e.g., patient 112, a caregiver, or a clinician) or another entity, such as a remote server system.
• the user interface may notify a user of an indication of a stroke risk.
• Processing circuitry of computing device 104 may generate an alert representative of the indication of a stroke risk.
• the alert may be any type of information understandable by a human or machine, such as a user or another entity.
• the alert may include information representative of the indication of the stroke displayed on a display of the user interface.
• the information representative of the indication of a stroke may be an automated voice message, text, email, push notification, or web application notification.
• computing device 104 may include one or more input components that receive tactile input, kinetic input, audio input, optical input, or the like from a user or another entity via the user interface.
• the user interface may receive user input from a user and send user input to computing device 104.
• the user input (e.g., user data) may include, for example, at least one of information associated with the indication of a stroke risk, MCS data, and EEG data.
• FIG. 2 is a conceptual diagram illustrating a 10-20 map for EEG sensor measurements according to the techniques of this disclosure.
• various locations on the head of patient 12 may be targeted using the electrodes carried by EEG device 108.
• EEG device 108 may sense signals at one or more of P3, Pz or P4.
• EEG device 108 may sense signals at one or more of F7, T3, or T5 and/or at one or more of F8, T4, or T6.
• FIG. 3 is a functional block diagram illustrating another example configuration of a stroke risk detection system according to the techniques of this disclosure.
• Stroke risk detection system 200 may be the same or substantially similar to stroke risk detection system 100 of FIG. 1 except for the differences described herein.
• stroke risk detection system 200 includes a computing device 204, an MCS device 206, an MCS controller 222 and an EEG device 208.
• Computing device 204 may be an example of computing device 104 of FIG. 1.
• MCS device 206 may be an example of MCS device 106 of FIG. 1.
• MCS controller 222 may be an example of MCS controller 122 of FIG. 1.
• EEG device 208 may be an example of EEG device 108 of FIG. 1.
• computing device 204 is communicatively coupled to network 250.
• MCS controller 222 and/or EEG device 208 are optionally communicatively coupled to network 250.
• Network 250 represents any public or private communication network, for instance, based on Bluetooth, WiFi®, a proprietary protocol for communicating with devices, or other types of networks for transmitting data between computing systems, servers, and computing devices, both implanted within and external to a patient.
• EEG device 208, computing device 204, and MCS controller 222 may each be operatively coupled to network 250 using respective network links 252, 254, and 256.
• Network links 252, 254, and 256 may be any type of network connections, such as wired or wireless connections as discussed above.
• Network 250 may provide selected devices, such as EEG device 208, computing device 204, and MCS controller 222 with access to the Internet, and may allow EEG device 208, computing device 204, and MCS controller 222 to communicate with each other. For example, rather than communicating via link 236, EEG device 208 and computing device 204 may communicate via network links 252 and 254. Similarly, rather than communicating via link 238, computing device 204 and MCS controller 222 may communicate via network links 254 and 256.
• EEG device 208 and computing device 204 may communicate via network links 252 and 254.
• computing device 204 and MCS controller 222 may communicate via network links 254 and 256.
• the network is operatively coupled to a stroke risk detection platform 260 via network link 262.
• stroke risk detection platform 260 may be a network platform, such as the Medtronic CareLink® Network developed by Medtronic, pic, of Dublin, Ireland, or may be an application running on a server, such as a cloud computing server.
• Network link 262 may be the same or substantially similar to network links 252, 254, and 256 discussed above.
• computing device 204 may communicate with stroke detection platform 260 via network links 254 and 262.
• computing device 204 may send data to stroke risk detection platform 260, receive data from stroke risk detection platform 260, or both via network 250.
• computing device 204 may send any of pump signal, EEG signal, or an indication of a stroke to stroke risk detection platform 260.
• stroke risk detection platform 260 may store data received from a plurality of patients as captured by, for instance, at least one of pump signal, EEG signal, or an indication of passage of embolus through the pump received from a plurality of computing devices (e.g., computing device 204).
• stroke risk detection platform 260 may analyze the pump signal and the EEG signal to determine an indication of a stroke risk. In this way, stroke detection platform 260 may perform one or more functions discussed herein with respect to computing device 204.
• stroke risk detection platform 260 may analyze at least one of the pump signal, the EEG signal, an indication of stroke risk, MCS data, and user data to determine one or more factors attributable to indication of stoke.
• stoke risk detection platform 260 may analyze one or more signals from a plurality of computing devices (e.g., each respective computing device associated with a respective patient) to determine a first feature in the pump signal and a second feature in the EEG signal indicative of a stroke. In this way, stroke risk detection platform 260 may be configured to improve the accuracy of a determination of a stroke risk.
• Computing device 204 also may receive data from stroke risk detection platform 260 including, for example, stored signals, stored indications of stroke risk, stored user data, notification data (e.g., regarding indications of a stroke risk), algorithm data (e.g., to update or modify algorithms used by computing device 204 to determine an indication of a stroke risk), or the like.
• stroke risk detection system 200 may collect and analyze pump signals, EEG signals, indications of a stroke risk, MCS data, and user data from at least one computing device 204 to notify the at least one patient (e.g., via user interface 276) of an indication of a stroke risk or information relevant to the user (e.g., date and time of the suspected stroke, severity of the suspected stroke, etc.).
• computing device 204 of FIG. 3 is shown separate from MCS controller 222, in some examples, computing device 204 may include MCS controller 222.
• computing device 204 includes processing circuitry 263, user interface (UI) 276 communications circuitry 266, one or more output devices 268, and memory 270.
• memory 270 includes stroke risk detection module 274.
• computing device 204 may include additional components or fewer components than those illustrated in FIG. 3.
• Processing circuitry 263 may include various type of hardware, including, but not limited to, microprocessors, controllers, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or equivalent discrete or integrated logic circuitry, as well as combinations of such components.
• DSPs digital signal processors
• ASICs application specific integrated circuits
• FPGAs field-programmable gate arrays
• processing circuitry may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry.
• Processing circuitry 263 represents hardware that can be configured to implement firmware and/or software that sets forth one or more of the algorithms described herein.
• processing circuitry 263 may be configured to implement functionality, process instructions, or both for execution within computing device 204 of processing instructions stored within memory 270, such as stroke risk detection module 274.
• processing circuitry 263 includes processing circuitry of an IMD and/or other devices of system 200.
• Computing device 204 also includes UI 276.
• UI 276, in some examples, is configured to receive input from a user through tactile, audio, or video sources. Examples of UI 276 may include, a mouse, a button, a keyboard, a voice responsive system, video camera, microphone, touchscreen, or any other type of device for detecting a command from a user.
• UI 276 also, in some examples, is configured to provide output to a user using, for example, audio, video or tactile media.
• output devices 246 may include a speaker, a display, a sound card, a video graphics adapter card, or any other type of device for converting a signal into an appropriate form understandable to humans or machines.
• Computing device 204 further includes communications circuitry 266.
• Computing device 204 may utilize communications circuitry 266 to communicate with other devices (e.g., MCS controller 222, EEG device 208, and/or stroke risk detection platform 260) via one or more networks, such as one or more wired or wireless networks.
• Communications circuitry 266 may include a communications interface, such as an Ethernet card, a radio frequency transceiver, cellular transceiver, a Bluetooth® interface card, USB interface, or any other type of device that can send and receive information.
• computing device 204 utilizes communications circuitry 266 to wirelessly communicate with a remote server system (e.g., stroke risk detection platform 260).
• Memory 270 may be configured to store information within computing device 204 during operation.
• Memory 270 in some examples, include a computer-readable storage medium or computer-readable storage device.
• memory 270 include a temporary memory, meaning that a primary purpose of memory 270 is not long term storage.
• Memory 270 in some examples, include a volatile memory, meaning that memory 270 does not maintain stored contents when power is not provided to memory 270. Examples of volatile memories include random access memories (RAM), dynamic random-access memories (DRAM), static random-access memories (SRAM), and other forms of volatile memories known in the art.
• RAM random access memories
• DRAM dynamic random-access memories
• SRAM static random-access memories
• memory 270 is used to store program instructions for execution by processing circuitry 263.
• Memory 270 in some examples, is used by software or applications running on computing device 204 to temporarily store information during program execution.
• memory 270 may further include portions of memory 270 configured for longer-term storage of information.
• memory 270 may include non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.
• EPROM electrically programmable memories
• EEPROM electrically erasable and programmable
• computing device 204 also may include stroke risk detection module 274.
• Stroke risk detection module 274 may be implemented in various ways. For example, stroke risk detection module 274 may be implemented as an application or a part of an application executed by processing circuitry 263. In some examples, stroke risk detection module 274 may be implemented as part of a hardware unit of computing device 204 (e.g., as circuitry). In some examples, stroke risk detection module 274 may be implemented remotely on a remote server system (e.g., stroke risk detection platform 260) as part of an application executed by one or more processors of the remote serve system or as a hardware unit of the remote serve system. Functions performed by stroke risk detection module 274 are explained below with reference to the example flow diagram illustrated in FIG. 6.
• FIGS. 4A-4C are conceptual diagrams illustrating example EEG devices according to the techniques of this disclosure.
• EEG device 310 may be embodied as a monitoring device having housing 314, proximal electrode 313 A and distal electrode 313B (individually or collectively “electrode 313” or “electrodes 313”).
• Housing 314 may further comprise first major surface 318, second major surface 320, proximal end 322, and distal end 324.
• Housing 314 encloses electronic circuitry located inside EEG device 310 and protects the circuitry contained therein from body fluids. Electrical feedthroughs provide electrical connection of electrodes 313.
• EEG device 310 may be embodied as an external monitor, such as patch that may be positioned on an external surface of the patient, or another type of medical device, such as described further herein.
• EEG device 310 is defined by a length “L,” a width “W,” and thickness or depth “D ”
• EEG device 310 may be in the form of an elongated rectangular prism wherein the length L is significantly larger than the width W, which in turn is larger than the depth D.
• the geometry of EEG device 310 in particular, a width W being greater than the depth D — is selected to allow EEG device 310 to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during insertion.
• the spacing between proximal electrode 313a and distal electrode 313B may range from 30 millimeters (mm) to 55 mm, 35 mm to 55 mm, and from 40 mm to 55 mm and may be any range or individual spacing from 25 mm to 60 mm.
• the length L may be from 30 mm to about 70 mm.
• the length L may range from 40 mm to 60 mm, 45 mm to 60 mm and may be any length or range of lengths between about 30 mm and about 70 mm.
• the width W of first major surface 18 may range from 3 mm to 10 mm and may be any single or range of widths between 3 mm and 10 mm.
• the thickness of depth D of EEG device 310 may range from 2 mm to 9 mm. In other examples, the depth D of EEG device 310 may range from 2 mm to 5 mm and may be any single or range of depths from 2 mm to 9 mm.
• EEG device 310 according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort.
• Examples of EEG device 310 described in this disclosure may have a volume of 3 cc or less, 2 cc or less, 1 cc or less, 0.9 cc or less, 0.8 cc or less, 0.7 cc or less, 0.6 cc or less, 0.5 cc or less, 0.4 cc or less, any volume between 3 and 0.4 cc, or any volume that is less than 3 cc and greater than zero.
• proximal end 322 and distal end 324 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.
• the first major surface 318 faces outward, toward the skin of the patient while the second major surface 320 is located opposite the first major surface 318. Consequently, the first and second major surfaces may face in directions along a sagittal axis of patient, and this orientation may be consistently achieved upon implantation due to the dimensions of EEG device 310. Additionally, an accelerometer, or axis of an accelerometer, may be oriented along the sagittal axis.
• Proximal electrode 313 A and distal electrode 313B are used to sense signals (e.g., EEG signals, ECG signals, other brain and/or cardiac signals, or impedance) which may be submuscular or subcutaneous. Signals may be stored in a memory of EEG device 310, and signal data may be transmitted via integrated antenna 326 to another medical device, which may be another implantable device or an external device, such as computing device 104 (FIG. 1). In some examples, electrodes 313 A and 313B may additionally or alternatively be used for sensing any bio-potential signal of interest, such as an EMG or a nerve signal, from any implanted location.
• signals e.g., EEG signals, ECG signals, other brain and/or cardiac signals, or impedance
• Signals may be stored in a memory of EEG device 310, and signal data may be transmitted via integrated antenna 326 to another medical device, which may be another implantable device or an external device, such as computing device 104 (FIG. 1).
• proximal electrode 313 A is in close proximity to the proximal end 322, and distal electrode 313B is in close proximity to distal end 324.
• distal electrode 313B is not limited to a flattened, outward facing surface, but may extend from first major surface 318 around rounded edges 328 or end surface 330 and onto the second major surface 320 so that the electrode 313B has a three- dimensional curved configuration.
• proximal electrode 313 A is located on first major surface 318 and is substantially flat, outward facing.
• proximal electrode 313 A may utilize the three- dimensional curved configuration of distal electrode 313B, providing a three-dimensional proximal electrode (not shown in this example).
• distal electrode 313B may utilize a substantially flat, outward facing electrode located on first major surface 318 similar to that shown with respect to proximal electrode 313 A.
• the various electrode configurations allow for configurations in which proximal electrode 313 A and distal electrode 313B are located on both first major surface 318 and second major surface 320. In other configurations, such as that shown in FIG.
• EEG device 310 may include electrodes 313 on both first major surface 318 and second major surface 320 at or near the proximal and distal ends of the device, such that a total of four electrodes 313 are included on EEG device 310.
• Electrodes 313 may be formed of a plurality of different types of biocompatible conductive material (e.g., stainless steel, titanium, platinum iridium, or alloys thereof), and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.
• biocompatible conductive material e.g., stainless steel, titanium, platinum iridium, or alloys thereof
• coatings such as titanium nitride or fractal titanium nitride.
• FIG. 4A includes two electrodes 313, in some embodiments EEG device 310 can include 3, 4, 5, or more electrodes carried by the housing 314.
• proximal end 322 includes a header assembly 332 that includes one or more of proximal electrode 313 A, integrated antenna 326, anti-migration projections 334, or suture hole 336.
• Integrated antenna 326 is located on the same major surface (i.e., first major surface 318) as proximal electrode 313a and is also included as part of header assembly 332. Integrated antenna 326 allows EEG device 310 to transmit or receive data. In other examples, integrated antenna 326 may be formed on the opposite major surface as proximal electrode 313 A, or may be incorporated within the housing 314 of EEG device 310. In the example shown in FIG. 4A, anti migration projections 334 are located adjacent to integrated antenna 326 and protrude away from first major surface 318 to prevent longitudinal movement of the device. In the example shown in FIG.
• anti-migration projections 334 includes a plurality (e.g., six or nine) small bumps or protrusions extending away from first major surface 318. In other examples anti-migration projections 334 may be located on the opposite major surface as proximal electrode 313 A or integrated antenna 326.
• header assembly 332 includes suture hole 336, which provides another means of securing EEG device 310 to the patient to prevent movement following insert. In the example shown, suture hole 336 is located adjacent to proximal electrode 313 A.
• header assembly 332 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of EEG device 310.
• FIG. 4B shows a third electrode 392B at a midpoint between electrodes 390B and 391B.
• the dimension D of housing 374B of EEG device 360B can be increased to adjust the angle a to obtain a more orthogonal orientation for the triangular configuration of electrodes 390B-392B.
• EEG device 360B may have the same shape and dimensions as EEG device 310, except that electrode 392B is added to the side surface or back surface of housing 374B to create a triangle-shaped electrode configuration.
• FIG. 4C shows EEG device 360C with an extended third dimension D.
• Third electrode 392C is positioned at a comer to create a triangular-shaped electrode configuration with electrodes 390C and 391C.
• Dimension D can be designed to achieve specific angles for the triangular configuration of electrodes 390C-392C.
• an EEG device may include supplementary electrodes configured to record noise, such as environmental noise and/or EMG signals from the skeletal muscles.
• the recorded noise could be subtracted from the signals sensed by a pair of primary electrodes to cancel or reduce the noise in the signals sensed by the pair of primary electrodes.
• the supplementary electrodes can be positioned on the backside of a housing or a can of the EEG device facing the skeletal muscle to sense the skeletal muscle noise for the purpose of canceling the noise.
• a separate device such as a wearable device or external patch may include electrodes for sensing noise.
• FIG. 5 is a conceptual diagram illustrating example signals which may be used to with a stroke detection system according to the techniques of this disclosure.
• the x- axis of the two graphs of FIG. 5 represents time and the y-axis of the two graphs represents amplitude of the respective signal.
• EEG signal 402 and pump signal 404 is shown.
• Pump signal 404 may be indicative of the power draw of pump 116 (FIG. 1), such as a combination of one or more of the power signal, a voltage signal, or a current signal.
• a first feature 406, such as one or more increases, may occur at a time T represented by the left dashed line.
• a second feature 408, such as deviations from baseline, or in some examples significant deviations from baseline, may occur some time later in EEG signal 402.
• embolic material may move from pump 116 to a brain of patient 112 (both of FIG. 1) causing a stroke.
• processing circuitry 263 of computing device 204 (both of FIG.
• processing circuitry 263 may monitor pump signal 404 to determine first feature 406 has occurred. Processing circuitry 263 may also monitor EEG signal 402 to determine second feature 408 has occurred. Processing circuitry 263 may determine whether second feature 408 is within predetermined time period 410 of first feature 406. Predetermined time period 410 may be in the range of from about 2 minutes to about 2 days. In the example of FIG. 4, second feature 408 is within predetermined time period 410 of first feature 406. Based on second feature 408 being within predetermined time period 410 of first feature 406, processing circuitry 263 may determine an indication of a stroke. For example, processing circuitry 263 may determine a stroke may have occurred.
• processing circuitry 263 may through communication circuitry 266 and/or through UI 276 (both of FIG. 2) send an alert regarding the indication of the stroke risk to a user, such as a clinician, caregiver, or patient 112 (of FIG. 1).
• FIG. 6 is a flow diagram illustrating an example method of detecting a stroke risk.
• Processing circuitry 263 may receive a pump signal, wherein the pump signal is indicative of an operational parameter of an MCS device (502).
• processing circuitry 263 may receive, via communication circuitry 266 and link 238, pump signal 404 (of FIG. 5) from MCS controller 222.
• processing circuitry 263 may receive pump signal 404 from one or more batteries 130 or from pump 116 (both of FIG. 1).
• Pump signal 404 may be indicative of an operational parameter of the MCS device 106, e.g., indicative of a power draw of pump 116.
• the operational parameter of MCS device 106 includes at least one of power, voltage, or a current drawn by pump 116 of MCS device 106.
• MCS device 106 is a left ventricle assist device.
• Processing circuitry 263 may receive an EEG signal (504). For example, processing circuitry 263 may receive, via communication circuitry 266 and link 236, EEG signal 402 from EEG device 208.
• EEG device 208 is an implantable medical device. In other examples, EEG device 208 is a wearable device, such as a patch, hat, headband, or other device configured to remain attached to the outside of the head of patient 112.
• Processing circuitry 263 may determine a first feature in the pump signal (506). For example, processing circuitry may determine first feature 406 in pump signal 404.
• first feature 406 is at least one increase in pump signal 404 compared to surrounding portions of pump signal 404.
• Processing circuitry 263 may determine a second feature in the EEG signal (508). For example, processing circuitry 263 may determine second feature 408 in EEG signal 402. Second feature 408 may be one or more deviations from a baseline or a feature indicative of a neurological event, such as a stroke, a transient ischemic attack, or other neurological event, having occurred. Processing circuitry 263 may monitor EEG signal 402 for such a second feature. In some examples, second feature 408 is at least one deviation (or significant deviation) from baseline in EEG signal 402 when compared to surrounding portions of EEG signal 402.
• At least one deviation from baseline may be greater than 1 standard deviation from baseline over a predetermined time period, greater than 1 standard deviation from the baseline dispersion of the EEG signal (where dispersion is a standard deviation, a difference between a maximum and minimum amplitude, a difference between percentiles, or the like), or other significant increases or decreases in EEG frequency content.
• Processing circuitry 263 may determine whether the second feature is within a predetermined time period of the first feature (510). For example, processing circuitry 263 may determine whether second feature 408 is within predetermined time period 410 of first feature 406. In some examples, predetermined time period 410 is within a range from 2 minutes to 48 hours.
• processing circuitry 263 may determine an indication of a stroke risk (510). For example, processing circuitry 263 may determine that stroke may have occurred based on second feature 408 being within predetermined time period 410 of first feature 406. [0068] In some examples, based on determining the indication of the stroke risk, processing circuitry 263 sends an alert to a user, e.g., via communication circuitry 266 and/or UI 276. In some examples, the user is a clinician.
• any of EEG device 208, MCS controller 222, MCS device 206, stroke risk detection platform 260, or any combination thereof, may perform the techniques of this disclosure.
• a stroke risk detection system may determine an indication of a stroke, either clinical or sub-clinical. This may lead to earlier treatment than otherwise may occur, resulting in a lower likelihood of a more severe stroke or permanent injury to the patient.
• a stroke risk detection system comprising: memory configured to store an indication of a first feature of a pump signal; and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: receive the pump signal, wherein the pump signal is indicative of an operational parameter of a mechanical circulatory support (MCS) device; receive an electroencephalogram (EEG) signal; determine the first feature in the pump signal; determine a second feature in the EEG signal; determine whether the second feature is within a predetermined time period of the first feature; and based on the second feature being within the predetermined time period of the first feature, determine an indication of a stroke risk.
• MCS mechanical circulatory support
• EEG electroencephalogram
• Example 2 The stroke risk detection system of Example 1, wherein the operational parameter of the MCS device comprises at least one of power, voltage, or current drawn by a pump of the MCS device.
• Example 3 The stroke risk detection system of Example 1 or Example 2, wherein the EEG signal is received from an implantable medical device.
• Example 4 The stroke risk detection system of Example 1 or Example 2, wherein the EEG signal is received from a wearable device.
• Example 5 The stroke risk detection system of any of Examples 1-4, wherein the MCS device is a left ventricle assist device.
• Example 6 The stroke risk detection system of any of Examples 1-5, wherein the processing circuitry is further configured to: based on determining the indication of the stroke risk, send an alert to a user.
• Example 7 The stroke risk detection system of Example 6, wherein the user is a clinician.
• Example 8 The stroke risk detection system of any of Examples 1-7, wherein the predetermined time period is within a range from 2 minutes to 48 hours.
• Example 9 The stroke risk detection system of any of Examples 1-8, wherein the first feature is at least one deviation in the pump signal compared to surrounding portions of the pump signal.
• Example 10 The stroke risk detection system of any of Examples 1-9, wherein the second feature is at least one deviation in the EEG signal when compared to surrounding portions of the EEG signal.
• Example 11 A stroke risk detection method comprising: receiving, by processing circuitry, a pump signal, wherein the pump signal is indicative of an operational parameter of a mechanical circulatory support (MCS) device; receiving, by the processing circuitry, an electroencephalogram (EEG) signal; determining, by the processing circuitry, a first feature in the pump signal; determining, by the processing circuitry, a second feature in the EEG signal; determining, by the processing circuitry, whether the second feature is within a predetermined time period of the first feature; and based on the second feature being within the predetermined time period of the first feature, determining, by the processing circuitry, an indication of a stroke risk.
• MCS mechanical circulatory support
• EEG electroencephalogram
• Example 12 The stroke risk detection method of Example 11, wherein the operational parameter of the MCS device comprises at least one of power, voltage, or a current drawn by a pump of the MCS device.
• Example 13 The stroke risk detection method of Example 11 or Example 12, wherein the EEG signal is received from an implantable medical device.
• Example 14 The stroke risk detection method of Example 11 or Example 12, wherein the EEG signal is received from a wearable device.
• Example 15 The stroke risk detection method of any of Examples 11-14, wherein the MCS device is a left ventricle assist device.
• Example 16 The stroke risk detection method of any of Examples 11-15, further comprising: based on determining the indication of the stroke risk, sending an alert to a user.
• Example 17 The stroke risk detection method of any of Examples 11-16, wherein the predetermined time period is within a range from 2 minutes to 48 hours.
• Example 18 The stroke risk detection method of any of Examples 11-17, wherein the first feature is at least one deviation in the pump signal compared to surrounding portions of the pump signal.
• Example 19 The stroke detection method of any of Examples 11-18, wherein the second feature is at least one deviation in the EEG signal when compared to surrounding portions of the EEG signal.
• Example 20 A non-transitory computer readable storage medium storing instructions, which, when executed, cause processing circuitry to: receive the pump signal, wherein the pump signal is indicative of an operational parameter of a mechanical circulatory support (MCS) device; receive an electroencephalogram (EEG) signal; determine a first feature in the pump signal; determine a second feature in the EEG signal; determine whether the second feature is within a predetermined time period of the first feature; and based on the second feature being within the predetermined time period of the first feature, determine an indication of a stroke risk.
• MCS mechanical circulatory support
• EEG electroencephalogram

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