Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/389—Electromyography [EMG]
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/48—Other medical applications
  • A61B5/4806—Sleep evaluation
  • A61B5/4818—Sleep apnoea

Definitions

• the present invention relates to a signal processing system and method for detecting periodic real-time functional activity, particularly respiratory muscle activity.
• Devices for monitoring the respiratory activity of a patient comprising a single sensor for measuring respiratory muscle activity.
• this measurement sensor is an electrode placed near the explored muscle to obtain a good signal-to-noise ratio.
• AMR respiratory muscle activity
• this solution is not suitable for measuring the activity of the respiratory muscles other than the diaphragm.
• this measure is invasive.
• Non-invasive devices which include a surface electrode whose measurement is not robust.
• non-invasive devices that include a surface electrode whose signal is synchronized with a flow signal.
• Optimized analysis of surface electromyograms of the scalenes during quiet breathing in humans, Respiratory Physiology & Neurobiology 2006, Hug F. et al.” These devices make it possible to obtain precisely and robustly the location of the activation of activity. inspiratory scalene muscles.
• this measurement is long and assumes a certain stationarity of the respiratory activity, therefore it is not adapted to the detection of the respiratory activity in real time and can not therefore be used to control a fan.
• the object of the present invention is therefore to solve these problems by proposing a system and a signal processing method for the detection of a periodic functional activity, in particular adapted to the detection of respiratory muscle activity in real time, such as non-invasive and robust allowing an exploration of respiratory muscle function or allowing the control of a respiratory assistance device.
• the subject of the invention is a signal processing system for detecting a real-time cyclic functional activity, the processing system comprising:
• a sensor network comprising at least two sensors
• event combining identification means from the recordings of the acquired signals making it possible to perform a calibration, comprising:
• Means for identifying a plurality of events in the acquired signals each acquired signal of an acquisition means including an identified event, means for defining a model of the functional activity, the model being a time order (chronogram) of the plurality of identified events and average delays between the identified events,
• Means for defining a detection time window from the functional activity model are means for defining a detection time window from the functional activity model.
• the treatment system comprises one or more of the following features:
• the plurality of successive cycles of treatment comprises ten to twenty cycles
• means for monitoring the functional activity for a last complete cycle comprising: means for calculating a sliding average of the delays between the signals acquired during a plurality of successive cycles of monitoring, the plurality of successive cycles of monitoring comprising the last complete cycle; and, means for displaying the rolling average of the delays,
• the plurality of successive cycles of monitoring is less than the plurality of successive cycles of treatment
• the monitoring means furthermore comprise: means for comparing the rolling average of the delays with the average of the delays calculated during the calibration step in order to detect an anomaly;
• means for generating and transmitting an alarm signal in the event of an anomaly comprises signal acquisition means integrating the sensor network,
• the means of acquisition of the signals are means of acquisition of cardiac muscle signals
• the means of acquisition of the signals are means of acquisition of skeletal muscle signals
• the signal acquisition means are means for acquiring respiratory muscle signals
• the signal acquisition means are non-invasive acquisition means, it further comprises complementary processing means comprising:
• a ventilatory request means for detecting in real time a ventilatory request, the ventilatory request being defined by an ordered succession of the ventilatory event and respiratory events identified thanks to the model of respiratory activity and detected in the acquired muscle signals, and means for generating and transmitting a control signal to a respiratory assistance device connected to a patient, and
• complementary processing means comprising means for acquiring information relating to the state of the respiratory assistance apparatus and means for comparing this information relating to the respiratory assistance apparatus and the detection of the respiratory activity of the patient.
• this system makes it possible in particular to improve the strategies and methods of monitoring and assisting the respiratory function in anesthesia and intensive care, using robust real-time detection of respiratory muscle activity.
• This system concerns any information taken indirectly and / or constructed using a mathematical model (thus indirectly) which constitutes an image of a muscular contraction, such as respiratory contraction: muscular pressure (Pmus), mechanic gram, electromyogram.
• a mathematical model which constitutes an image of a muscular contraction, such as respiratory contraction: muscular pressure (Pmus), mechanic gram, electromyogram.
• This system is a means of combined analysis of muscle activity information. This information is extracted from indirect measurements such as EMG or MMG measurements or constructed by the model such as muscle pressure (this pressure corresponds to a first level of filtering of a flow signal).
• This information is able to deliver events by level thresholding. In no case does the system measure activity, but detects events representative of muscle activity, such as respiratory activity.
• the invention also relates to a signal processing method for detecting a real-time cyclic functional activity, intended to be implemented by a processing system of the aforementioned type, the treatment method comprising the following steps. simultaneous reception of the signals acquired by a sensor network comprising at least two sensors, and recording of the acquired signals during a plurality of successive cycles of processing; calibration from the recordings of the acquired signals, the calibration comprising the following steps:
• Defining a model of the functional activity the model being a temporal order of the plurality of identified events and the average detection delays between the identified events
• the treatment method comprises one or more of the following features:
• a step of monitoring the functional activity for a last complete cycle comprising the following steps: calculating a sliding average of the delays between the signals acquired during a plurality of successive cycles tracking, the plurality of successive cycles of tracking comprising the last complete cycle; then,
• the acquired signals are respiratory muscular signals and the model of functional activity is a model of respiratory activity
• the inventors have found in a new way that it is possible to describe the respiratory muscle activity by a network of activity of different muscles, called the physiological network, and to be able to qualify this network by a limited number of sensors or acquisition means. a signal forming an "instrumental network".
• FIG. 1 represents a block diagram illustrating the structure of a signal processing apparatus acquired by a sensor array for the detection of a real-time periodic functional activity according to the invention
• FIG. 2 is a block diagram; illustrating the signal processing method acquired by a sensor network for detecting a real-time periodic functional activity according to the invention
• FIG. 3 is a schematic representation of the signals acquired by a sensor array and the model obtained during the execution of the signal processing method as illustrated in FIG.
• FIG. 4 represents a block diagram illustrating a respiratory assistance device controlled by a signal processing apparatus acquired by a sensor network for the detection of a periodic functional activity in real time
• FIG. 5 is a block diagram. illustrating the signal processing method according to the invention and executed by the apparatus illustrated in FIG. 4.
• the system 2 for detecting the respiratory muscle activity (AMR) of a patient 4 comprises at least two acquisition means 6 or signal sensors forming an instrumental network.
• the signal acquisition means are means for acquiring respiratory muscle signals.
• the signal acquisition means are non-invasive means, such as surface electrodes, electromyogram (EMG) or myomecanogram (MMG) sensors.
• EMG electromyogram
• MMG myomecanogram
• each sensor invasive or not, explores only a particular muscle of the muscular respiratory system.
• One of these acquisition means may be a flow measurement from which muscle pressure will be constructed by a method known to those skilled in the art.
• the detection system 2 of the AMR further comprises means 8 for processing the muscular signals acquired by the acquisition means 6 according to a method for detecting the respiratory muscle activity described in detail hereinafter.
• the acquisition means 6 are connected to the processing means 8 of the muscular signals.
• the processing means 8 of the muscular signals comprise means for simultaneous reception of the signals acquired by the sensor array.
• They furthermore comprise means for recording the muscle signals acquired during a plurality of successive cycles called “processing cycles” and instrumental network calibration means from the recordings of the acquired signals.
• This calibration consisting in identifying a combination of events, the calibration means will also be called identification means of a combination of events.
• the instrumental network calibration means comprise means for identifying a plurality of events in the acquired signals where each acquired signal of an acquisition means includes an identified event.
• the calibration means further comprise means for defining a respiratory muscle activation model.
• the model is a temporal order of the plurality of identified events and average two-by-two delays between the identified events.
• the calibration means further comprise means for defining a time detection window based on the model of respiratory muscle activity.
• means for validating the position of the sensors 6 are integrated in the signal processing means.
• the means for validating the position of the sensors 6 include means for calculating a correlation score between two signals and means for comparing this correlation score with a value determined by an operator.
• the respiratory muscle activity detection system 2 further comprises means for monitoring respiratory muscle activity and displaying it in real time.
• the tracking means comprises means for calculating a sliding average of the delays between the signals acquired during a plurality of successive cycles called “tracking cycles”. This plurality of successive cycles of monitoring integrates the last complete cycle.
• the monitoring means 10 further comprise means for displaying the rolling average of the delays.
• the tracking means 10 may also comprise means for comparing the rolling average of the delays with the average of the delays calculated during the calibration step in order to detect an anomaly and means for generating and transmitting a signal. warning signal in case of anomaly.
• the classical approach proposes to select a priori the signal of the most representative muscle of the physiological phenomenon studied, for example, the measurement of the main inspiratory muscle when the inspiratory function is studied. This is the case with the isolated measure of diaphragmatic activity which reduces the generation of an inspiratory flow to the activity of this single muscle.
• the detection system integrating the network formed by the acquisition means is necessarily smaller than the physiological network. It is therefore essential to ensure the ability of the instrumental network to effectively describe the organization of the activity of different muscles or the existence of a physiological network. The existence of such an organization then makes it possible to define the inspiratory activity by a combination of different respiratory events and to ensure its robust detection.
• the operator positions at least two acquisition means or sensors on respiratory muscles of the patient participating in the respiratory activity of the latter so that the acquisition means can detect distinct muscle signals. each other.
• the sensors are positioned on different respiratory muscles. All the sensors thus arranged form the instrumental network.
• a pre-treatment is carried out in line, in particular for filtering, in particular to eliminate disturbances of the 50 Hz type and of the ECG type. (electrocardiogram), and are recorded during a plurality of respiratory cycles of patient treatment.
• the plurality of respiratory treatment cycles comprises 10 to 20 respiratory cycles.
• the position of each acquisition means positioned on the patient is validated.
• the analysis of the information shared by the signals from each sensor of the instrumental network over a long time scale characterizes the coupling between the activities of the various muscles explored and in particular their temporal relationship.
• the information exchange measurement is described by Pompe B. et al. in the article entitled "Using Mutual Information to Measure Coupling in the Cardiorespiratory System.” IEEE Eng Med Biol., Mag 1998, 17: 32-39.
• This information exchange measure takes into account the non-linearity of the mechanisms and is based on Shannon's entropy. For that, one makes an analysis on the envelopes of the signals obtained by straightening and integration and on sliding windows of parametrizable duration. Thus, we elaborate in a manner known per se as the average envelope of each muscular signal acquired over the plurality of respiratory cycles and a coherence score or coupling level is calculated between each pair of muscular signals. Finally, each calculated consistency score is compared to a threshold value, previously determined by the operator.
• the two muscular signals are well correlated, which validates the position of the two acquisition means on the respiratory muscles of the patient.
• the muscular signals are non-coherent, while the operator modifies the position of at least one acquisition means.
• the operator can change the position of at least one acquisition means on the muscle where he was already positioned, or move it to another respiratory muscle or remove it.
• the preceding steps are performed again until the relevant muscle signals are obtained, that is, all correlated with each other and participating in respiratory muscle activity of the patient.
• the obtaining of relevant muscular signals validates the position of all the acquisition means used on the patient.
• the validation of the position of the sensors is performed manually by the operator.
• step 16 of validating the position of all the acquisition means one proceeds to the step 24 of calibration or qualification of the apparatus for detecting the respiratory muscle activity of the patient.
• a respiratory muscle muscular activation model of the patient as equipped with the acquisition means 6 is defined.
• the calculation of the correlation functions of the muscular signals defines a temporal order of activation of the different respiratory muscles.
• the mean delay between two events identified in two signals is determined 26 for all the muscular signals.
• the measurement of the average delays more specifically related to the beginning of the activities of the various signals representative of the organization of a respiratory event, such as the inspiratory request is obtained by the coherent accumulation method Respiratory Physiology & Neurobiology 2006, Hug F. et al. " To implement this method, an additional measure of flow is also used.
• a standard criterion is used to determine the onset of muscle activation, namely: the timing of an activation event in a signal is determined by calculating the local derivative or slope of the signal. the signal envelope and compare its value to a reference value chosen by the operator.
• FIG. 3 illustrates the intensity as a function of time of three acquired signals S A , S B and S c on different muscles A, B, C.
• the order indicates the activation of muscle A before that of muscle C and finally that of muscle B at the respective instants t A , t B and t c .
• the average delays are noted ⁇ t A B, ⁇ t A c and ⁇ t C B-
• This method can also be implemented using as an event the end of the intensity of activation of the muscles or a variation of muscular pressure.
• the set of average delays / delays and the temporal order of activation of the respiratory muscles defines a model of respiratory muscle activity (AMR) of the patient also called activation identification model and generally represented by a chronogram.
• AMR respiratory muscle activity
• a temporal detection window is defined based on these average delays / delays, for example the beginning of the time detection window coincides with the first identified event and its duration is the largest average delay, i.e. say between the first and the last identified activation event of the respiratory muscles.
• the calibration step 24 of the apparatus for detecting the respiratory muscle activity of the patient is then completed.
• This step 24 qualifies the capacity of the network of sensors 6 distributed on different muscles involved in the respiratory activity of the patient to demonstrate a model of activation of the respiratory muscles of the patient 1. It allows the choice of the most relevant routes for the qualification of the network and finally identifies the scale of analysis time necessary for the characterization of respiratory events, and in particular inspiratory events.
• the system Once the system is qualified and calibrated, it can be used to monitor the patient's respiratory muscle activity in real time.
• the time detection window is updated to monitor the respiratory muscle activity of the patient at each new respiratory cycle.
• the average delays over the last M successive respiratory cycles between the acquired muscle signals are calculated by averaging over the last M values of the delays between each couple of muscle signals.
• the number M is determined by the operator.
• the number of cycles of follow-up is less than the number of cycles of treatment.
• it is between 8 and 12 respiratory cycles and preferably M is equal to ten.
• the values of these delays can be compared to the values calculated during the calibration step or to reference values determined by the operator to detect an anomaly, and in case of anomaly a signal can be detected. be emitted 38 in a sound and / or visual manner.
• This monitoring of the respiratory muscle activity of the patient is useful to the anesthesiologist and provides information on the patient's condition.
• the clinical information that can be extracted is, for example, disorganization of the respiratory function, respiratory discomfort or anesthetic state.
• the device for detecting respiratory muscle activity is then an apparatus for exploring the respiratory functioning of the patient in real time.
• the apparatus for detecting respiratory muscle activity also comprises complementary processing means comprising means 40 for acquiring a signal representing a ventilatory activity of the patient and means for receiving this ventilatory signal.
• This signal includes a ventilatory event.
• these ventilatory acquisition means comprise at least one air flow sensor installed at the level of the patient's mouth.
• the additional processing means further comprise means for detecting in real time a ventilatory request.
• the ventilatory demand is defined by an ordered succession of the ventilatory event and the respiratory events identified thanks to the model of respiratory activity and detected in the acquired muscular signals.
• the additional processing means also comprise means for generating and transmitting a control signal to a respiratory assistance device 44 connected to a patient.
• This apparatus implements the method illustrated in FIG. 5 and detailed below.
• the preliminary steps to perform the calibration 24 of the device are identical to those described above.
• Instrumental network calibration or qualification 24 provides a window adapted to the patient's behavior for the robust detection of a respiratory, preferably inspiratory, demand, such as the combination of unit respiratory events in a given detection window. This combination can correspond to a temporal sequence of unit activations and / or a logical combination of these different activations.
• Calibration 24 of the apparatus makes it possible to define the respiratory muscle activation model of the patient as equipped with the acquisition means.
• This model can be used to robustly detect a real-time respiratory event, preferably an inspiratory patient request, and thereby trigger air insufflation to the patient by a ventilatory assist device.
• any detection of the inspiratory request must be performed in a time less than an electro-mechanical delay which will be defined for example, as the difference between the objective perception of the inspiratory activity through the sensor network and the passage through zero of the patient's airflow.
• ventilatory acquisition means 40 installed on the patient 4, for example near his mouth.
• This measurement of ventilatory activity 50 and the detection of respiratory muscle activity 52 are used to generate a control signal of the respiratory assistance apparatus 44. Indeed, when an inspiratory request is detected in a time less than the electromechanical delay, a control signal is emitted to the respiratory assistance device 44 to trigger the insufflation of air to the patient.
• the measurement of the respiratory muscle activity and the detection of the respiratory event are used to detect an anomaly in the operation of the respiratory assistance device, for example a desynchronization between the previously programmed apparatus and the patient.
• the accuracy of the detection of an inspiratory activity is lower than the electromechanical delay, it can be used to evaluate the quality of the adjustment between the inspiratory activity of a patient and his device. 'respiratory support.
• an audible or visual warning signal is emitted to warn the patient. operator.
• the system 2 comprises means for acquiring information relating to the state of the respiratory assistance apparatus and means for comparing this information relating to the respiratory assistance apparatus and the detection of the respiratory activity of the patient, such as an inspiratory request.
• the method provides the detection of events in the time detection window from the functional activity model, which is a sliding window, and it will understand that the device comprises means for detecting events in the detection window.
• a system and a method according to the invention make it possible to ensure a robust measurement of the respiratory muscle activity of a patient from non-invasive measurements on several muscles and in real time, in particular to control the triggering of a respiratory assistance device.
• This solution has several advantages over previous methods. On the one hand, it provides robust detection of inspiratory demand from non-invasive sensors while retaining the temporal accuracy gained for detection on a single signal. And on the other hand, it represents an adaptive solution of the detection which takes into account the diversity and revolution of the mode of respiratory muscular activation of the patient and the instrumental capacity of the network of sensors developed for the functional exploration.
• a device for detecting respiratory muscle activity can be used, for example, to provide a decision aid for weaning the respiratory assistance of patients with vigilance disorders. Indeed, in such patients, the lengthening of the activation delay is associated with a deterioration in the efficiency of the respiratory muscle activity and the need to maintain the mechanical respiratory assistance.
• the device and the method according to the invention thus make it possible to help to take the right direction. decision.
• the invention has been described in the context of detecting an inspiratory request from the patient. However, it obviously applies to the detection of any other respiratory event, for example an expiratory request from the patient.
• the invention is applicable to other fields, for example in motor rehabilitation, pathology of aging and / or cardiology and, more generally, all areas where a "functional activity model" and its alteration in pathology can be identified.
• the means for acquiring the signals include means for acquiring cardiac muscle signals or skeletal muscle signals.

Landscapes

• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Medical Informatics (AREA)
• Biophysics (AREA)
• Pathology (AREA)
• Engineering & Computer Science (AREA)
• Biomedical Technology (AREA)
• Heart & Thoracic Surgery (AREA)
• Physics & Mathematics (AREA)
• Molecular Biology (AREA)
• Surgery (AREA)
• Animal Behavior & Ethology (AREA)
• General Health & Medical Sciences (AREA)
• Public Health (AREA)
• Veterinary Medicine (AREA)
• Physiology (AREA)
• Pulmonology (AREA)
• Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
• Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=41124196

Family Applications (1)

Country Status (6)

Families Citing this family (3)

Citations (1)

Family Cites Families (17)

• 2009
  • 2009-04-07 FR FR0952277A patent/FR2943902B1/fr not_active Expired - Fee Related
• 2010
  • 2010-04-06 EP EP10723211A patent/EP2416704A1/de not_active Withdrawn
  • 2010-04-06 WO PCT/FR2010/050661 patent/WO2010116087A1/fr active Application Filing
  • 2010-04-06 US US13/263,180 patent/US8897867B2/en not_active Expired - Fee Related
  • 2010-04-06 CN CN201080020557.1A patent/CN102438516B/zh not_active Expired - Fee Related
  • 2010-04-06 CA CA2758227A patent/CA2758227A1/fr not_active Abandoned

Patent Citations (1)

Non-Patent Citations (2)

Also Published As

Similar Documents

Legal Events