WO2008132736A2 - Procédé et dispositif permettant de caractériser le sommeil - Google Patents

Procédé et dispositif permettant de caractériser le sommeil Download PDF

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WO2008132736A2
WO2008132736A2 PCT/IL2008/000562 IL2008000562W WO2008132736A2 WO 2008132736 A2 WO2008132736 A2 WO 2008132736A2 IL 2008000562 W IL2008000562 W IL 2008000562W WO 2008132736 A2 WO2008132736 A2 WO 2008132736A2
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data
ecg
sleep
threshold
determining
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WO2008132736A3 (fr
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Armanda Baharav
Zvi Shinar
Shulamit Eyal
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HypnoCore Ltd
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HypnoCore Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
    • 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
    • A61B5/4812—Detecting sleep stages or cycles
    • 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
    • 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
    • A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems

Definitions

  • the present invention relates to sleep disorders. More specifically the present invention relates to a method and a device for characterizing sleep.
  • NREM sleep is subdivided into four stages, which are enumerated from Stagel to Stage 4, in accordance with an increasing threshold to the arousing influence of external stimuli. These stages are also known as the depth of sleep.
  • OSA Obstructive Sleep Apnea
  • Insomnia as a primary disorder has a prevalence of about 10% of the general population. According to the "2005 sleep in America poll" by the National Sleep Foundation, 54% of adult population reported that they experienced at least one symptom of insomnia at least several nights a week.
  • Determination of body position during sleep may also assist in diagnosing sleep disorders originating from frequent body position changes during sleep. Many sleep disorders, in particular snoring, sudden infant death syndrome and OSA, are position- dependent. Knowing the body position during sleep is important for study, diagnosis and treatment strategy of such sleep disorders.
  • PSG polysomnograph
  • EEG electroencephalogram
  • EOG electrooculogram
  • EMG electromyogram
  • EEG signals are derived primarily from the cortex of the brain.
  • an EMG signal which monitors muscle activity, generally from some of the muscles of the head (i.e. submental) is measured, together with left eye and right eye EOG (signals produced by eyeball movements relative to the skull).
  • EEG, EMG and EOG signals are conventionally recorded on a multi-channel physiological recorder (digital polysomnograph).
  • Sleep related respiratory events are commonly detected based on the following signals: nasal pressure or oronasal flow, respiratory inductive plethysmography (RIP), respiratory effort (1-2 leads) or inductive belts, oxygen saturation, electrocardiogram (ECG), body position and a microphone for snoring.
  • Apnea or hypopnea events require a reduction of at least 50% in the respiration signal, or a lesser reduction associated with either cortical arousal or a >3% reduction in the oxygen saturation signal [American academy of sleep medicine task force. "Sleep related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research.” Sleep 1999, 22: 667-689].
  • the detection of respiratory events is considered in relation to sleep / wake stages and arousals obtained from the analysis of the three signals mentioned in the previous section.
  • apnea screening tests are partially accepted, using only one or two channels recordings, typically pulse oximetry and/or respiratory movements recording using oronasal pressure, oronasal thermistor, abdomen or thorax piezoelectric or inductive sensors.
  • These screening tests have many limitations, primarily they suffer from low sensitivity, and hence are recommended only as confirmation for suspected OSA, and are not accepted for ruling out OSA in a symptomatic patient [Netzer N et al, "Overnight pulse oximetry for sleep disordered breathing in adults.” Chest 2001, 120: 625-633].
  • sleep parameters e.g. the electrical activity of the heart (e.g. ECG), and pulse oximetry (SpO2 and pulse wave) signals, with or without one or more EMG (electromyogram) signals
  • a method for determining an incident of a sleep related respiratory event, from ECG data and oximetry data comprising: [0014] determining occurrence of an attenuation in ECG derived respiratory signal below a first threshold,
  • the method further comprises using EMG data to classify the sleep related respiratory event as a central sleep related respiratory event or obstructive sleep related respiratory event.
  • the method further comprises determining sleep stages using the ECG data and pulse transit time derived from the ECG data and a pleth data.
  • the method further comprises determining sleep stages using EMG data, ECG data and pulse transit time derived from the ECG data and a pleth data.
  • the method further comprises receiving the ECG data and the oximetry data from a remote user over the Internet.
  • the method further comprising providing information on the sleep related respiratory event over the Internet to the user.
  • a device for determining an incident of a sleep related respiratory event comprising:
  • an ECG sensor [0026] a oxygen saturation sensor
  • a processor provided with an algorithm for determining an incident of a sleep related respiratory event, from ECG data and oximetry data, the algorithm comprising:
  • the device further comprises an EMG sensor.
  • the algorithm of the processor further comprises using EMG data to classify the sleep related respiratory event as a central sleep related respiratory event or obstructive sleep related respiratory event.
  • the algorithm of the processor further comprises determining sleep stages using EMG data, ECG data and pulse transit time derived from the ECG data and a pleth data.
  • the algorithm of the processor further comprises determining sleep stages using the ECG data and pulse transit time derived from the ECG data and a pleth data.
  • the device is further provided with a communication link wherein the algorithm of the processor further comprises receiving the ECG data and the oximetry data from a remote user over the Internet using the communication link.
  • the algorithm of the processor further comprises providing information on the sleep related respiratory event over the Internet to the user using the communication link.
  • the device further comprises an amplifier for amplifying signals received from the sensors.
  • said oxygen saturation sensor is selected from a group of sensors consisting of a pulse waveform sensor and SpO2 sensor.
  • Fig. 1 illustrates a block-diagram of the information processing path in a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • FIG. 2 illustrates a block-diagram of the information processing path in an internet web-based device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • FIG. 3 illustrates a block-diagram of the main steps preformed by a processor in a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig. 4 is a flow chart of preliminary processing of the data by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig. 5 is an illustration of RR interval and pulse transient time (PTT) for understanding the definition of the terms used in the text.
  • Fig. 6 is a flow chart of oximetry data validity-check by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig. 7 is an illustration of an example of pleth and ECG graphs for performing a validity check by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig. 8 is a flowchart describing the detection of arousal and awakening events by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig. 9 is an illustration of an example of supporting indication of an arousal event based on a decrease in a pulse wave amplitude (PWA) parameter by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig 10 is an illustration of an example of supporting indication of an arousal event based on an increase in submental EMG amplitude by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • PWA pulse wave amplitude
  • Fig 11 is a flowchart describing the first part of detection of respiratory and de- saturation events by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig. 12 is a flowchart describing the second part of detection of respiratory and desaturation events that are described in Fig. 11.
  • Fig. 13a is an illustration of an example of detection of obstructive respiratory events from EMG amplitude by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • FIG. 13b is an illustration of an example of detection of central respiratory events from EMG amplitude by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig. 14 is a block-diagram describing the evaluation of sleep stages by a device for the characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • Fig. 15 is a flow chart describing the incorporation of the information based on respiratory and de-saturation events, sleep stages and arousal events by a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • the present invention relates to a method, device, and system for characterizing sleep, and more particularly, to a method, device, and system for an efficient determination of wake and sleep stages, as well as sleep related respiratory events (also known as apnea and hypopnea events or breathing disorder) and their severity, using data derived from signals of electrical activity of the heart, such as electrocardiogram (ECG), and photoplethysmography (PPG) based signals, such as pulse oximeter and an option to include data derived from one or more electrical activity of muscles, i.e electromyogram (EMG).
  • ECG electrocardiogram
  • PPG photoplethysmography
  • An embodiment of the present invention discloses a method, device, and system that combine acquisition of the electrical activity of the heart (e.g. ECG), and pulse oximetry (SpO2 and pulse wave) signals, with or without one or more EMG (electromyogram) signals and enables the accurate characterization of sleep, detection of sleep stages, sleep related respiratory events, awakenings and arousals, and body position.
  • ECG electrical activity of the heart
  • SpO2 and pulse wave pulse wave
  • the following can be derived, during sleep: sensor reliability, oxygen saturation levels, desaturation events, and arousals from the pulse waveform envelope.
  • sensor reliability By integrating data recorded in parallel from the electrical activity of the heart with data from the oxygen saturation and pulse wave of a sleeping person the following additional information or improvements can be derived: better estimation of the oxygen sensor reliability, pulse-transient-time (PTT) for estimating sympathetic nervous system (SNS) activity, better characterization of sleep stages, better characterization of arousals and awakenings - hence having better estimate of insomnia, better characterization of respiratory events and estimating the severity of Obstructive Sleep Apnea Syndrome (OSAS) by cross referencing respiratory data series with arousals and desaturations.
  • PTT pulse-transient-time
  • SNS sympathetic nervous system
  • OSAS Obstructive Sleep Apnea Syndrome
  • Adding EMG information recorded from any respiratory muscle to the above data can further improve the characterization of respiratory events by distinguishing between central and obstructive origin of each respiratory event.
  • Adding EMG information recorded from any muscle that lowers its tonus during REM stage to the ECG, SpO2 and pleth data can further improve the characterization of sleep stages and arousals.
  • An embodiment of the present invention provides information on sleep quality and architecture, thus allows for improved diagnosis of sleep related breathing disorder in comparison with screening devices based on pulse oximetry. It also provides much more specific details compared with ECG based sleep analysis devices.
  • oximetry pulse waveform pulse wave
  • pulse wave pulse wave
  • pleth pulse wave
  • Spiratory events respiratory disorder
  • breathing disorder apnea and hypopnea events which can be of central, obstructive or mixed origin.
  • arousals and “arousals and awakenings” events are used interchangeably herein.
  • the main difference between awakenings and arousals is at the scale at which these non-sleep periods affect the ECG data.
  • Respiratory events are better identified by integrating in the ECG analysis information obtained from the SpO2 data.
  • Further improvement in the characterization of respiratory events can be obtained by classifying the sleep related respiratory event as a central sleep related respiratory event or obstructive sleep related respiratory event by adding EMG information from respiratory muscle to the above information.
  • Fig. 1 illustrates a block-diagram of the information processing path in a device for characterizing the physiological states of a sleeping person in accordance with an embodiment of the present invention.
  • An acquisition device (10) is composed of several sensors and amplifier (20).
  • the different sensors record simultaneously the electrical activity of .the heart (e.g. ECG) (12), pulse waveform (pleth) (14), oxygen saturation level in arterial blood (SpO2) (16), and, optionally, record one or more muscle tone (EMG signal) (18).
  • the sensor for recording the activity of the heart is composed of at least 2 electrodes and one reference electrode.
  • the acquiring of the pleth and SpO2 is usually done by the same detector, i.e. a photo-plethysomograph (PPG) operating with red and infrared light capable of recording percent of oxygenated blood (SpO2) and pulse waveform (pleth).
  • the sensor for recording a single EMG signal is composed of two electrodes capable of recording electrical activity of a muscle.
  • the amplifier (20) is capable of amplifying, digitizing and storing the data received from the acquisition unit (10).
  • Preferred digitization of the electrical activity of the heart is a sample rate of 300Hz and quantization of 0.5 ⁇ V (relative to input).
  • Preferred digitization of the PPG includes averaging for 1 sec for the blood saturation, and 100Hz for pulse waveform.
  • Preferred digitization of the EMG is a sample rate of 300Hz.
  • the processor (22) is capable of applying the various algorithms, as shown in Fig. 3, on the digitized data and to produce a report that summarizes sleep analysis to be sent to an output device (
  • FIG. 2 illustrates a schematic block-diagram of a web based embodiment of the present invention.
  • acquisition devices (10) (of which a single device is illustrated in Figure 1) are shown connected to the internet web.
  • Each of the acquisition devices can transfer its recorded information via the web (26) to a processor (22).
  • the processor (22), as shown in Fig. 1, is capable of applying the various algorithms to the digitized data and to produce a report that summarizes the sleep analysis course.
  • the report can be directed via the internet web to one or more location provided with an output device (24).
  • Fig. 3 illustrates a block-diagram of the main steps preformed by the processor (22) described in Fig 1 of the device in an embodiment of the present invention.
  • the first step includes the preparation of the data series (100) (shown in Fig. 4), followed by detection of arousal and awakening events (200) (shown in Fig. 8), respiratory and desaturation events (300) (shown in Fig. 11 and 12), and sleep stages (400) (shown in Fig. 14).
  • the entire information sets that were detected are analyzed and incorporated (500) (shown in Fig. 15).
  • body position (BP) can be evaluated using a the same method as was used by Akselrod et al. (600) [0080]
  • Fig. 4 is a flowchart of the main elements included in the preparation of the data series (100) described in Fig. 3.
  • the ECG data typically location of the R wave (110) (shown in Fig 5), and the inter-beat interval is calculated to build the R-R interval (RRI) series.
  • the validity of the oximetry data i.e. the SpO2 and pleth data series, is checked (120) (shown in Fig. 6).
  • the next step includes the calculation of the pulse transient time (PTT) (130).
  • the PTT is the time difference between the position of the R wave and the following peak in the pleth data series (illustrated in Fig. 5). In case there is an EMG data series it is recommended to remove the ECG artifact that contaminates it (150).
  • FIG. 5 is an illustration of an RR interval (RRI) and pulse transient time (PTT) for the understanding the definitions of the terms used in the text.
  • the figure shows ECG tracing (123) of 4 heart beats (designated 127a, 127b 127c and 128d) and the corresponding pleth tracing (125).
  • ECG tracing (123) of 4 heart beats (designated 127a, 127b 127c and 128d) and the corresponding pleth tracing (125).
  • the first upward deflection within the sharp complex in the ECG is denoted as the R wave.
  • the peak of this wave is the R wave location (and labeled as 'R').
  • the time difference between consecutive R-s is defined as the RR interval.
  • This interval is inversely related to the instantaneous heart rate.
  • PTT is the time elapsed from the peak of the R wave in the ECG to the corresponding peak in the pleth data.
  • Fig. 6 is a flowchart describing oximetry data validity check procedure (120) described in Fig. 4. Note the algorithm-function checks the validity of each small segment of the data series.
  • the first step checks the validity of the SpO2 data (121) by looking for invalid values or extreme slopes. For example, SpO2 values (which are measured in percentage) of above 100% or below 50% are disregarded. In addition, SpO2 data with local slope of above 10% per second are disregarded. Failing to pass the SpO2 validity test automatically disqualifies the corresponding pleth segment (122). Then an autocorrelation between pleth segments is done (124). The autocorrelation is done between pleth segments that correspond to consecutive beats as defined based on the R wave position (illustrated in Fig.
  • FIG. 7 is an example of oximetry validity check. This figure concentrates only on the validity check of the pleth data.
  • the figure shows the pleth and ECG data in the upper and lower panels (129 and 131 respectively) as a function of time.
  • the R wave locations are indicated as circles on the ECG time series (in 131).
  • the arrows connect each R wave peaks and its corresponding pleth peak (in 129).
  • the autocorrelation of the pleth data was done by comparing the pleth data that corresponds with a specific beat (its location was defined based on the ECG) relative to the pleth data of previous beat. If the autocorrelation value is below a certain threshold, for example zero, this pleth segment is disregarded.
  • any section of 10 beats which includes at least 4 disregarded segments (each segment at a size of a single beat) of pleth will result in disregarding the entire 10 beats.
  • Such region will be defined as 'bad' region both for SpO2 and pleth data and their data will be disregarded.
  • An example of a region with 'bad' pleth data can be seen in the figure in the region between the time measurements 4186 and 4192 seconds.
  • Fig. 8 is a flowchart describing the detection of arousal and awakening events (200, shown in Fig. 3) from data of electrical activity of the heart (e.g. ECG), pulse oximetry and if available, relevant EMG (obtained from muscle that lower its tonus during REM stages).
  • the main difference between awakenings and arousals is at the scale at which these non- sleep periods affect the ECG data.
  • the awakening periods which are typically characterized by trace duration of at least 15 seconds, affect the ECG data in the low frequencies region while the arousals periods, which are typically characterized by trace duration of 3-15 seconds, affect the ECG data in the intermediate-high frequencies region.
  • the initial part includes arousal detection based on ECG data (210).
  • the RRI series is filtered using a low-pass- filter thereby providing a first series of data.
  • the RRI series is filtered using a band-pass-filter thereby providing a second series of data.
  • a typical cutoff frequency for the low-pass-filter is about 0.01 Hz, and typical cutoff frequencies of the band-pass-filter are 0.05 Hz for the low limit and about 0.2 Hz for upper band limit.
  • Awakening periods are defined as a plurality of beats each associated with at least one of the first series of data which is below a predetermined threshold.
  • Arousal periods are defined as a plurality of beats each associated with at least one of the second series of data which is below a predetermined threshold.
  • the calculation may result in positive detected arousals periods and suspected periods, defined based on the threshold used. Each of these events follows several tests (215) prior to acceptance as valid arousal (230). Typical thresholds for positively identifying the awakening and arousals events are at 0.85 of the averaged value of the first series and the second series of data, respectively.
  • Arousal events can also be identified by weaker indication obtained from the ECG, for example using a threshold of 0.9 of the averaged value in either of the data series (first or second filtered data), which are accompanied by a decrease in the pleth's amplitude (240).
  • the local pulse wave amplitude (PWA) is defined as the difference between the local maximum and the local minimum of the pleth data, an example of the time window for calculating the local maximum (or minimum) can be 1.2 times the average RRI of the entire data.
  • a considered decrease in the PWA can be defined as plurality of beats each associated PWA values which are below a predetermined threshold. Such threshold can be about 0.7 of the averaged value of PWA of the preceding region.
  • weak indications of arousal or awakening events from the ECG for example using a threshold of 0.9 of the averaged value in either of the data series (first or second RRI filtered data), can also be identified as an event if they are accompanied by an increase in the EMG (250).
  • the increase in EMG data can be observed for example using the calculation of the mean rectified of the EMG data (mrEMG).
  • mrEMG is defined as the moving average of the absolute value of the amplitude of the EMG data.
  • a considered increase in the EMG data can be defined as increase in the mrEMG above a predetermined first threshold for a short period surrounded by a longer period in which the mrEMG is above a second (lower) threshold.
  • Such thresholds can be 3 times and 1.5 times the average mrEMG values of the preceding period for the first and second threshold respectively.
  • Recommended periods can be 1 and 3 seconds to be used in the first and second threshold respectively.
  • Fig. 9 is an example of the pulse wave amplitude (PWA) (245) as a function of time prior and during an arousal event.
  • the PWA series is calculated as the difference between the local maximum and local minimum of the pleth data in which the window for the local calculation is 1.2 times the average RRI of the entire data.
  • the dashed line (246) indicates the beginning of the arousal event. The decrease in the PWA series at the time of the arousal event relative to the time prior to the event can be easily seen.
  • Fig. 10 is an example of the submental mean rectified EMG (mxEMG) (255) as a function of time prior and during an arousal event.
  • the mrEMGT is defined as the moving average of the absolute amplitude of the EMG data.
  • the dashed line (256) indicates the beginning of the arousal event. The increase in the mxEMG series at the time of the arousal event relative to the time prior to the event can be easily seen.
  • Fig. 11 is a flowchart describing the first part of the detection of respiratory and desaturation events (300, shown in Fig 3).
  • apnea can be classified using a reduction in a respiratory time series with desaturation or arousal.
  • the first stage in the evaluation is the detection of desaturation events based on valid SpO2 data (310).
  • a desaturation event is defined as a decrease in SpO2 below a predefined threshold relative to baseline value.
  • An example of such threshold can be a decrease of 3%.
  • EDR ECG derived respiratory
  • EDR can be extracted from the waveform parameters of the ECG according to Moody et al. [Moody G.B., Mark R.G., Zoccola A., and Mantero S. (1985): "Derivation of respiratory signals from multi-lead ECGs", Comp. Cardiol., 12, pp. 113-6].
  • an EDR attenuation parameter which is the minus of the ratio between a moving characteristic of the amplitude of. the EDR over a first time window relative to the moving averaged amplitude of the EDR over a second time window.
  • the moving characteristic can be a predefined percentage of EDR amplitude, such as the percentage 85. It is recommended that the first time window will be larger than the second time window, for example the length of 20 averaged breathing periods for the first time window and the length of 1.5 average breathing periods for the second window. The depth of the attenuation is thus defined by this ratio
  • Respiratory events are then detected based on several criteria (317) that combine the information from prior calculations. Respiratory events are identified (325) by any of the following: [0095] Deep attenuation as expressed by low EDR attenuation parameter below a predefined first threshold (320), in which the first threshold might be -4.
  • Deep desaturation event below a predefined second threshold (330), in which the second threshold might be 4.
  • the second threshold might be 4.
  • Fig. 12 is a flowchart describing the second part of the detection of respiratory and desaturation events. Following the detection of respiratory events (show in Fig. 11) there is a further analysis executed for each event (317) that can be preformed only if relevant EMG data exist, i.e EMG data related to respiratory muscle (355). If there is an increase in the EMG data, as characterized for example by the mrEMG (the moving average of the absolute amplitude of the EMG data) above a predefined threshold, such as 3 times the original value, (365) at the same time as the respiratory event, the event is defined to rise from an obstructive source (370, illustrated in detail in Fig. 13a).
  • mrEMG the moving average of the absolute amplitude of the EMG data
  • a predefined threshold such as 3 times the original value
  • FIG. 13a and 13b are examples of detection of obstructive respiratory events and detection of central respiratory events (designated 370 and 380 in Fig. 12) from the mean rectified EMG (mrEMG) time series (377) as a function of time.
  • mrEMG is defined as the moving average of the absolute amplitude of the EMG signal. The obstructive events are seen as an increase in the mrEMG when compared to a decrease in the mrEMG in central events.
  • Fig. 14 is a block-diagram describing the evaluation steps of sleep stages
  • the sleep stages that are identified in accordance with an embodiment of the present invention are: wake, REM sleep and Non-REM sleep which is further divided into two 2 stages.
  • Light sleep (LS) stage which combines Non-REM stages 1 and 2
  • slow wave sleep (SWS) stage which combines Non-REM stages 3 and 4.
  • the sleep stages classification is based on calculation of several parameters of the ECG 5 RRI, PTT and if available, relevant EMG data.
  • the first step includes the evaluation of waveform, time domain and frequency domain parameters of the ECG and RjRI signals (410).
  • the ECG waveform parameters includes the extraction of left R wave duration (L- RWD), right R wave duration (R-RWD) and R wave amplitude (RWA) for each R wave.
  • the L-RWD is defined as the time duration between the inflection point just prior to the R wave fiducial point and the R wave fiducial point.
  • the R-RWD is defined as the time duration from the R wave fiducial point and the inflection point just following it.
  • the RWA is the amplitude of the R wave with reference to the minimal value out of the local minimal values obtained in either of its sides.
  • the RRI time domain parameter is a nonlinear parameter indicated as BQ, which is the balance between the number of points in the odd and even quartiles in the phase space constructed by two adjacent RRI values (i.e. Poincare plot of RRI).
  • the RRI frequency parameters are obtained by a time-frequency decomposition (e.g. wavelet analysis) that is performed on the RRI series.
  • the output of such analysis include several frequency domain parameters, that reflect the activity of the sympathetic and parasympathetic nervous system, such as the power of the RKI series in different frequency ranges as a function of time.
  • the recommended frequency bands are very low frequency (VLF) at 0.008-0.04Hz, low frequency (LF) at 0.04-0.15Hz, and high frequency (HF) at 0.15-0.5Hz.
  • VLF very low frequency
  • LF low frequency
  • HF high frequency
  • the recommended frequency band for this frequency band is the same as for the LF of the RRI i.e. 0.04-0.15Hz.
  • CLF is known to correlate with sympathetic nervous system activity.
  • EMG time domain parameters are mrEMG (defined above), zero crossing frequency (ZC), defined as the number of times the signal crosses 0 level during a predefined period, and turns which is defined as the number of times the signal derivatives crosses zero level or the number of times the signal changes direction during a predefined period.
  • ZC zero crossing frequency
  • the recommended time period for ZC and turns parameters is 30 seconds.
  • the EMG frequency parameters are the normalized power (nPWR), defined as the mean of the power spectrum of the signal, and PVAR, defined as the variance of the absolute value spectral coefficients of the signal.
  • the Bayesian classifier (440) uses a priori probabilities of different sleep stages and a database of these parameters that were calculated for known wake / sleep states, to determine current sleep and wake stages for the whole duration of the recording.
  • Fig. 15 is a flowchart describing the incorporation of the information based on respiratory and desaturation events, sleep stages and arousal events (designated as 500 in Fig. 3).
  • a desaturation events occurred while the patient was at a wake stage (510) the desaturation event is disregarded (520). Similar, if a respiratory event occurs while the patient was at a wake stage (530) the respiratory event is disregarded (540).
  • the arousal events may be attributed to a single long event (560).

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Abstract

La présente invention concerne un procédé et un dispositif permettant de déterminer un incident correspondant à un événement respiratoire associé au sommeil, à l'aide de données ECG et de données oxymétriques. Le procédé consiste à : déterminer l'apparition d'une atténuation d'un signal respiratoire à partir d'un ECG en dessous d'un premier seuil ; déterminer l'apparition d'une désaturation dans les données oxymétriques en dessous d'un deuxième seuil ; déterminer l'apparition d'une atténuation d'un signal respiratoire à partir d'un ECG en dessous d'un troisième seuil, accompagnée d'une désaturation correspondante dans les données oxymétriques en dessous d'un quatrième seuil ; déterminer l'apparition d'une atténuation dans un signal respiratoire à partir d'un ECG en dessous d'un cinquième seuil, accompagnée d'une détection de l'éveil ; et déterminer l'incident correspondant à un événement respiratoire associé au sommeil si l'une quelconque de ces apparitions existe.
PCT/IL2008/000562 2007-05-01 2008-04-28 Procédé et dispositif permettant de caractériser le sommeil Ceased WO2008132736A2 (fr)

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WO2017040333A1 (fr) * 2015-08-28 2017-03-09 Awarables, Inc. Visualisation, évaluation, enregistrement et analyse de données de sommeil et d'hypnogrammes
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