WO2006008743A2 - Indicateurs de la qualite du sommeil - Google Patents

Indicateurs de la qualite du sommeil Download PDF

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Publication number
WO2006008743A2
WO2006008743A2 PCT/IL2005/000776 IL2005000776W WO2006008743A2 WO 2006008743 A2 WO2006008743 A2 WO 2006008743A2 IL 2005000776 W IL2005000776 W IL 2005000776W WO 2006008743 A2 WO2006008743 A2 WO 2006008743A2
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segments
frequency
sleep
states
plot
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PCT/IL2005/000776
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English (en)
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WO2006008743A3 (fr
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Koby Todros
Baruch Levy
Alex Novodvorets
Amir B. Geva
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Widemed Ltd.
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Priority to US11/572,481 priority Critical patent/US20090292215A1/en
Priority to EP05761327A priority patent/EP1779257A4/fr
Publication of WO2006008743A2 publication Critical patent/WO2006008743A2/fr
Publication of WO2006008743A3 publication Critical patent/WO2006008743A3/fr

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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
    • A61B5/48—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
    • A61B5/02—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
    • 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
    • A61B5/369—Electroencephalography [EEG]
    • 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
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • 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
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/384—Recording apparatus or displays specially adapted therefor
    • 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
    • A61B5/4815—Sleep quality
    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • A61B5/087—Measuring breath flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • 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
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • 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, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • 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
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof

Definitions

  • Patent Application 10/677,176, filed October 2, 2003 published as US 2004/0073098 Al.
  • the present invention relates generally to physiological monitoring and diagnosis, and specifically to sleep recording and analysis.
  • Human sleep is generally described as a succession of five recurring stages (plus waking, which is sometimes classified as a sixth stage). Sleep stages are typically monitored using a polysomnograph to collect physiological signals from the sleeping subject, including brain waves (EEG), eye movements (EOG), muscle activity (EMG), heartbeat (ECG), blood oxygen levels (SpO2) and respiration.
  • EEG brain waves
  • EEG eye movements
  • EMG muscle activity
  • ECG heartbeat
  • SpO2 blood oxygen levels
  • respiration respiration.
  • the commonly-recognized stages include:
  • Stage 1 sleep or drowsiness.
  • the eyes are closed during Stage 1 sleep, but if aroused from it, a person may feel as if he or she has not slept.
  • Stage 2 is a period of light sleep, during which the body prepares to enter deep sleep.
  • Stages 3 and 4 are deep sleep stages, with Stage 4 being more intense than Stage 3.
  • Stage 5 REM (rapid eye movement) sleep, is distinguishable from non-REM (NREM) sleep by changes in physiological states, including its characteristic rapid eye movements.
  • Polysomnograms show brain wave patterns in REM to be similar to Stage 1 sleep. In normal sleep, heart rate and respiration speed up and become erratic, while the muscles may twitch. Intense dreaming occurs during REM sleep, but paralysis occurs simultaneously in the major voluntary muscle groups.
  • sleep staging is widely accepted as the standard method for diagnosis and classification of sleep disorders, this method provides only coarse resolution and fails to exploit the wealth of information in the polysomnogram signals.
  • the inventors have found many cases in which traditional sleep stage analysis fails to uncover underlying sleep pathologies.
  • the inventors have developed a family of sleep quality indicators, which assist the diagnostician in recognizing sleep-related disorders.
  • a sleep analysis system acquires physiological signals, such as EEG signals, during sleep, and adaptively segments the signals to identify quasi-stationary segments.
  • the system automatically analyzes each segment to determine the relative energy in each of a number of frequency bands, and thus assigns the segments to different frequency states.
  • the states are defined for each patient by fuzzy clustering of features extracted from each segments; and each segment is assigned a degree of membership with respect to each of the states. Based on the fuzzy clustering and membership levels, the system determines and displays sleep quality indicators relating to the distribution of the segments among the clusters.
  • the system displays the results of the analysis so that changes in the distribution of states over time, in the course of a period of sleep, can be readily visualized by a caregiver, such as a medical sleep specialist. Additionally or alternatively, the system may display characteristic patterns of transition between different states.
  • system may calculate the fundamental frequency of each segment, typically expressed as the moment of the EEG power spectrum.
  • the fundamental frequency is then displayed so as to enable the caregiver to visualize changes in the trend and standard deviation of the fundamental frequency, which are indicative of continuous changes in the patient's sleep quality.
  • a method for diagnosis including: acquiring a physiological signal from a patient during a period of sleep; segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum; computing respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum; and displaying a plot indicative of the levels of membership of the segments in the sequence over time.
  • computing the respective levels includes applying fuzzy clustering to the segments so as to define the states.
  • displaying the plot includes displaying a density plot, in which the levels of membership are represented by color variations.
  • displaying the plot includes displaying an accumulation plot, showing cumulative levels of membership of the segments in the plurality of frequency states over the sequence.
  • displaying the plot includes displaying an accumulation plot showing cumulative durations of the segments in each of the plurality of frequency states.
  • the method includes determining and comparing respective accumulation rates of the cumulative durations in at least two of the frequency states.
  • displaying the plot includes assigning each of at least some of the segments to one of a waking state and a sleep state responsively to the frequency spectrum, and displaying an accumulation plot showing a cumulative assignment of the segments to the waking and sleep states over time.
  • a method for diagnosis including: acquiring a physiological signal from a patient during a period of sleep; segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum; computing a fundamental frequency of each segment in the time sequence responsively to a moment of the respective frequency spectrum of the segment; and displaying a plot showing the fundamental frequency of the segments in the sequence over time. Displaying the plot may include showing at least one of a trend and a variance of the fundamental frequency.
  • a method for diagnosis including: acquiring a physiological signal from a patient during a period of sleep; segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum; computing respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum; based on the respective levels of membership, determining a sleep quality indicator responsively to a statistical characteristic of the segments; and displaying the sleep quality indicator.
  • the statistical characteristic includes at least one of: a cumulative duration of the segments associated with each of the frequency clusters; a relative duration of the segments associated with each of the frequency clusters; a mean duration of the segments associated with each of the frequency clusters; a variance of a duration of the segments associated with each of the frequency clusters; a total number of the segments associated with each of the frequency clusters; and a relative duration of the segments associated with each of the frequency clusters.
  • the method includes assigning the segments to predefined sleep stages responsively to the frequency spectrum, and determining the sleep quality indicator includes computing the statistical characteristic with respect to each of the sleep stages.
  • displaying the sleep quality indicator includes displaying a plot indicative of the levels of membership of the segments in the sequence over time.
  • displaying the sleep quality indicator includes displaying a plot showing a fundamental frequency of the segments in the sequence over time.
  • computing the respective levels of membership includes assigning the segments in the time sequence to respective frequency states, and determining the sleep quality indicator includes computing probabilities of transition among the frequency states.
  • the physiological signal includes an electroencephalogram (EEG) signal.
  • EEG electroencephalogram
  • the method includes identifying transient phenomena in the EEG signal, and computing an index quantifying a frequency of occurrence of the transient phenomena.
  • the transient phenomena may include one or more of K-complexes and spindles.
  • the physiological signal may include a respiration signal.
  • the method includes identifying respiratory events occurring during the period of sleep, and computing statistical characteristics of the respiratory events. Typically, computing the statistical characteristics includes computing and displaying a respiratory event histogram.
  • the method includes measuring a heart rate of the patient, and computing the statistical characteristics includes computing a relative heart rate index indicative of changes in the heart rate associated with the respiratory events.
  • computing the statistical characteristics includes assigning respective confidence levels to the respiratory events, and displaying the confidence levels as a function of respiration state.
  • a method for diagnosis including: acquiring a physiological signal from a patient during a period of sleep; segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum; computing respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum; responsively to the respective levels of membership, assigning each of at least some of the segments to one of a waking state and a sleep state responsively to the frequency spectrum; and displaying an accumulation plot showing a cumulative assignment of the segments to the waking and sleep states over time.
  • the method may include determining and comparing respective accumulation rates of the waking and sleep states.
  • diagnostic apparatus including a sensor, which is adapted to acquire a physiological signal from a patient during a period of sleep, and a diagnostic processor, which is adapted to carry out the functions described above.
  • the sensor includes at least one electrode
  • the physiological signal includes an electroencephalogram (EEG) signal.
  • EEG electroencephalogram
  • the sensor may include a respiration sensor and/or a heart rate sensor.
  • a computer software product including a computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the computer to carry out the functions described above.
  • Fig. 1 is a schematic, pictorial illustration of a system for polysomnography, in accordance with an embodiment of the present invention
  • Fig. 2 is a flow chart that schematically illustrates a method for determining sleep quality parameters, in accordance with an embodiment of the present invention
  • Fig. 3 is a three-dimensional plot showing clusters of EEG frequency states, in accordance with an embodiment of the present invention.
  • Fig. 4A is a hypnogram, showing classification of sleep stages of a patient over time, in accordance with an embodiment of the present invention
  • Fig. 4B is a density plot showing a distribution of frequency cluster membership of successive segments of an EEG signal as a function of time for the patient of Fig. 4A, in accordance with an embodiment of the present invention
  • Fig. 5 A is a hypnogram, showing classification of sleep stages of another patient over time, in accordance with an embodiment of the present invention
  • Fig. 5B is a density plot showing a distribution of frequency cluster membership of successive segments of an EEG signal as a function of time for the patient of Fig. 5A, in accordance with an embodiment of the present invention
  • Figs. 6A and 6B are plots showing variations in the fundamental frequency of EEG signals over time, in accordance with an embodiment of the present invention.
  • Fig. 7 is a frequency state accumulation plot, showing cumulative frequency state durations of successive segments of an EEG signal over time, in accordance with an embodiment of the present invention
  • Fig. 8 is a sleep/wake state accumulation plot, showing cumulative durations of sleep and wake states of a patient over time, in accordance with an embodiment of the present invention
  • Fig. 9 is a transition matrix showing probabilities of transitions among frequency states in successive segments of an EEG signal, in accordance with an embodiment of the present invention.
  • FIG. 1 is a schematic, pictorial illustration of a system 20 for sleep monitoring and diagnosis, in accordance with an embodiment of the present invention.
  • system 20 is used to monitor a patient 22 in a home or hospital ward environment, although the principles of the present invention may similarly be applied in dedicated sleep laboratories.
  • System 20 receives and analyzes physiological signals generated by the patient's body, including an EEG signal measured by scalp electrodes 23, an ECG signal measured by skin electrodes 24, and a respiration signal measured by a respiration sensor 26. (As shown in the figure, respiration sensor 26 makes electrical measurements of thoracic and abdominal movement.
  • air flow measurement may be used for respiration sensing.
  • air flow measurement may be used for respiration sensing.
  • sensors may be used, and/or other sensors may be added, such as an EMG or SpO2 sensor, as are known in the art.
  • Console 28 may process and analyze the signals locally, using the methods described hereinbelow. Alternatively or additionally, console 28 may be coupled to communicate over a network 30, such as a telephone network or the Internet, with a diagnostic processor 32. This configuration permits sleep studies to be performed simultaneously in multiple different locations.
  • Processor 32 typically comprises a general-purpose computer with suitable software for carrying out the functions described herein. This software may be downloaded to processor 32 in electronic form, or it may alternatively be provided on tangible media, such as optical, magnetic or non- volatile electronic memory.
  • Processor 32 analyzes the signals conveyed by console 28 in order to identify sleep states of patient 22 and to extract sleep quality indicators. The results of the analysis are presented to an operator 34, such as a physician, on an output device 36, such as a display or printer.
  • Fig. 2 is a flow chart that schematically illustrates a method for determining sleep quality parameters, in accordance with an embodiment of the present invention.
  • the method and examples of sleep quality indicators given below relate mainly to processing of EEG signals. The principles of this method and the types of indicators derived therefrom, however, may similarly be applied to other sorts of signals, such as respiration and ECG signals.
  • Processor 32 acquires an EEG signal from patient 22, at a signal acquisition step 40. Typically, for sleep studies, the signal is acquired over the course of at least several hours. The processor then adaptively segments the signal into quasi-stationary segments, at a segmentation step 42. Adaptive segmentation is described at length in the above-mentioned U.S. patent applications.
  • processor 32 advances a sliding window, of variable size, through the EEG signal and evaluates statistical features of the signal within the window.
  • the statistical features typically include aspects of the frequency spectrum of each segment, which are determined by methods of spectral analysis known in the art.
  • the processor optimizes the window boundaries so as to envelope a segment that is statistically stationary to within a predefined bound.
  • the EEG signal is divided into a time sequence of quasi- stationary segments of varying length, separated by shorter transient periods.
  • This sort of adaptive segmentation is advantageous in that the segments that are chosen represent actual physiological states of the patient, as opposed to the arbitrary 30-second epochs that are used in conventional sleep scoring.
  • EEG signals normally comprise five major types of waves: (1) ⁇ -wave (1.0-3.5 Hz),
  • Each quasi-stationary segment typically comprises one dominant wave and possibly other frequency components superimposed on the dominant wave.
  • the frequency composition of the different types of segments determined at step 42 typically varies from patient to patient. Therefore, in order to classify the segments for each individual patient, processor 32 applies a fuzzy clustering algorithm to divide the segments into clusters, at a clustering step 44. Each cluster has a characteristic distribution of features, such as frequency components and overall segment energy. Methods of fuzzy clustering are likewise described in the above-mentioned patent applications. Fig.
  • FIG. 3 is a three-dimensional plot showing clustering of EEG frequency states, in accordance with an embodiment of the present invention.
  • the dots represent individual segments of an EEG signal, plotted on three axes corresponding to the following segment features: 1) Relative energy in the delta frequency band; 2) Relative energy in the alpha, sigma and beta frequency bands; and 3) Total segment energy, normalized to a scale of 0-100. Details of the features and clustering scheme are presented below in Appendix A. The inventors have found that this set of features provides useful differentiation between sleep states, but other sets of features, in two, three, or more dimensions may similarly be used for clustering purposes.
  • a high- frequency (HF) cluster 60 a low-energy mixed-frequency cluster 62 (MFl), a high-energy mixed frequency cluster 64 (MF2), and a low-frequency cluster 66 (LF).
  • HF high- frequency
  • MFl low-energy mixed-frequency cluster 62
  • MF2 high-energy mixed frequency cluster 64
  • LF low-frequency cluster 66
  • these clusters have been found to correlate respectively with deep sleep (stages 3 and 4), moderate sleep (stage 2), light sleep (stage 1/REM), and wakefulness.
  • other clustering models may be used, particularly in conjunction with other feature axes.
  • the bounds of each cluster are determined adaptively for each patient at step 44.
  • processor 32 computes membership levels with respect to each of the frequency states, at a membership computation step 46.
  • the processor determines the similarity of each segment to each of the clusters found at step 44.
  • the membership level of a given segment n having a feature vector X n may be computed relative to the center vectors ⁇ of the different clusters.
  • K is the number of clusters and D is a scalar function of distance between X n and ⁇ # .
  • H denotes the conjugate transpose operator.
  • other methods known in the art may be used for computing cluster membership.
  • the membership levels may be advantageously displayed as a function of time, as illustrated below in Fig. 4B.
  • the membership values determined at step 46 may be used by processor 32 in automatically assigning each 30-sec epoch during the monitoring period to one of the accepted sleep stages, at a sleep staging step 48. For example, the following scheme may be used, combining the states of the segments in the EEG signal with additional information from EMG and EOG signals:
  • Stage wake - Epochs more than 50% of whose duration are occupied by high-frequency EEG and/or body movements and/or eye blinks are classified as stage wake. Epochs that are not classified as stage wake are classified as sleep.
  • processor 32 typically computes sleep quality indicators, at a sleep quality assessment step 50. For example, for each of clusters 60, 62, 64 and 66 (referred to respectively as HF, MFl, MF2 and LF states), the processor may compute the following sleep quality parameters:
  • the sleep quality parameters may be computed over all the quasi-stationary segments identified at step 42, or alternatively over a selected sequence of the segments.
  • processor 32 may combine the segmentation data with the sleep staging performed at step 48 in order to compute the above parameters separately for each identified sleep stage or group of sleep stages.
  • the relative duration of the HF state in REM may be calculated as follows:
  • the relative number of HF segments in REM may be calculated as follows:
  • Figs. 4A and 4B show processed results of EEG measurements made in system 20, in accordance with an embodiment of the present invention.
  • Fig. 4A is a hypnogram, showing sleep stages of the patient over time, as derived from the polysomnogram signals at step 48.
  • Fig. 4B is a density plot showing the distribution of membership of the sequence of EEG segments in each of the four states defined above (HF, MFl, MF2 and LF).
  • density plot is used herein to denote a plot in which the color at a given point is indicative of the relative value of a parameter referred to the Cartesian coordinates of the point.
  • density plot for each point in time along the horizontal axis (corresponding to the segment of the EEG signal occurring at that time), four density values are arrayed vertically, corresponding to the degree of membership of the segment in each of clusters HF, MFl, MF2 and LF, which are arrayed along the vertical axis.
  • a density scale 70 at the bottom of the figure shows the correspondence between colors and normalized membership values. (“Color" in this context includes shades of gray.)
  • Fig. 4A contains much richer information about the EEG activity occurring at many points during the sleep period.
  • the inventors have found that the information contained in the density plot (which is lost in the discrete hypnogram) permits the caregiver to recognize abnormal sleep patterns that would otherwise go unnoticed. For example, in one clinical study, the inventors identified a group of patients whose hypnograms appeared to be normal, but who showed relatively high levels of HF membership during sleep. This result is indicative of sleep fragmentation, i.e., poor sleep quality in this group.
  • Figs. 5A and 5B respectively, show a hypnogram and a density plot for another patient, who was known to suffer from a sleep disorder.
  • a sleeping drug was administered to the patient, in an attempt to induce deep sleep.
  • the hypnogram it appears that drug was ineffective, since the patient's sleep stage never drops below stage 2.
  • a period of low-frequency activity at around 1 AM demonstrates the short-term efficacy of the drug.
  • Fig. 6A is a plot showing variations in the fundamental frequency of an EEG signal over time, in accordance with an embodiment of the present invention. This plot was derived from the EEG signal of the patient whose hypnogram and density plot are shown in Figs. 4A and 4B.
  • the fundamental frequency is determined for each segment by taking the moment of the frequency spectrum of the segment, as shown in Appendix A.
  • the solid line in the figure shows the fundamental frequency value, while the dotted marks above the solid line show the variance.
  • a line at 4 Hz shows the approximate boundary between deep sleep and other sleep stages.
  • the fundamental frequency correlates well with the hypnogram sleep stages, but provides richer information that is lost in the discrete hypnogram. This information may be further brought out, for example, by displaying a trend line and a range of standard deviation of the fundamental frequency over time (omitted from Fig. 6 A for the sake of simplicity). It will be observed in Fig. 6 A, for instance, that at some points changes in frequency are precipitous, while other changes are more gradual. These variations in slope, which are lost for the most part in the hypnogram, can be useful in assessment of clinical factors such as drug effects.
  • the fundamental frequency plot also permits the caregiver to observe local variability, even when the frequency trend (and hence the sleep stage) is flat. In this regard, note the difference between the smooth fundamental frequency plot in the neighborhood of 1 AM and the highly- variable plot at around 2 AM.
  • the fundamental frequency may be correlated with the patient's sleep stages.
  • processor 32 may calculate the average fundamental frequency, and possibly the variance of the fundamental frequency, over each of the sleep stages identified at step 48.
  • Fig. 6B is a plot of fundamental frequency taken from the EEG signal of the patient whose hypnogram and density plot are shown in Figs. 5A and 5B.
  • the fundamental frequency drops below the 4 Hz threshold only occasionally, if at all.
  • the effect of sleeping drug administration can be seen in the period of deep sleep at about 1 AM following administration of the drug, despite the negative hypnogram findings.
  • the precipitous frequency drop at 1 AM is followed by shallower, more gradual drops thereafter, reflecting the cyclical interaction of the drug with the sleep states of the brain.
  • Fig. 7 is a frequency state accumulation plot, showing cumulative duration of successive segments of an EEG signal over time, in accordance with an embodiment of the present invention.
  • Curves 80, 82, 84 and 86 respectively show the cumulative durations of HF, MF2, MFl and LF sleep states, as a fraction of the total sleep period.
  • the accumulation function Ac(t,s) for state s at time t is given by:
  • D denotes the duration in seconds
  • T is the total duration of all EEG segments.
  • D denotes the duration in seconds
  • T is the total duration of all EEG segments.
  • the duration of the segment is added to the cumulative duration of the state to which the segment belongs, while the cumulative durations of the other states remains unchanged.
  • cumulative membership values may be computed and displayed by integrating the above-mentioned membership function wi? over successive segments. Parameters that can be extracted in this manner include:
  • the estimated accumulation rate ⁇ for each curve is shown in the figure.
  • Changing trends in the state accumulation plot are indicative of changes and/or fragmentation of sleep states.
  • a knee 88 in HF curve 80 marks the point of transition from wakeful to sleeping states (occurring in this case about one hour after the beginning of the trial).
  • the accumulation rate of HF states is markedly lower following the wake/sleep transition in normal patients, as can be seen in Fig. 7.
  • patients who suffer from sleep disorders exhibit higher values of HF accumulation during periods of sleep.
  • Fig. 8 is a sleep/wake accumulation plot, showing cumulative durations of sleep and wake states of a patient over time, in accordance with an embodiment of the present invention.
  • curves 90 and 92 show the fractional durations of wake and sleep states, respectively.
  • each EEG segment may be classified as sleep- or wake-related, according to the following criteria: • Wake-related - HF and noisy EEG segments.
  • This plot provides information similar to the frequency state accumulation plot of Fig. 7, but in a more condensed form. Accumulation rates of the sleep and wake states are computed in a manner similar to that described above. Changing trends in the sleep/wake accumulation plot may indicate changes and/or fragmentation of sleep.
  • Fig. 9 is a transition matrix showing probabilities of transitions among frequency states in successive segments of an EEG signal, in accordance with an embodiment of the present invention.
  • a matrix can be calculated over the entire recording time for a given patient or for certain portions of the recording, for example, during a selected sleep stage.
  • processor 32 counts changes or persistence of the sleep state from second to second, hi other words, if the duration of an HF segment is 10 sec, followed by transition to MFl, the processor will count ten transitions from the HF state to itself and then one HF:MF1 transition. (As a result, it can be seen that the values on the diagonal of the transition matrix are much larger than the off-diagonal values.)
  • the transition probability P(iJ) from state i to state/ is then calculated as follows: N
  • N j j is the number of transitions from state i to state/.
  • the transition matrix shows a pattern of frequency state dynamics during sleep, which can be used as a measure of sleep quality.
  • the inventors found that in a group of patients suffering from fragmented sleep (who nonetheless presented apparently normal hypnograms), the transition probability from state MF2 to LF state was substantially lower than in normal patients. This result reflects a deficiency in low-frequency (LF) activity that characterizes fragmented sleep.
  • LF low-frequency
  • Various other sleep quality indicators may be derived from the EEG signal and calculated over the entire sleep period or for selected sleep stages.
  • the sleep quality indicators may relate to transient phenomena in the EEG, such as K-complexes and/or spindles.
  • a K-complex index which quantifies the frequency of K-complex episodes during sleep, may be calculated as follows:
  • a spindle index quantifying the frequency of EEG spindles during sleep, may be calculated in like fashion.
  • K-complexes and spindles are well-known phenomena in EEG. Techniques for automatic identification and monitoring of these phenomena are described in the above- mentioned related patent applications.
  • a snoring index (based on identification of snoring episodes by audio analysis) may be used to indicate the number or duration of snores during one or more sleep stages.
  • a transition matrix of the type shown in Fig. 9 may be computed for other indicators, such as pathological respiration states.
  • pathological respiration states include central breathing, obstructive breathing, mixed breathing, hypopnea and RERA (respiratory effort related arousal).
  • a suitable transition matrix may be constructed to show transition patterns between the respiration states.
  • processor 32 may generate respiratory event histograms to describe the distribution of the duration of respiratory events during different sleep stages. (Methods for identifying respiratory events are likewise described in the above-mentioned related applications.) Additionally or alternatively, respiratory event histograms may be presented as a function of body position, time of night, or pressure titration levels of a respiratory assist device. The processor may also assign a confidence level to each suspected respiratory event (for example, from 0 for non-events to 1 for events that are certain), and the confidence levels may be displayed as a function of respiration state in a density plot similar to that shown in Fig. 4B.
  • Respiratory events are typically accompanied by a drop in heart rate (bradycardia), followed by heart rate elevation (tachycardia).
  • Processor 32 may calculate sleep quality indicators based on these phenomena. For example, a relative heart rate index RHR, indicating the change (drop and/or elevation) of the heart rate associated with respiratory events, may be calculated as follows:
  • HR(t) is the HR in the time interval of interest
  • BHR is the baseline heart rate
  • Example calculation of relative energy in a given band.
  • V(xj ⁇ ) denote the energy variance of the samples within the EEG segment
  • the fundamental frequency of an EEG segment is the moment of the frequency spectrum, calculated as follows: oo
  • the EEG segments are classified into the following classes in the feature space defined by f ⁇ , f 2 and f $ :
  • centroid are returned to C 2 .
  • Hierarchical fuzzy clustering partitions the feature space in a recursive manner. Each level of recursion generates a new hierarchy level, in which a portion of the feature space attributed to one selected cluster is subdivided into M groups. In the present case, at each hierarchy level, the cluster with minimal centroid value is partitioned into two new clusters until the diversity level between clusters at the same hierarchy level drops below a predetermined threshold.
  • the diversity level D is given by:
  • the threshold on D is 2, i.e., when D ⁇ 2 the recursion stops.
  • the feature vectors attributed to the cluster with minimal centroid value are assigned to C4, while the rest of the feature vectors are assigned to C 2 .
  • C4 corresponds to MFl, while
  • C 2 corresponds to MF2.

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Abstract

L'invention concerne une méthode de diagnostic consistant à obtenir un signal physiologique fourni par un patient (22) durant une période de sommeil, puis à segmenter ce signal pour définir une séquence temps de segments quasi stationnaires, chacun de ces segments ayant un spectre de fréquence respectif. Dans cette méthode, des niveaux respectifs d'appartenance des segments dans une pluralité d'états de fréquence sont calculés en réponse aux spectres de fréquence respectifs. Les niveaux respectifs d'appartenance permettent d'obtenir et d'afficher un indicateur de la qualité du sommeil sur la base d'une caractéristique statistique des segments.
PCT/IL2005/000776 2003-05-15 2005-07-21 Indicateurs de la qualite du sommeil WO2006008743A2 (fr)

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US7314451B2 (en) 2005-04-25 2008-01-01 Earlysense Ltd. Techniques for prediction and monitoring of clinical episodes
WO2008037020A1 (fr) * 2006-09-27 2008-04-03 Resmed Ltd Procédé et appareil d'évaluation de la qualité du sommeil
EP2355700A2 (fr) * 2008-11-14 2011-08-17 Neurovigil, Inc. Procédés d'identification de motifs de sommeil et d'éveil et leurs utilisations
US8491492B2 (en) 2004-02-05 2013-07-23 Earlysense Ltd. Monitoring a condition of a subject
US8517953B2 (en) 2004-02-05 2013-08-27 Earlysense Ltd. Techniques for prediction and monitoring of coughing-manifested clinical episodes
US8585607B2 (en) 2007-05-02 2013-11-19 Earlysense Ltd. Monitoring, predicting and treating clinical episodes
US8603010B2 (en) 2004-02-05 2013-12-10 Earlysense Ltd. Techniques for prediction and monitoring of clinical episodes
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US8821418B2 (en) 2007-05-02 2014-09-02 Earlysense Ltd. Monitoring, predicting and treating clinical episodes
US8882684B2 (en) 2008-05-12 2014-11-11 Earlysense Ltd. Monitoring, predicting and treating clinical episodes
WO2015092591A1 (fr) * 2013-12-16 2015-06-25 Koninklijke Philips N.V. Système et procédé pour déterminer un stade de sommeil sur la base d'un cycle de sommeil
CN105105714A (zh) * 2015-08-26 2015-12-02 吴建平 一种睡眠分期方法及系统
JP2016140729A (ja) * 2015-02-05 2016-08-08 日本電信電話株式会社 睡眠段階推定装置、方法およびプログラム
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CN108471942A (zh) * 2015-09-30 2018-08-31 心测实验室公司 定量心脏测试
CN109498001A (zh) * 2018-12-25 2019-03-22 深圳和而泰数据资源与云技术有限公司 睡眠质量评估方法和装置
US10292625B2 (en) 2010-12-07 2019-05-21 Earlysense Ltd. Monitoring a sleeping subject
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US8517953B2 (en) 2004-02-05 2013-08-27 Earlysense Ltd. Techniques for prediction and monitoring of coughing-manifested clinical episodes
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US7314451B2 (en) 2005-04-25 2008-01-01 Earlysense Ltd. Techniques for prediction and monitoring of clinical episodes
US11369762B2 (en) 2006-09-27 2022-06-28 ResMed Pty Ltd Methods and apparatus for assessing sleep quality
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US8617068B2 (en) 2006-09-27 2013-12-31 ResMed Limitied Method and apparatus for assessing sleep quality
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EP2355700A4 (fr) * 2008-11-14 2013-07-10 Neurovigil Inc Procédés d'identification de motifs de sommeil et d'éveil et leurs utilisations
US11696724B2 (en) 2008-11-14 2023-07-11 Neurovigil, Inc. Methods of identifying sleep and waking patterns and uses
EP2355700A2 (fr) * 2008-11-14 2011-08-17 Neurovigil, Inc. Procédés d'identification de motifs de sommeil et d'éveil et leurs utilisations
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US11147476B2 (en) 2010-12-07 2021-10-19 Hill-Rom Services, Inc. Monitoring a sleeping subject
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WO2015092591A1 (fr) * 2013-12-16 2015-06-25 Koninklijke Philips N.V. Système et procédé pour déterminer un stade de sommeil sur la base d'un cycle de sommeil
US11344253B2 (en) 2013-12-16 2022-05-31 Koninkliike Philips N.V. System and method for determining sleep stage based on sleep cycle
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JP2016140729A (ja) * 2015-02-05 2016-08-08 日本電信電話株式会社 睡眠段階推定装置、方法およびプログラム
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WO2017032315A1 (fr) * 2015-08-26 2017-03-02 吴建平 Procédé et système de découpage en phases du sommeil
CN108471942A (zh) * 2015-09-30 2018-08-31 心测实验室公司 定量心脏测试
US11445968B2 (en) 2015-09-30 2022-09-20 Heart Test Laboratories, Inc. Quantitative heart testing
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CN109498001A (zh) * 2018-12-25 2019-03-22 深圳和而泰数据资源与云技术有限公司 睡眠质量评估方法和装置

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