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WO2012018157A1 - Système permettant un classement automatique des stades du sommeil sur la base de signaux biologiques - Google Patents

Système permettant un classement automatique des stades du sommeil sur la base de signaux biologiques Download PDF

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Publication number
WO2012018157A1
WO2012018157A1 PCT/KR2010/006703 KR2010006703W WO2012018157A1 WO 2012018157 A1 WO2012018157 A1 WO 2012018157A1 KR 2010006703 W KR2010006703 W KR 2010006703W WO 2012018157 A1 WO2012018157 A1 WO 2012018157A1
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WIPO (PCT)
Prior art keywords
biosignal
eeg
gsr
sleep stage
ppg
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PCT/KR2010/006703
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English (en)
Korean (ko)
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유선국
이충기
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연세대학교 산학협력단
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Publication of WO2012018157A1 publication Critical patent/WO2012018157A1/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 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
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • 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
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • 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
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/398—Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
    • 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
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0531—Measuring skin impedance
    • A61B5/0533—Measuring galvanic skin response
    • 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
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6814—Head
    • A61B5/682—Mouth, e.g., oral cavity; tongue; Lips; Teeth
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6813—Specially adapted to be attached to a specific body part
    • A61B5/6825—Hand
    • A61B5/6826—Finger

Definitions

  • the present invention relates to a biosignal-based automatic sleep stage classification system, and more particularly, to a system for biosignal combinations based on convenience of biosignal measurement using a quadratic classifier.
  • the sleep stage can be more accurately analyzed and conveniently measured.
  • the present invention configures a subset of optimal biosignal combinations in consideration of the accuracy and convenience of measurement during sleep stage analysis using biosignals under different conditions, and constructs and repeats the data set independent of the experimental date.
  • the present invention relates to an automatic sleep stage classification system based on bio-signals, which allows for accurate and convenient analysis of sleep stages through performance evaluation.
  • the present invention may be selectively supplemented by an individual optimization process using a gene-fuzzy algorithm (when high accuracy is required), thereby further improving its accuracy.
  • Sleep stages are generally divided into rapid eye movement (REM), shallow sleep, and deep sleep states, and sleep stages 1 and 2 are classified into light sleep (LS) and sleep. Stages 3 and 4 are classified as Deep Sleep (DS).
  • REM rapid eye movement
  • LS light sleep
  • DS Deep Sleep
  • the sleep classification method defined above includes a biosignal reading method (visual inspection, manual inspection) based on R & K techniques (EEG, EOG, EMG).
  • the technical problem to be solved by the present invention is to configure a subset of the optimal bio-signal combination in consideration of the accuracy and convenience of the measurement in the sleep stage analysis using the bio-signals of different conditions to accurately and conveniently Make it predictable.
  • the biological signal for obtaining a variety of biological signals through different channels using at least one or more of a plurality of sensors, such as EEG, EOG, EMG, PPG, SKT, GSR sensor Signal processing for extracting feature points representing the characteristics of each biosignal from each biosignal by performing signal processing on the biosignals detected through the respective channels by the detector and the biosignal detector.
  • sensors such as EEG, EOG, EMG, PPG, SKT, GSR sensor Signal processing for extracting feature points representing the characteristics of each biosignal from each biosignal by performing signal processing on the biosignals detected through the respective channels by the detector and the biosignal detector.
  • Sleep step (REM, LS, DS) through a subset generator for generating a biosignal combination based on the feature points of each biosignal of the feature extractor, the signal processor and the feature extractor, and the biosignal combinations combined by the subset generator
  • Performance evaluation unit for classifying and analyzing the performance evaluation of combinations according to each step, and individual optimization process through gene-fuzzy algorithm
  • a bio-signal-based automatic sleep stage classification system comprising.
  • a subset is composed of various biosignal combinations in consideration of the accuracy and convenience of measurement in the sleep stage analysis using the biosignals of different conditions,
  • the sleep stages can be easily predicted by accurately and predicting the sleep stages and reducing the number of measurement signals.
  • FIG. 1 is a block diagram schematically showing the overall configuration of a biological signal-based automatic sleep stage classification system according to an embodiment of the present invention.
  • 3 is a graph illustrating the results of evaluating the experimenter-dependent data set using the second order linear separator.
  • FIG. 4 is a graph illustrating the results of evaluating the experimenter-independent data set using the second order linear separator.
  • 5B is a block diagram illustrating an individual optimization process of sleep stage classification using a gene-fuzzy algorithm.
  • FIG. 6 is a graph illustrating the relative power spectrum of the test subject and the normalization result of the sleep stage when the gene-fuzzy algorithm is used.
  • FIG. 7 is a graph showing differences between individualized normalized relative power spectral values of five frequency bands (alpha, theta, gamma, sigma, and beta) in successive sleep stages when using a gene-fuzzy algorithm.
  • FIG. 7 is a graph showing differences between individualized normalized relative power spectral values of five frequency bands (alpha, theta, gamma, sigma, and beta) in successive sleep stages when using a gene-fuzzy algorithm.
  • biosignal detection unit 110 120: EEG sensor
  • FIG. 1 is a block diagram schematically showing the overall configuration of a biological signal-based automatic sleep stage classification system according to an embodiment of the present invention
  • Figure 2 is a detailed description of the sleep stage classification process performed in an embodiment of the present invention Illustrated classification conceptual diagram.
  • the biosignal-based automatic sleep stage classification system includes a biosignal detection unit 100, a signal processing and feature extraction unit 200, a subset generator 300, and a performance evaluation unit. 400, and a performance optimizer 500.
  • the biosignal detection unit 100 is a block for measuring various biosignals from each part of the body using a 7-channel amplifier (Biopac MP150TM).
  • the 7-channel amplifier includes two channels of electroencephalogram (EEG) sensors 110 and 120, Electro-oculogram (EOG) sensor 130, electromyogram (EMG) sensor 140, photoplethysmography (PPG) sensor 150, galvanic skin response (GSR) sensor 160, and skin temperature (SKT) sensor 170 With a plurality of bio-signals detected from each sensor to obtain each through a different channel.
  • EEG electroencephalogram
  • EMG electromyogram
  • PPG photoplethysmography
  • GSR galvanic skin response
  • SKT skin temperature
  • Two-channel EEG sensors 110 and 120 are attached to the body using conductive gels, and Grass electrodes are attached to the central side C3 and C4 of the scalp, respectively.
  • the EOG sensor 130 for measuring the movement of the eyeball is attached to the left eye (+) and the right eye (-), respectively, and the ground electrode is attached to the forehead.
  • An EMG sensor 140 for measuring EMG is attached under the chin.
  • PPG sensor 150 is attached to a finger using a reflective IR sensor.
  • the GSR sensor 160 is attached to the middle finger and the ring finger using two electrodes.
  • SKT sensor 170 for skin temperature measurement is attached to the body using a calibrated sensor.
  • the EOG signal is used by removing the direct current (DC) level and removing the overall spectral power value to measure the movement of the pupil in the REM state.
  • DC direct current
  • the crisp output of the fuzzy inference algorithm is evaluated by the defuzzification of u (y).
  • the method of defuzzification is performed on the median of the maximum value representing the average value of the maximum values of all local membership functions.
  • the life stage is awake when the crisp output value is displayed as 1, rapid eye movement (REM) status when displayed as 2, shallow sleep when displayed as 3, and displayed as 4 At the same time, it can be determined by deep sleep.
  • REM rapid eye movement
  • the gene-fuzzy optimizer 500 that receives a plurality of subsets of the biosignal is defuzzified through a fuzzy inference algorithm and outputs a sleep stage, and the gene-fuzzy optimizer 600 compares the result.
  • the sleep stage analyzed at 500 and the sleep stage analyzed at the performance evaluation unit 400 are compared.
  • the comparison unit 600 helps the user determine the accuracy by determining and displaying whether the sleep stage analyzed by the gene-fuzzy optimizer 500 and the sleep stage analyzed by the performance evaluation unit 400 are the same. .
  • the fuzzy inference algorithm set of the sequence S is expressed as in Equation 13.
  • TP refers to the number of sleep steps accurately measured, and N represents the length of the sleep steps.
  • K 1, 2, 3, or 4 indicate each of the sleep stages WA, REM, SS (shallow sleep), and DS (deep sleep).
  • the genetic structure of the fuzzy-genetic optimizer 500 is an algorithm for generating population sizes of 20 and 50,
  • the genetic algorithm's crossover rate is 0.8 for scatter functions. This generated a random binary vector with '1' obtained from the first parent gene and '0' obtained from the second parent gene.
  • the chromosomes obtained here combine the form with a new generation of genes. Gaussian modifications selected from the Gaussian distribution were applied to modify the parent chromosome. The degree of mutation relative to the distributed standard deviation decreased linearly with each new generation, finally reaching zero, the last step.
  • FIG. 7 is a graph illustrating individual differences in normalizing relative power spectral values of five frequency bands (alpha, theta, gamma, sigma, and beta) in successive sleep stages.

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Abstract

La présente invention concerne un système de classement automatique des stades du sommeil sur la base de signaux biologiques permettant de prédire les stades du sommeil en fonction de signaux biologiques différents les uns des autres. A cette fin, la présente invention concerne un système de classement automatique des stades du sommeil en fonction de signaux biologiques comprenant une unité de détection de signaux biologiques utilisant des capteurs d'EEG, EOG, EMG, PPG, SKT et GSR pour acquérir divers signaux biologiques par des canaux différents les uns des autres ; une unité de traitement des signaux et d'extraction de caractéristiques utilisant un procédé adapté à l'extraction des caractéristiques de chaque signal biologique et ce, afin de traiter les divers signaux biologiques acquis par le biais de chaque canal de l'unité de détection des signaux biologiques et d'extraire une certaine caractéristique à partir de chacun des signaux biologiques ; une unité de génération de sous-ensembles capable de générer des groupes de signaux biologiques sur la base de certaines caractéristiques de chaque signal biologique extrait par l'unité de traitement des signaux et d'extraction de caractéristiques ; et une unité d'évaluation des performances permettant de classer les signaux biologiques en catégories de stades du sommeil (c'est-à-dire sommeil paradoxal, sommeil léger et sommeil profond) sur la base des groupes de signaux biologiques établis par l'unité de génération de sous-ensembles, et d'analyser une évaluation des performances des groupes pour chaque stade. Pour l'analyse des stades du sommeil sur la base de signaux biologiques différents les uns des autres, les stades du sommeil peuvent être prédits de façon précise et pratique par la combinaison de signaux biologiques, pour des mesures précises et une plus grande commodité.
PCT/KR2010/006703 2010-08-01 2010-09-30 Système permettant un classement automatique des stades du sommeil sur la base de signaux biologiques WO2012018157A1 (fr)

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CN104936514A (zh) * 2013-01-17 2015-09-23 姜承完 生物体信息检测装置及生物体信息检测方法
CN106709469A (zh) * 2017-01-03 2017-05-24 中国科学院苏州生物医学工程技术研究所 基于脑电和肌电多特征的自动睡眠分期方法
CN106999055A (zh) * 2014-12-11 2017-08-01 皇家飞利浦有限公司 用于确定针对睡眠阶段分类的谱边界的系统和方法
CN107361745A (zh) * 2017-08-08 2017-11-21 浙江纽若思医疗科技有限公司 一种有监督式睡眠脑电眼电混合信号分期判读方法
WO2018027141A1 (fr) 2016-08-05 2018-02-08 The Regents Of The University Of Colorado, A Body Corporate Systèmes de détection intra-auriculaire et méthodes de surveillance de signaux biologiques
CN108056770A (zh) * 2018-02-02 2018-05-22 合肥芯福传感器技术有限公司 一种基于人工智能的心率检测方法
CN111447536A (zh) * 2013-06-14 2020-07-24 奥迪康有限公司 助听装置及其控制方法
CN112568873A (zh) * 2021-02-25 2021-03-30 南京畅享医疗科技有限公司 一种实时睡眠监测记录与分析方法
CN113080966A (zh) * 2021-03-22 2021-07-09 华南师范大学 一种基于睡眠分期的抑郁症自动检测方法

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US10368798B2 (en) 2013-06-28 2019-08-06 North Carolina State University Systems and methods for determining sleep patterns and circadian rhythms
KR102354351B1 (ko) 2014-12-04 2022-01-21 삼성전자주식회사 수면 상태를 결정하는 전자 장치 및 그 제어 방법
EP4523734A3 (fr) * 2015-01-06 2025-05-14 David Burton Systèmes de surveillance portables mobiles
KR102501837B1 (ko) 2015-11-09 2023-02-21 삼성전자주식회사 신호 특징 추출 방법 및 장치
KR102211647B1 (ko) * 2018-12-07 2021-02-04 이화여자대학교 산학협력단 인공지능 수면개선 비침습적 뇌회로 조절치료시스템 및 방법
KR102267105B1 (ko) * 2018-12-26 2021-06-22 (주)허니냅스 딥러닝 기반의 수면다원 검사장치 및 그 방법
KR102258726B1 (ko) 2019-08-12 2021-06-01 (주)허니냅스 딥러닝을 이용한 수면질환 자동 판정을 위한 데이터 처리 장치 및 그 동작 방법
KR102251388B1 (ko) * 2019-08-12 2021-05-12 (주)허니냅스 딥러닝을 이용한 수면질환 판정 자동화 장치 및 그 동작 방법
KR102236420B1 (ko) * 2020-11-06 2021-04-06 (주)에이아이딥 인공지능을 이용한 수면 분석 시스템 및 방법
KR102617046B1 (ko) * 2021-07-19 2023-12-21 이화여자대학교 산학협력단 딥러닝 모델을 이용한 수면 단계 예측 방법 및 분석장치
KR102691350B1 (ko) * 2021-12-22 2024-08-05 고려대학교 산학협력단 단일 뇌전도 기반 수면단계 분류방법 및 수면단계 분류장치
KR20250059168A (ko) 2023-10-24 2025-05-02 고려대학교 산학협력단 단일 ppg 신호를 이용한 딥러닝 기반의 수면 단계 예측 장치 및 방법
KR20250064371A (ko) 2023-11-02 2025-05-09 닉스 주식회사 단일 ppg 신호를 이용한 딥러닝 기반의 수면 단계 예측 장치 및 방법

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CN104936514B (zh) * 2013-01-17 2018-03-02 爱麦德森株式会社 生物体信息检测装置及生物体信息检测方法
CN104936514A (zh) * 2013-01-17 2015-09-23 姜承完 生物体信息检测装置及生物体信息检测方法
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CN106999055B (zh) * 2014-12-11 2021-04-27 皇家飞利浦有限公司 用于确定针对睡眠阶段分类的谱边界的系统和方法
WO2018027141A1 (fr) 2016-08-05 2018-02-08 The Regents Of The University Of Colorado, A Body Corporate Systèmes de détection intra-auriculaire et méthodes de surveillance de signaux biologiques
EP3493737A4 (fr) * 2016-08-05 2020-01-01 The Regents of the University of Colorado, a body corporate Systèmes de détection intra-auriculaire et méthodes de surveillance de signaux biologiques
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CN106709469A (zh) * 2017-01-03 2017-05-24 中国科学院苏州生物医学工程技术研究所 基于脑电和肌电多特征的自动睡眠分期方法
CN107361745A (zh) * 2017-08-08 2017-11-21 浙江纽若思医疗科技有限公司 一种有监督式睡眠脑电眼电混合信号分期判读方法
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CN113080966A (zh) * 2021-03-22 2021-07-09 华南师范大学 一种基于睡眠分期的抑郁症自动检测方法

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