WO2018180219A1 - Évaluation d'un état de sommeil - Google Patents

Évaluation d'un état de sommeil Download PDF

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WO2018180219A1
WO2018180219A1 PCT/JP2018/008172 JP2018008172W WO2018180219A1 WO 2018180219 A1 WO2018180219 A1 WO 2018180219A1 JP 2018008172 W JP2018008172 W JP 2018008172W WO 2018180219 A1 WO2018180219 A1 WO 2018180219A1
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time
sleep
window
series data
power
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PCT/JP2018/008172
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Japanese (ja)
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早野　順一郎
恵美 湯田
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公立大学法人名古屋市立大学
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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 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state

Definitions

  • the present invention relates to sleep state evaluation. Specifically, the present invention relates to a method and a detection system for detecting non-REM sleep, and uses thereof.
  • This application claims priority based on Japanese Patent Application No. 2017-061892 filed on Mar. 27, 2017, the entire contents of which are incorporated by reference.
  • this invention makes it a subject to provide the means which detects a sleep state, especially non-REM sleep with high precision and high reliability.
  • Non-REM sleep is accompanied by a change in respiratory pattern, and in particular, the regularity of the respiratory cycle is higher than when awakened or during REM sleep.
  • Respiration modulates the beat cycle of each heart beat and generates an HF component in heart rate variability.
  • the peak of the HF component appears at a location that coincides with the respiration frequency (respiration rate). Therefore, when the regularity of the respiratory cycle increases, the power of the HF component concentrates in a narrow frequency band.
  • a method for detecting non-REM sleep is generating time-series data of the heart beat interval of the subject.
  • Detection means for pulse wave detection or electrocardiogram recording; Means for generating time-series data of the heart beat interval of the subject from the data acquired by the detection means; Means for setting a window of a predetermined time length that moves along the time axis of the time series data, and for performing spectral analysis on the time series data in the window including the window for each of a plurality of determination time points on the time axis; , Means for calculating the power concentration of the heart rate variability high frequency component from the spectrum of each window; Means for determining whether it is non-REM sleep based on the calculated degree of concentration; Sleeping evaluation system with [8] Processing for generating time-series data of the heart beat interval of the subject from the acquired data; A process of setting a window of a predetermined time length that moves along the time axis of the time series data, and performing spectral analysis on the time series data in the window including the window for each of a plurality of determination time points on the time axis; , From the spectrum of each
  • the flowchart which shows an example of the detection method of this invention.
  • Fm peak frequency of HF component
  • L width of frequency band for evaluating concentration
  • P (f) heartbeat power spectrum
  • S L frequency range from Fm-L / 2 to Fm + L / 2 Sum of power
  • S ⁇ sum of power in the frequency band Fm- ⁇ / 2 to Fm + ⁇ / 2
  • SI sleep index based on power concentration of HF component.
  • the body movement (BM), RR interval (RRI), power spectral density (PSD) of the RR interval, and new sleep index (SI) HF component power concentration during transition from awakening to NREM sleep Represents power ratio curve. Data for 15 minutes are shown, and the power spectrum and power ratio curve correspond to the first, middle and last 5 minutes, respectively, from the left.
  • the sleep index (SI) based on the power concentration of the sleep stage, body movement (BM), RR interval (RRI), heart rate variability HF component during the total bedtime of typical examples.
  • the sleep stages are W: awakening, REM: REM sleep, N1-N3: non-REM sleep. When SI> 70%, it was determined as NREM sleep, and the portion was indicated by a black bar.
  • Step 1 Step of generating time series data of the heart beat interval of the subject
  • Step 2 Setting a window of a predetermined time length that moves along the time axis of the time series data, and on the time axis
  • Step 3 Step of calculating the concentration of power of the heartbeat variability high frequency component from the spectrum of each window
  • Step 4 Calculate the spectrum of the time series data in the window including the determination time The step of determining whether or not it is non-REM sleep based on the degree of concentration
  • the detection method of the present invention can be executed by a non-REM sleep detection system described later.
  • time-series data of the heart beat interval of the subject is generated from the data acquired by the detection means (step 1).
  • a device for detecting a pulse wave or a device for recording an electrocardiogram is used as the detection means.
  • pulse wave interval data is acquired
  • RR interval data for each beat is acquired from an electrocardiogram.
  • Devices that detect pulse waves include wearable pulse wave sensors, fingertip volume pulse waves, pulse oximeters, etc., that use optical sensors (for example, those equipped with LEDs as light emitting elements and LEDs or light transistors as light receiving elements) Can be used.
  • various electrocardiographs for example, a Holter electrocardiograph, a wireless electrocardiograph, and a bedside vital monitor
  • a window of a predetermined time length (a partition frame defining a time zone to be analyzed) that moves along the time axis of the time series data is set, and for each of a plurality of determination time points on the time axis, Spectral analysis is performed on the time-series data in the included window (step 2).
  • a window is run on the time series data, and the spectrum analysis is executed along the time series.
  • the time series data is divided (partitioned).
  • time-series data is divided by the size of the window, but adjacent sections may overlap at both ends or one end.
  • the window size is not particularly limited. However, since the window size affects the time resolution, the window size may be determined in consideration of the necessary time resolution (accuracy). For example, the window size is set to 1 to 15 minutes. The preferred size is 2 to 5 minutes. The smaller the window size, the higher the time resolution of non-REM sleep determination, while the frequency resolution of the spectrum decreases and the non-REM sleep determination system decreases. However, such a problem can be solved if the processing capability is improved by improving the technology, and the above range is merely an example.
  • the window size is usually unchanged (constant), but variable (for example, the time resolution is increased and the time resolution is increased in the time zone where there is a high probability of non-REM sleep appearing, and the size is increased in other time zones. It is also possible to increase the system and reduce misrecognition).
  • FFT Fast Fourier transform
  • MEM maximum entropy
  • outliers such as extra contraction and noise are removed from the generated time series data, and the missing portion is interpolated by a method such as step interpolation. This reduces false positives and false negatives in non-REM sleep determination and increases detection sensitivity and specificity.
  • the power concentration degree (sleep indicator, (SI) of the heart rate variability high frequency (HF) component is calculated from the spectrum of each window (step 3). That is, the degree of concentration of the power of the high frequency (HF, 0.15 to 0.45 Hz) component of the heart rate variability is calculated.
  • the power concentration degree (SI) of the heart rate variability HF component is calculated, for example, by the following procedures (1) to (6).
  • L be the width of the frequency band for examining the power concentration in the region centered on Fm (Hz).
  • L is a constant (0.14 Hz) and Fm is set to 0.07 Hz on the left and right, but the left and right do not need to be the same width, and the size of L can be set freely within a positive value of 0.3 Hz or less. Can do.
  • a power ratio R ( ⁇ ) which is a ratio of S ( ⁇ ) to S (L) is obtained by the following equation.
  • An index SI representing the power concentration degree of the heart rate variability HF component is obtained as the area under the curve (AUC) of the power ratio curve R ( ⁇ ) (FIG. 3B) by the following equation.
  • ⁇ may be a constant (eg, 0.005 to 0.05, preferably 0.01 to 0.02).
  • the reference value is specific values within the range of 60% to 80% (for example, 60%, 65%, 70%, 75%, 80).
  • the reference value can be set based on data acquired from a plurality of subjects (preferably 100 or more, more preferably 200 or more).
  • the power ratio R ( ⁇ ) (the ratio of S ( ⁇ ) to S (L)) is used to determine whether it is non-REM sleep.
  • determination can be made as follows (Example 1 and Example 2).
  • ⁇ Example 1 (when ⁇ is specified)> ⁇ is specified (for example, 0.01), and when the value of the power ratio is higher than the reference value (threshold) or equal to or higher than the reference value, it is determined that the person is in a non-REM sleep state.
  • ⁇ is specified (for example, 0.01)
  • the reference value is specific values within the range of 30% to 60% (for example, 30%, 40%, 50%, etc.).
  • the reference value can be set based on data acquired from a plurality of subjects (preferably 100 or more, more preferably 200 or more).
  • the reference value can be set based on data acquired from a plurality of subjects (preferably 100 or more, more preferably 200 or more).
  • REM sleep is derived from the phenomenon that the eyeball moves under the eyelid (rapid eye movement).
  • the brain during REM sleep is said to be close to awakening, while the body is in a resting state with skeletal muscles relaxed.
  • NREM sleep is sleep that does not involve rapid eye movement, and is also called slow wave sleep.
  • Non-REM sleep is divided into four stages, stage 1 to stage 4.
  • Non-REM sleep is a so-called restful sleep and the brain is not awake.
  • a window that brought a determination result that is not in a non-REM sleep state and a window that brought a determination result that it is in a non-REM sleep state are adjacent to or in the time series adjacent to each other in this order, or the latter It can be estimated that there is a time of falling asleep in the time zone included in the window.
  • the detection method of the present invention it is possible to determine or estimate a time zone or a sleep time when the subject is in non-REM sleep.
  • the determination result according to the present invention can be used for estimating a sleep time period and / or a wake time period. That is, it is useful for grasping and determining a sleep state and sleep quality (for example, sleep efficiency represented by actual sleep time / total sleep time) or health risks related to these or sleep disorders.
  • non-REM sleep time zone and sleep onset uses information provided by the present invention (non-REM sleep time zone and sleep onset) and other means or devices such as actigram, electrocardiograph, pulse meter, blood flow meter, respiratory monitor, pulse oximeter, etc.
  • physiological index and / or behavior index for example, body acceleration, heart rate, pulse, respiration, time series analysis index of those signals
  • sleep parameters include sleep time, awake time, mid-wake time, re-sleep time, sleep time, sleep latency, sleep efficiency, sleep quality, sleep stage (wake, non-REM sleep, REM sleep), apnea -Time of hypopnea, frequency of apnea / hypopnea, etc.
  • some of the sleep parameters listed here can be estimated by the present invention alone, but accuracy, reliability, and the like can be improved by using other means or devices in combination.
  • FIG. 2 is a diagram conceptually showing a configuration example of the non-REM sleep detection system of the present invention.
  • the non-REM sleep detection system 1 includes a detector 2, an arithmetic processing unit 3, an operation unit 4, a storage unit 5, and an output unit 6.
  • the detector 2 is a pulse wave sensor, for example.
  • the pulse wave sensor for example, an optical reflective sensor including a light emitting element, a driving device thereof, and a light receiving element is used.
  • the pulse wave sensor is attached to a finger, wrist, upper arm, forearm, chest or the like for detecting a pulse wave.
  • the arithmetic processing means 3 is provided in a wearable sensor or in an external computer including a cloud, and is composed of a CPU, memory (RAM, ROM) and the like. Operation means 4 for performing arithmetic processing operations on the arithmetic processing means 3, storage means 5 for storing and holding the arithmetic processed data, and data after arithmetic processing (converted into a table or graph format) The output means 6 for outputting (which may be edited) is connected by wire or wirelessly.
  • the arithmetic processing means 3 executes predetermined processing (each step shown in FIG. 1) according to the program.
  • the program is installed and executed from, for example, a CD or DVD, various memory cards, a network or another computer / server on the cloud.
  • the output means 6 is a display device such as a monitor, in which case the result after the arithmetic processing is displayed in the form of a graph or a table.
  • the output means 6 does not directly display the data after the arithmetic processing, but the output means 6 outputs the data after the arithmetic processing to another place (for example, a stationary type or a portable type storage device or storage medium / recording medium or cloud computer). May be output.
  • the present invention also provides a computer program for use in a non-REM sleep detection system.
  • the computer program of the present invention causes a computer to execute the following processes (i) to (iv).
  • the computer program of the present invention is, for example, a CD (Compact Disc), a DVD (Digital Versatile Disc), a BD (Blu-ray (registered trademark) Disc), or various memory cards (USB flash memory, SD memory card, etc.). It is provided in a state of being stored in a computer-readable storage medium such as a computer or downloaded from a cloud computer.
  • Non-REM (NREM) sleep during sleep has higher regularity and less frequency (breathing rate) variation than during awakening. Since the high frequency (HF, 0.15-0.45 Hz) component of heart rate variability reflects fluctuations in heart rate due to breathing, the power of the HF component is concentrated in a narrow frequency band due to the reduction in variation in respiratory rate due to falling asleep. Therefore, we created a new sleep index (SI) based on the power concentration of the HF component, and compared the discrimination power of NREM sleep by SI with that of conventional heart rate variability and body motion index.
  • SI sleep index
  • HF component power concentration index As a new sleep indicator (SI), the highest peak in the HF region (0.15-0.4 5Hz) of the power spectrum of heart rate variability and the index that expresses the degree of power concentration around it. Developed ( Figure 3). The SI calculation method was as follows.
  • the R-R interval for each beat is measured from the electrocardiogram, and the heartbeat interval time series data is obtained. Eliminate outliers due to extrasystoles and noise from the heartbeat interval time series, and interpolate the missing part (step interpolation is used in this study).
  • a 5-minute window runs on the interpolated heart beat interval time series, and the power spectrum is calculated by fast Fourier transforming the time series in each window.
  • SI is obtained from the spectrum of each window by the following procedures (1) to (6).
  • a power ratio R ( ⁇ ) which is a ratio of S ( ⁇ ) to S (L) is obtained by the following equation.
  • An index SI representing the power concentration degree of the heart rate variability HF component is obtained as the area under the curve (AUC) of the power ratio curve R ( ⁇ ) (FIG. 3B) by the following equation.
  • the sleep stage was determined according to the American Academy of Sleep Medicine (AASM) guidelines (Non-patent Document 5) as an epoch every 30 seconds as one of the three stages of awakening, NREM, and REM. Furthermore, 10 consecutive epochs were set as one segment (5 minutes), and when 6 or more epochs out of 10 were in the same sleep stage, it was set as the sleep stage of that segment. Segments with less than 6 epochs at the same sleep stage were classified as transitional segments.
  • AASM American Academy of Sleep Medicine
  • Non-Patent Document 6 From the electrocardiogram of the polygraph, an RR interval time series was extracted according to the method already reported (Non-Patent Document 6). The R-R interval time series and actigraph data were analyzed by dividing into segments of every 5 minutes in accordance with the segments of the segment that determined the sleep stage.
  • the average R-R interval was calculated and converted to a heart rate.
  • the R-R interval time series was interpolated by the step function and resampled at 512 points at equal intervals.
  • Hanning-window processing spectrum analysis by FFT was performed.
  • the obtained spectral density function was integrated in the very low frequency (VLF, 0.0033-0.04 Hz) region, low frequency (LF, 0.04-0.15 Hz) region, and HF region, and the power of VLF, LF, and HF components was calculated. .
  • LF / HF was obtained as the ratio of the power of LF component to HF component.
  • the power of each frequency component was converted to a natural logarithm value.
  • Actigraph data is obtained by processing the x-, y-, and z-axis accelerations sampled at 31.25 Hz using a 0.02-0.08 Hz band-pass filter to remove DC components and high-frequency noise, and then measuring the magnitude of body movement.
  • BM (t) was calculated by the following formula as an index.
  • BM (t) For each 5-minute segment, the maximum value of BM (t) was calculated and used as an index for determining the sleep stage based on body movement.
  • the statistical analysis system program package (SAS Institute Inc., Cary, NC, USA) was used for statistical analysis. Differences in heart rate, heart rate variability index, body movement, and SI due to sleep stage were tested by ANOVA using a general linear model.
  • the power to detect NREM sleep by each index is the area under the curve (AUC) of the receiver-operating characteristic (ROC) curve, which is the discriminatory power between the segment classified as awake and REM sleep and the segment classified as NREM sleep. ).
  • AUC area under the curve
  • ROC receiver-operating characteristic
  • FIG. 4 shows data at the time of transition from awakening of typical examples to NREM sleep (left figure) and during NREM sleep (right figure). Compared to awakening, the NREM stage shows a sharp spectrum of HF components, and the SI value exceeds 70%.
  • Fig. 5 shows the data of the representative example during the entire bedtime. SI exceeds 70% in the NREM stage.
  • a total of 22,103 segments of data were obtained from 262 polysomnograms, 3,522 segments (15.9%) were awakening stages, 7,759 (35.1%) were NREM stages, 3136 (14.2%) were REM stages, and the remaining 7,686. (34.8%) was the transition segment.
  • SI sleep indicator based on power concentration of HF component
  • HR heart rate
  • BM maximum value of body movement in the segment for 5 minutes
  • Table 2 (ROC analysis of discriminatory power between NREM sleep segment and segment combining wakefulness and REM sleep) for the ability to distinguish NREM stage 7,759 segment from 6,658 segment combining wakefulness and REM sleep ).
  • the discriminating power (AUC) of the NREM segment based on the conventional index was lower in all heart rate, VLF, LF, HF, LF / HF, and body motion AUC than SI AUC (all P ⁇ 0.0001).
  • the index (SI) that represents the power concentration of the HF component of the power spectrum of heart rate variability, using the increase in breathing regularity associated with NREM sleep, is used to distinguish between NREM sleep and wakefulness and REM sleep. It has a higher discriminating power than the number, the heart rate variability index, and the body motion index by acceleration.
  • ⁇ Respiration during sleep is more regular than during awakening, and there is little variation in frequency (respiration rate). Since the HF component of the heart rate variability reflects fluctuations in the heart rate due to breathing, the power of the HF component is concentrated in a narrow frequency band by reducing the variation in the respiratory rate due to falling asleep. According to the present invention using this phenomenon, it is possible to estimate the sleep onset time (start time of non-REM sleep) based on not only the accuracy but also a reliable physiological basis. In this respect, the present invention far exceeds the conventional techniques and methods. If the present invention is applied or applied to a wearable sensor for detecting a heartbeat or a pulse, various devices for detecting the heartbeat or a pulse, an analysis device for heartbeat or pulse data, various sleep parameters can be estimated. Measuring sleep parameters on a regular or daily basis is useful for health management, discovery of health risks and unconscious sleep disorders, objective evaluation, and the like.

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Abstract

La présente invention aborde le problème de fourniture d'un moyen pour détecter un état de sommeil, en particulier, un sommeil paradoxal, avec une précision élevée et une fiabilité élevée. L'invention concerne un procédé de détection du sommeil non paradoxal, le procédé comprenant : une étape de génération de données de série temporelle sur des intervalles de battement de cœur du cœur d'un sujet ; une étape de réglage d'une fenêtre qui a une durée prédéterminée et se déplace le long de l'axe temporel des données de série temporelle, et de conduite, pour chacun d'une pluralité de points de détermination sur l'axe de temps, une analyse spectrale sur les données de série temporelle dans la fenêtre comprenant le point de détermination ; une étape de calcul, à partir des spectres des fenêtres, du degré de concentration de puissance d'une composante à haute fréquence de variabilité de fréquence cardiaque ; et une étape de détermination que l'état de sommeil est ou non un sommeil non paradoxal sur la base du degré de concentration calculé.
PCT/JP2018/008172 2017-03-27 2018-03-02 Évaluation d'un état de sommeil WO2018180219A1 (fr)

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CN112244773A (zh) * 2020-10-15 2021-01-22 上海我乐科技有限公司 睡眠质量监测装置、方法及床垫
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CN113080897A (zh) * 2021-04-02 2021-07-09 北京正气和健康科技有限公司 一种基于生理和环境数据分析的入睡时刻评估系统及方法
CN113080897B (zh) * 2021-04-02 2023-07-28 北京正气和健康科技有限公司 一种基于生理和环境数据分析的入睡时刻评估系统及方法
CN113568505A (zh) * 2021-07-22 2021-10-29 歌尔科技有限公司 入睡时间点确定方法、装置、设备及可读存储介质
CN114366027A (zh) * 2021-12-29 2022-04-19 深圳融昕医疗科技有限公司 睡眠数据波形生成方法及终端
CN116779110A (zh) * 2023-08-07 2023-09-19 安徽星辰智跃科技有限责任公司 基于模态分解的睡眠可持续性检测调节方法、系统和装置
CN116779110B (zh) * 2023-08-07 2024-05-31 安徽星辰智跃科技有限责任公司 基于模态分解的睡眠可持续性检测调节方法、系统和装置
RU224143U1 (ru) * 2023-10-13 2024-03-18 Федеральное государственное бюджетное учреждение науки "Санкт-Петербургский Федеральный исследовательский центр Российской академии наук" Устройство для диагностики профессиональной работоспособности человека-оператора

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