JP2022089524A - Biological signal processing device and control method of the same - Google Patents

Abstract

To provide a biological signal processing device which estimates a sleeping time of a subject on the basis of data regarding breathing and a motion of the subject, and a control method of the device.SOLUTION: The biological signal processing device acquires data regarding a body movement or a physique measured to evaluate sleeping, and data regarding breathing. The biological signal processing device detects a breathing-stabilization time when breathing is determined to be stabilized, on the basis of the data regarding breathing, and estimates a hypnagogic time on the basis of the data regarding a body movement or a physique measured before the breathing-stabilization time.SELECTED DRAWING: Figure 3

Description

The present invention relates to a biometric signal processing apparatus and a control method thereof, and more particularly to a biometric signal processing apparatus that handles data measured for sleep evaluation and a control method thereof.

There is growing social interest in Sleep Apnea Syndrome (SAS). A PSG (polysomnography) test in which a large number of sensors are attached to the subject is required to confirm the SAS, but the PSG test requires hospitalization for 1 to 2 nights, so the burden on the subject is not small. .. Therefore, before performing a PSG test (detailed test), a screening test (also referred to as a simple PSG test) for determining the necessity of a PSG test is often performed. Patent Document 1 discloses an apparatus for screening inspection.

Japanese Unexamined Patent Publication No. 2017-80141

Since the simple PSG inspection uses a portable measuring device and the number of sensors to be attached is small, it can be performed at home, and there is an advantage that the burden on the subject is small. On the other hand, the simple PSG test does not measure (cannot measure) biological signals that can identify the sleep state, such as brain waves, eye movements, and mentalis electromyogram, so it is necessary to know the true sleep time of the subject during measurement. I can't.

On the other hand, the Apnea Hypopnea Index (AHI) and Oxygen Desaturation Index (ODI), which are generally used as criteria for determining the necessity of PSG test (detailed test) and as an index for SAS diagnosis. Sleep time is required for the calculation. Therefore, it is necessary to estimate the sleep time of the subject based on the data related to respiration and the movement of the subject, which is generally measured by the simple PSG test.

Therefore, an object of the present invention is to provide a biological signal processing device for estimating the sleep time of a subject based on data on respiration and movement of the subject, and a control method thereof.

The above-mentioned purpose is to determine that breathing is stable based on the acquisition means for acquiring data on body movement or position and data on breathing from the measurement data measured for sleep evaluation, and data on breathing. By a biological signal processing device characterized by having a detection means for detecting a stable breathing time and an estimating means for estimating a sleep onset time based on data on body movement or body position measured before the stable breathing time. Achieved.

INDUSTRIAL APPLICABILITY According to the present invention, it is possible to provide a biological signal processing device for estimating the sleep time of a subject based on data on respiration and movement of the subject, and a control method thereof.

It is a block diagram which shows the functional structure example of the biological signal processing apparatus which concerns on embodiment. It is a figure which shows the example of the measurement data and the artifact section. It is a flowchart about the estimation operation of the sleep onset time which concerns on embodiment. It is a figure which showed the estimation operation of the sleep onset time which concerns on embodiment using the measurement data.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings based on an exemplary embodiment. The embodiments described below do not limit the present invention in any sense. Moreover, not all of the configurations described in the embodiments are essential to the present invention. Further, the configurations included in different embodiments may be combined or interchanged unless it is clearly impossible or denied. Further, in order to omit duplicate explanations, the same or similar components are given the same reference numbers throughout the attached drawings.

FIG. 1 is a block diagram showing a functional configuration example of the biological signal processing device 100 according to the present embodiment. The biological signal processing device 100 can be realized by executing an application program that realizes the operation described later by, for example, a programmable processor. Therefore, the biological signal processing device can be implemented in a general electronic device having a programmable processor such as a personal computer.

The biological signal processing device 100 handles data measured and recorded by a simple PSG test device for sleep evaluation. In the present specification, the simple PSG test means a test that does not measure biological signals that directly represent the sleep state, such as electroencephalogram, eye movement, and mentalis electromyogram. The data measured by the simple PSG test device may differ depending on the device, but in this embodiment, at least nasal or oral and nasal breathing (pressure) is used as data related to breathing, and body movement or position is used as data related to the movement of the subject. It is assumed that it has been measured. It is also assumed that the percutaneous arterial oxygen saturation (SpO2) data is also measured.

The control unit 110 has a programmable processor, a ROM, and a RAM, and by reading a program stored in the ROM and the recording unit 130 into the RAM and executing the program, the biometric signal processing device 100 includes a waveform editing process described later. Realize the processing. Further, the control unit 110 enables the user to interactively operate the biological signal processing device 100 by executing the processing according to the operation of the operation unit 160.

The external I / F 120 is an interface for the biometric signal processing device 100 to perform wired and / or wireless communication with the external device. The biological signal processing device 100 can acquire measurement data to be processed from an external device through an external I / F 120. The external I / F 120 can have a configuration conforming to one or more of the standards for communication between devices such as USB, wireless LAN, wired LAN, and Bluetooth (registered trademark).

The recording unit 130 is a device for holding measurement data and the like, and may be a built-in storage device such as an SSD or an HDD, and / or a detachable storage device such as a USB memory or a memory card. The control unit 110 records data in the recording unit 130 and reads out the data recorded in the recording unit 130.

The display unit 150 is a display device such as a liquid crystal display device (LCD), and displays a user interface (GUI) of the biological signal processing device 100, measurement data, subject information, and the like. The display unit 150 may be an external display device.

The operation unit 160 is a general term for input devices such as a keyboard, a pointing device, a touch panel, a switch, and a button for a user to input an instruction to the biological signal processing device 100. When the display unit 150 is a touch display, the software key displayed on the display unit 150 constitutes a part of the operation unit 160.

FIG. 2 shows an example of the measurement data held by the recording unit 130 and the artifact section existing in the measurement data. The artifact section is a time section in which at least a part of the measurement items cannot be measured correctly due to sensor disconnection or the like and should not be regarded as an effective measurement time.

FIG. 2 shows an example in which artifact sections 201, 203, 205, 207, and 209 exist in the measurement section. The artifact sections 203, 205, and 207 are caused by poor mounting of the SpO2 sensor and / or poor mounting of the respiratory sensor. The artifact section 201 is a section before the subject falls asleep (pre-sleep onset section), and the artifact section 209 is a section after the subject wakes up (wake-up section).

The biological signal processing device 100 of the present embodiment detects or estimates such an artifact section from the measurement data. In particular, it is characterized in that the section before falling asleep and after waking up of the subject is estimated based on the measurement data related to the body movement or posture and respiration. Hereinafter, (1) estimation of the section before falling asleep, (2) detection of sensor mounting failure, and (3) estimation of the wake-up section will be described in order. The following processing can be implemented as a part of the function of the measurement data analysis application, for example, it is automatically executed when the control unit 110 acquires the measurement data from the recording unit 130 while the analysis application is being executed. Can be done.

(1) Estimating the pre-sleep onset section First, the estimation operation of the pre-sleep onset section will be described with reference to FIGS. 3 and 4. In the present embodiment, the pre-sleep onset section (more specifically, the sleep onset time) is estimated based on the body movement data or the body position data and the respiratory data.

The respiratory data may be data representing nasal breathing or oral and nasal breathing by changing the pressure value. Such respiratory data can be measured using a nasal or nasal cannula worn on the subject and a pressure sensor. The pressure sensor may be provided on the cannula or may be provided on the inspection device to which the cannula is connected.

The body movement data is data based on the output of a body movement sensor (for example, an acceleration sensor) attached to the chest of the subject, for example, and when the value of the body movement data continuously exceeds a predetermined threshold for a certain period of time, It is considered that body movement has been detected.
The body position data is data representing the body position of the subject (supine position, left lateral position, right lateral position, prone position, or standing / sitting position), and can be measured using, for example, a body movement sensor.

The control unit 110 measures what kind of measurement items the measurement data acquired from the recording unit 130 relates to, and under what measurement conditions (for example, sampling frequency, number of bits, etc.) each measurement item is measured. It shall be possible to grasp the frequency. The control unit 110 has a table that stores measurement items and measurement conditions in association with model information of the inspection device, and refers to the table based on the information about the measurement device acquired from the header information of the data file that stores the measurement data. By doing so, the measurement items and measurement conditions can be specified. The measurement items and measurement conditions may be specified by any other method.

In S301, the control unit 110 reads at least one of the body movement data and the body position data and the respiration data from the recording unit 130 into the internal RAM for a predetermined time from the beginning of the measurement section. Then, based on the respiratory data, the time when the respiratory is considered to be stable (respiratory stable time) is detected.

Specifically, the control unit 110 detects the maximum value and the minimum value from the time-series data constituting the respiratory data, and specifies the timing of inspiration and expiration. Then, the control unit 110 calculates the difference absolute value between the adjacent maximum value and the minimum value as one breathing amplitude. Next, the control unit 110 calculates an index (variation index) indicating the variation of the respiratory amplitude calculated for the respiratory data for a predetermined fixed time (for example, 30 seconds).

ｎは３０秒間に計測された呼吸の総数
ｘ_iは３０秒間で計測された呼吸振幅の大きさ（１≦ｉ≦ｎ）
Medianは、３０秒間で計測された呼吸振幅の中央値
である。 As an example, the control unit 110 calculates a variation index represented by the following equation.

n is the total number of respirations measured in 30 seconds x _i is the magnitude of the respiration amplitude measured in 30 seconds (1 ≤ i ≤ n)
Median is the median respiratory amplitude measured in 30 seconds.

The control unit 110 detects, for example, the time when the calculated variation index satisfies a predetermined condition with respect to the stability threshold value stored in the ROM as the respiratory stability time t1. For example, the control unit 110 compares the latest variation index with the stability threshold value, determines that if the variation index is below the stability threshold value (if it is less than the stability threshold value), it has detected respiratory stability, and calculates the variation index. Of the plurality of respiratory amplitudes used in the above, the time corresponding to the last respiratory amplitude is detected as the respiratory stabilization time t1.

Alternatively, if the control unit 110 has a predetermined ratio or more of the latest variation index less than the stability threshold, or the latest variation index continues for a predetermined time (or a predetermined plurality) and is less than the stability threshold. It is determined that the respiratory stability is detected, and the time corresponding to the last respiratory amplitude among the plurality of respiratory amplitudes used in the calculation of the latest variation index is detected as the respiratory stability time t1. Detecting respiratory stability based solely on the latest variability indicators can erroneously detect a decrease in local variability indicators as respiratory stability. By detecting the stability of respiration based on a plurality of recent variability indexes, the detection accuracy can be improved.

The stability threshold value may be, for example, an experimentally determined constant value, but may be calculated based on measurement data in order to cope with individual differences of the subject. For example, the control unit 110 shifts the interval of the measurement data for calculating the variation index by one breath waveform (or a predetermined time) from the start of the measurement, and sets the variation index over a predetermined number (or a predetermined time of the measurement data). )calculate. Then, the control unit 110 may use a value obtained by multiplying the representative values (for example, the median value, the average value, etc.) of the obtained plurality of variation indexes by a predetermined coefficient (<1) as the stability threshold value. The coefficient can be, for example, about 0.4 to 0.5.

When calculating the stability threshold value from the measurement data, the control unit 110 uses the most recently calculated stability threshold value for comparison with the variation index.
The control unit 110 executes S303 if it determines that respiratory stability has been detected, and executes S307 if it determines that respiratory stability has not been detected.

In S303, the control unit 110 refers to at least one of the body movement data and the body position data, and the body movement is detected or the standing / sitting position is detected before the respiratory stability time t1 detected in S301. Check if it is. If the measurement data includes only one of the body movement data and the body position data, the control unit 110 in S303 refers to one of the measurement data. Further, when the measurement data includes both the body movement data and the body position data, the control unit 110 may refer to both of them or one of the presets in S303.

When the control unit 110 refers only to the body movement data, the control unit 110 executes S305 if the body movement is detected before the respiratory stability time t1, and executes S307 if it is not detected. When the control unit 110 refers only to the body position data, the control unit 110 executes S305 if the standing / sitting position is detected before the respiratory stability time t1, and executes S307 if it is not detected. When the control unit 110 refers to both the body movement data and the body position data, the control unit 110 executes S305 if the body movement or the standing / sitting position is detected before the respiratory stability time t1, and neither of them is detected. If S307 is executed.

In S305, the control unit 110 sets the time when the body movement or the standing / sitting position is detected immediately before the respiratory stability time t1 as the estimated sleep onset time t2, and ends the estimation operation of the sleep onset time. If the detection of body movement or standing / sitting is continued, the end time of the period is set as the estimated sleep onset time t2. Therefore, the time when the body movement or the standing / sitting position is not detected in the immediate vicinity of the respiratory stability time t1 may be set as the estimated sleep onset time t2.

In S307, the control unit 110 determines whether or not the variation index has been calculated for the section including the end of the measurement data in S301. If it is determined that the variation index has been calculated up to the section including the end of the measurement data, the control unit 110 ends the sleep onset time estimation operation. If it is not determined that the variation index has been calculated up to the section including the end of the measurement data, the control unit 110 advances the section for calculating the variation index and executes S301.

If the respiratory stability time t1 cannot be detected until the end of the measurement data, the stability threshold value used in S301 may be changed to a good large value, and the sleep onset time estimation operation may be repeated from the beginning of the measurement data. For example, in the above example in which the stability threshold value is calculated from the measurement data, the stability threshold value is changed by setting the coefficient used for calculating the stability threshold value to a predetermined value (for example, 0.1), and the respiratory stability time is detected again. You may do it. If the respiratory stability time t1 cannot be detected even if the respiratory stability time is repeatedly detected until the coefficient reaches a predetermined upper limit value (<1), it is considered that the respiratory stability time cannot be detected and the estimation of the sleep onset time is terminated. ..

FIG. 4 is a diagram schematically showing the estimation operation of the sleep onset time described with reference to FIG. 3 together with specific examples of respiratory data and body movement data. The upper part is the measurement data, and shows the pressure data of nasal breathing, the body movement data, and the body movement detection section (the section where the body movement data exceeds the threshold), respectively. Further, the lower part shows the variation index of the respiratory amplitude calculated from the pressure data, the time series data of the median value, and the stability threshold value. The median value and the stability threshold value of the variation index indicate the values at the time when the respiratory stability is detected. Here, a stability threshold dynamically calculated from the median value of the variation index is used.

In addition, the variation index shall be calculated every 30 seconds, and when it is detected that the ratio of the variation index smaller than the stability threshold is 80% or more among the latest 10 variation indexes, the respiratory stability is stable. Is determined to be detected, and the respiratory stability time t1 is determined. Then, the time t2 at which the latest body movement detection section ends retroactively from the respiratory stability time t1 is set as the estimated sleep onset time t2.

The control unit 110 estimates the measurement section before the sleep onset time estimated as described above as the pre-sleep onset section. The above is the estimation operation of the section before falling asleep.

(2) Detection of sensor mounting failure Next, the sensor mounting failure detection operation will be described. The biological signal processing device 100 of the present embodiment detects a poor mounting of the respiration sensor (including a poor mounting of the cannula when the pressure sensor is in the main body of the inspection device) and a poor mounting of the SpO2 sensor.

First, the detection of improper attachment of the respiratory sensor will be described. The control unit 110 calculates the respiration amplitude from the respiration data in the same manner as the estimation operation of the sleep onset time, and detects a section in which the respiration amplitude is equal to or less than a predetermined threshold value as a section in which the respiration sensor (pressure sensor) is poorly attached. The control unit 110 defines a section in which an amplitude exceeding the threshold value and an amplitude below the threshold value are mixed as a section in which the respiratory sensor is not properly attached. The control unit 110 may detect a section in which the respiratory amplitude exceeds the threshold value over a predetermined fixed period as a section in which the respiratory sensor is correctly attached, and may use the other section as the respiratory sensor section.

Next, the detection of improper mounting of the SpO2 sensor will be described. The control unit 110 refers to the SpO2 data, and if there is a section in which the SpO2 value is not measured (the value is 0) continuously for a predetermined time (for example, 5 minutes) or more, the SpO2 sensor is not installed properly in that section. Detect as an interval.

(3) Estimating the wake-up section Finally, the estimation operation of the wake-up section will be described. In the present embodiment, the wake-up section (more specifically, the wake-up time) is estimated based on the respiratory data and the SpO2 data.
The control unit 110 traces back from the end of the measurement data and detects a section in which the breathing sensor mounting failure section and the SpO2 sensor mounting failure section overlap. Then, the control unit 110 goes back from the end of the measurement data to end the section where the breathing sensor mounting failure section and the SpO2 sensor mounting failure section overlap, and the SpO2 sensor is mounted even in the breathing sensor mounting failure section. The estimated wake-up time is the time when the section disappears even in the defective section.

It is considered that the subject intentionally removed the breathing sensor and the SpO2 sensor in the section where the breathing sensor mounting failure section and the SpO2 sensor mounting failure section overlap at the end of the measurement section. Further, when the breathing sensor and the SpO2 sensor are intentionally removed, they are removed one by one, so that the improper attachment of one of the sensors is detected first, and then the improper attachment of both sensors is detected. When this is detected retroactively in time, the improper attachment of both sensors is no longer detected further back from the start time of the section where the improperly attached section of the breath sensor and the improperly attached section of the SpO2 sensor overlap. The time corresponds to the time when the first sensor is removed.

The control unit 110 estimates the measurement section after the wake-up time estimated as described above as the wake-up section. The above is the estimation operation of the wake-up section.

When the above-mentioned automatic detection of the artifact section is performed, the control unit 110 stores information for specifying the artifact section as a detection result in, for example, a ROM. Then, as shown in FIG. 2, the control unit 110 may display the automatic detection result on the display unit 150 together with the corresponding measurement data. Further, the automatically detected artifact section may be editable by the user (change of the start time and start time of the section, deletion of the section) through the operation unit 160. Further, in addition to editing the automatically detected artifact section, the user may be able to set a new artifact section.

When calculating an index of SAS such as AHI and ODI, the control unit 110 uses the length of a section obtained by excluding the length of all artifact sections from all measurement sections as the sleep time. By removing the artifact section from the measurement section, the reliability of the obtained AHI and ODI values is improved.

Further, the automatic detection of the artifact section may be executed at an arbitrary timing in response to a user's instruction to the analysis application through the operation unit 160.

As described above, the biological signal processing device 100 of the present embodiment is based on the data on the respiration and the movement of the subject, which are generally measured by the simple PSG inspection device for sleep evaluation. It is possible to estimate the section before falling asleep and the section after waking up. Therefore, it becomes possible to calculate the value used for the index of SAS diagnosis such as AHI and ODI from the measurement data by the simple PSG test.

In particular, in this embodiment, not only the data related to body movement and body position but also the data related to respiration are used to estimate the pre-sleep onset section and the wake-up section. Therefore, it is possible to estimate the pre-sleep onset section and the wake-up section with higher accuracy than when only the data on body movement and body position are used. For example, since AHI, which is generally used as an index of SAS, is the number of occurrences of apnea and hypopnea events per hour, the estimation accuracy of sleep time directly affects the accuracy of AHI. The same applies to ODI because it is the number of SpO2 decreases per hour. By increasing the estimation accuracy of the section before falling asleep and the section of waking up, the accuracy of such an index of SAS can be improved.

The biometric signal processing device according to the present invention is a generally available electronic device capable of executing a program such as a personal computer, a smartphone, or a tablet terminal, and performs the operations described with reference to FIGS. 3 to 4. It can also be realized by executing a program (application software) to be executed. Therefore, such a program that causes the computer to function as the biometric signal processing device according to the embodiment, and a storage medium (an optical recording medium such as a CD-ROM or a DVD-ROM, or a magnetic recording medium such as a magnetic disk) that stores the program. , Semiconductor memory cards, etc.) also constitute the present invention.

100 ... Biological signal processing device, 110 ... Control unit, 120 ... External I / F, 130 ... Recording unit, 150 ... Display unit, 160 ... Operation unit

Claims (18)

An acquisition method for acquiring data on body movement or posture and data on respiration from the measurement data measured for sleep evaluation.
A detection means for detecting a respiratory stability time at which respiratory stability is determined based on the respiratory data, and a detection means.
An estimation means for estimating the sleep onset time based on the data related to the body movement or the body position measured before the respiratory stability time, and
A biological signal processing device characterized by having. The biological signal according to claim 1, wherein the estimation means estimates a time when body movement is no longer detected or a time when standing is no longer detected as a sleep onset time, going back from the respiratory stability time. Processing device. The biological signal processing apparatus according to claim 1 or 2, wherein the detection means detects the respiratory stability time based on an index relating to a variation in respiratory amplitude obtained from the data relating to breathing. The third aspect of the present invention is characterized in that the detection means detects the respiratory stability time based on the fact that the index relating to the variation of the respiratory amplitude satisfies a predetermined condition with respect to a predetermined threshold value. The biosignal processing device described. The third or fourth aspect of the present invention, wherein the detection means detects the respiratory stability time based on the fact that the index relating to the variation of the respiratory amplitude falls below a predetermined threshold value for the first time from the start of measurement. Biosignal processing device. The detection means is characterized in that it detects the respiratory stability time based on the fact that a predetermined ratio or more of the most recent indexes relating to the variation of the respiratory amplitude falls below a predetermined threshold value. The biological signal processing apparatus according to 3 or 4. The biological signal processing apparatus according to any one of claims 4 to 6, wherein the predetermined threshold value is calculated based on the index relating to the variation in respiratory amplitude. The acquisition means further acquires data on percutaneous arterial oxygen saturation (SpO2) from the measurement data.
The detection means further detects a breathing sensor mounting failure section among the measurement sections of the measurement data based on the breathing data, and detects a SpO2 sensor mounting failure section based on the SpO2 data. death,
The estimation means further estimates the wake-up time of the subject based on the improperly attached section of the breathing sensor detected by the detecting means and the improperly attached section of the SpO2 sensor.
The biological signal processing apparatus according to any one of claims 1 to 7. The section from the start of measurement of the measurement data to the sleep onset time, the section from the wake-up time to the end of measurement of the measurement data, the improperly attached section of the breathing sensor, and the improperly attached section of the SpO2 sensor are the sections of the measured data. The claim is characterized in that it further has a calculation means for calculating a value that is an index for diagnosing sleep aspiration syndrome (SAS) by using the length of the section excluded from the measurement section as the sleep time of the subject. 8. The biometric signal processing apparatus according to 8. An acquisition means for acquiring data on percutaneous arterial oxygen saturation (SpO2) and data on respiration from measurement data measured for sleep evaluation.
A detection means that detects a breathing sensor mounting failure section among the measurement sections of the measurement data based on the breathing data, and detects a spO2 sensor mounting failure section based on the SpO2 data.
An estimation means for estimating the wake-up time of the subject based on the improperly attached section of the breathing sensor detected by the detecting means and the improperly attached section of the SpO2 sensor.
A biological signal processing device characterized by having. Claims 8 to 10 are characterized in that the estimation means estimates the wake-up time based on a section of the measurement section in which the breathing sensor mounting failure section and the SpO2 sensor mounting failure section overlap. The biological signal processing apparatus according to any one of the above items. The estimation means traces back from the start time of the last section of the measurement section in which the breathing sensor mounting failure section and the SpO2 sensor mounting failure section overlap, and traces back from the start time of the breathing sensor mounting failure section and the SpO2 sensor. The biometric signal processing apparatus according to any one of claims 8 to 11, wherein the time when no sensor mounting failure section is detected is estimated as the wake-up time. The living body according to any one of claims 8 to 12, wherein the detection means detects a poorly attached section of the respiration sensor based on the magnitude of the respiration amplitude obtained from the data on the respiration. Signal processing device. The detection means is characterized in that, among the measurement sections, a section in which the magnitude of the respiration amplitude obtained from the data related to the respiration is equal to or less than a predetermined threshold value is detected as a section in which the respiration sensor is poorly attached. The biometric signal processing apparatus according to claim 13. The detection means 8 to 14 are characterized in that, among the data relating to the SpO2, a section in which the SpO2 value is not measured for a predetermined fixed time or longer is detected as a mounting failure section of the SpO2 sensor. The biological signal processing apparatus according to any one of the above items. The acquisition process to acquire data related to body movement or posture and data related to respiration from the measurement data measured for sleep evaluation, and
A detection step for detecting a respiratory stability time at which respiratory stability is determined based on the respiratory data, and a detection step.
An estimation process for estimating the sleep onset time based on the data related to the body movement or the body position measured before the respiratory stability time, and the estimation process.
A method for controlling a biological signal processing device, which comprises. An acquisition process for acquiring data on percutaneous arterial oxygen saturation (SpO2) and data on respiration from measurement data measured for sleep evaluation.
A detection step of detecting a breathing sensor mounting failure section among the measurement sections of the measurement data based on the breathing data and detecting a spO2 sensor mounting failure section based on the SpO2 data.
An estimation step of estimating the wake-up time of the subject based on the improperly attached section of the breathing sensor and the improperly attached section of the SpO2 sensor detected in the detection step, and an estimation step.
A method for controlling a biological signal processing device, which comprises. A program that causes a computer to function as each means of the biological signal processing apparatus according to any one of claims 1 to 15.

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