JP2023066909A - Sleep apnea determination device, sleep apnea determination method, and sleep apnea determination program - Google Patents

Abstract

To enable determination of sleep apnea syndrome.SOLUTION: It is determined that a subject has sleep apnea syndrome on the basis of a change in the sleep stage as a result of determining the sleep stage of the subject at regular time intervals. For example, a sleep apnea syndrome is determined to be present when the number of times a change in sleep stage appears at regular intervals in the order of stage 2 non-REM sleep, wakefulness, and stage 1 non-REM sleep is greater than the number of times wakefulness appears in three consecutive regular hours. Alternatively, when the change from non-REM sleep to wakefulness occurs in Stage 3 or Stage 4, the subject is determined to have sleep apnea.SELECTED DRAWING: Figure 6

Description

The present invention relates to a sleep apnea syndrome determination device, a sleep apnea syndrome determination method, and a sleep apnea syndrome determination program for determining sleep apnea syndrome of a subject.

In medical practice, the sleep state of a subject is measured in order to diagnose sleep disorders and sleep apnea syndrome. It is known that the human sleep stage is classified into six stages from the viewpoint of the depth of sleep, and the six sleep stages are wakefulness, REM sleep, non-REM sleep (stage 1 to 4). Conventionally, these six sleep stages are determined by attaching a large number of electrodes to the subject's face and head, for example, and measuring electroencephalograms, eye movements, and jaw electromyograms from the large number of electrodes. Analysis of the results was done.

In addition, patients with sleep apnea syndrome often experience apnea during sleep, difficulty breathing, light sleep, and awake sleep stages. , sleep stages should be measured.
However, in order to determine sleep apnea syndrome, in addition to the measurement of sleep stages, various measurements such as air flow associated with breathing such as mouth and nose air flow and ventilation movements of the chest and abdomen are required. measurements must be made simultaneously. Then, based on the analysis result of the sleep stage and the measurement result of the respiratory state, the doctor diagnoses whether or not the patient has an apnea syndrome.

Sleep tests with a large number of electrodes attached to the face and head, which are necessary for making such a diagnosis, are usually performed by staying at a medical institution and attaching electrodes to the body continuously for a long time. This is an examination performed by the patient, and imposes a mental and physical burden on the person (patient) to be measured. In addition, the acquired data must be analyzed and judged by a doctor with specialized knowledge and experience. For this reason, sleep apnea syndrome cannot be determined easily.

In order to solve the problems related to sleep stage measurement, many sleep stage estimation methods that do not require diagnosis by a specialist have been proposed.
For example, Patent Document 1 describes a method called Database-based Compact Genetic Algorithm, which is an improved learning method using a genetic algorithm, and a technology for estimating sleep stages from detection data of a mattress type pressure sensor. there is The technique described in this Patent Document 1 estimates a sleep stage based on the subject's body motion and heart rate detected by a mattress-type pressure sensor. According to the technique described in Patent Literature 1, by estimating the sleep stage using the mattress-type pressure sensor, it is possible to estimate the sleep state of the subject without imposing a burden on the subject.

JP 2014-239789 A

As described above, when patients with apnea syndrome develop apnea during sleep, breathing becomes difficult, sleep becomes light, and the sleep stage often shifts to an arousal state. For this reason, detecting sleep stages using the detection data of the mattress-type pressure sensor, that is, the body movement detection data, is an index for determining apnea syndrome.

The point that there is a possibility of apnea syndrome when the sleep stage during sleep often becomes arousal is, for example, the change in the sleep stage of a healthy person in FIG. This can be seen by comparing the change in the patient's sleep stage (b). In other words, as can be seen from FIG. 4, the patients with apnea syndrome have more awakening sleep stages than the healthy subjects.
However, even in healthy people, there are many cases in which deep sleep is not possible depending on the situation, and it is not possible to judge that apnea syndrome is present simply because of the large number of awakenings in the sleep stage during sleep. Therefore, there is an urgent need to more accurately determine apnea syndrome.

An object of the present invention is to provide an apnea syndrome determination device, an apnea syndrome determination method, and an apnea syndrome determination program capable of determining sleep apnea syndrome.

The apparatus for determining sleep apnea syndrome of the present invention determines that the person to be measured has sleep apnea syndrome based on changes in the sleep stage as a result of determining the sleep stage of the person to be measured at regular time intervals. An occasional apnea syndrome determination unit is provided.

Further, the sleep apnea syndrome determination method of the present invention determines that the subject has sleep apnea syndrome based on changes in the sleep stage as a result of determining the sleep stage of the subject at regular intervals. including sleep apnea syndrome determination processing.

Further, the sleep apnea syndrome determination program of the present invention causes a computer to execute each process performed by the sleep apnea syndrome determination method as a procedure.

ADVANTAGE OF THE INVENTION According to this invention, it becomes possible to determine sleep apnea syndrome with high accuracy by utilizing the fact that sleep stage changes peculiar to patients with sleep apnea syndrome appear. Therefore, it is possible to easily determine sleep apnea syndrome using data obtained by measuring sleep stages during sleep.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the structural example of the sleep apnea syndrome determination apparatus by one embodiment of this invention. FIG. 4 is a diagram showing an example of sleep apnea syndrome determination state according to an embodiment of the present invention; 1 is a block diagram showing a hardware configuration example of a device for determining sleep apnea syndrome according to an embodiment of the present invention; FIG. FIG. 5 is a diagram showing an example of processing for obtaining a power spectrum during sleep at regular intervals according to an embodiment of the present invention; It is a figure comparing an example of changes in sleep stages during sleep between a healthy subject (a) and a patient with sleep apnea syndrome (b). 4 is a flow chart showing the flow of processing for determining sleep apnea syndrome according to one embodiment of the present invention. It is a diagram showing an example of frequent changes for each patient, counting changes in sleep stage for 90 seconds during sleep of sleep apnea syndrome patients (nine). FIG. 10 is a diagram showing an example of changes in sleep stages that are counted for 90 seconds during sleep of healthy subjects (nine persons) and that occur frequently for each healthy subject. Among the changes in the sleep stages of the patients with sleep apnea syndrome (9 people) in FIG. , and a case in which awakening (w) continues three times. Among the changes in sleep stages of healthy subjects (9 people) in FIG. is a diagram for comparison with the case where is continued three times. FIG. 8 is a diagram showing a change in the sleep stage of sleep apnea syndrome patients (9 people) in FIG. 7 from stage 2 or 3 non-REM sleep (2/3) to wakefulness (w). . FIG. 9 is a diagram showing the number of occurrences when changing from stage 2 or 3 non-REM sleep (2/3) to wakefulness (w) among the changes in the sleep stages of healthy subjects (nine persons) in FIG. 8 . Among the changes in the sleep stages of the sleep apnea syndrome patients (nine persons) in FIG. FIG. 4 is a diagram showing; FIG. 9 is a diagram showing a case where either condition of comparison of changes in the example of FIG. 10 or occurrence of changes in the example of FIG. 12 is satisfied among changes in sleep stages of healthy subjects (nine persons) in FIG. 8 . FIG. 8 is a diagram showing an example of changes in sleep stages of the sleep apnea syndrome patients (nine persons) in FIG. 7 that can be distinguished by other combinations. FIG. 9 is a diagram showing an example of changes in sleep stages of healthy subjects (nine persons) in FIG. 8 that can be discriminated by another combination.

An embodiment of the present invention (hereinafter referred to as "this example") will now be described with reference to the drawings. In the following description and drawings, the sleep apnea syndrome is referred to as SAS, and the sleep apnea syndrome patient is referred to as the SAS patient. In addition, when referring to healthy subjects in the following description, it means those who are not SAS patients.
[1. Configuration of sleep apnea syndrome determination device]
FIG. 1 is a block diagram showing the configuration of a sleep apnea syndrome determination device 10 of this example.
FIG. 2 is a diagram showing an example of a state in which sleep apnea syndrome determination is performed using the sleep apnea syndrome determination device 10 of this example.

The sleep apnea syndrome determination device 10 of the present embodiment acquires the body vibration of the person to be measured as pressure data with the mattress sensor 2 . The bio-vibration includes vibration components due to heartbeat and respiration in addition to vibration components due to the body motion of the person being measured. The mattress sensor 2 detects the bio-vibration of the upper body of the subject A during sleep as a change in pressure. Note that the mattress sensor 2 is used by laying it on or under the mattress of the bed 1 on which the subject A sleeps, as shown in FIG. 2, for example. Arranging the mattress sensor 2 on the mattress under the subject A is an example, and the mattress sensor 2 may be built in the mattress, for example.

FIG. 2 shows an example in which the sleep apnea syndrome determination device 10 is installed beside the bed 1 and the mattress sensor 2 and the sleep apnea syndrome determination device 10 are connected with a cable. The pressure data (biological vibration data) may be wirelessly transmitted to the sleep apnea syndrome determination device 10 in another room.
In the following description, the pressure data output by the mattress sensor 2 will be referred to as biological vibration data.
It should be noted that obtaining biological vibration data from a pressure sensor is an example, and other sensors may be used. For example, an infrared sensor, a laser, or the like may be used to measure the vibration of the subject during sleep without contact.

The sleep apnea syndrome determination device 10, as shown in FIG. ) 14 and an output unit 15 .
The biometric data acquisition unit 11 performs biodata acquisition processing for acquiring biovibration data output from the mattress sensor 2 . The biological vibration data acquired by the biological data acquisition section 11 is supplied to the biological data processing section 12 .

The biological data processing unit 12 samples the supplied biological vibration data, converts it into digital data, and calculates the frequency power spectrum of the digitalized biological vibration data. The process of calculating the power spectrum of the frequency of this biovibration data is performed in a cycle of 30 seconds. However, in this example, one calculation is actually performed for 32 seconds, and the calculation for the 32 seconds is performed in a cycle of 30 seconds, that is, overlapped by 2 seconds.

It should be noted that the calculation of the power spectrum by the biological data processing unit 12 at a cycle of 30 seconds is an example, and the power spectrum may be calculated at a cycle shorter than 30 seconds or longer than 30 seconds. For example, the biological data processing unit 12 may calculate the power spectrum in 60-second cycles. The overlapping of two seconds in one calculation is also an example, and there may be no overlapping period.
Then, the biological data processing unit 12 supplies the calculation result of the power spectrum for each fixed period during sleep to the sleep stage determination unit 13 and the SAS determination unit 14 .

The sleep stage determining unit 13 determines the sleep stage of the person being measured for each period based on the calculation result of the power spectrum for each certain period. When determining this sleep stage, not only the power spectrum but also various feature amounts obtained by calculating the biological vibration data may be used for determination. Alternatively, a random forest, which is one of machine learning, may be used to determine a sleep stage from feature amount data.

As sleep stages, there are wakefulness (WAKE: W), REM sleep (R), and non-REM sleep (NR) in order from the lightest sleep stage. Non-REM sleep has four stages from stage 1 to stage 4 (NR1- NR4). Therefore, sleep stages are divided into six stages in total. Among the six sleep stages, stage 4 non-REM sleep (NR4) is the deepest sleep stage. However, in actual sleep, it is rare to enter a deep sleep stage such as stage 4 non-REM sleep.
In the drawings, stages 1 to 4 of NREM sleep may be referred to as "N1" to "N4" or "1" to "4" by omitting "NR".
Although the sleep stage determination unit 13 of this example determines sleep stages from biological vibration data, the sleep stage determination unit 13 may determine sleep stages from other biological data such as electroencephalograms.

The SAS determination unit 14 determines whether the subject A is asleep based on the state of occurrence of specific changes in the sleep stage at regular intervals (every 30 seconds) during one sleep of the subject A (from falling asleep to waking up). It is determined whether or not there is apnea syndrome (SAS). Examples of changes in sleep stages specifically determined by the SAS determination unit 14 will be described later.

The output unit 15 outputs the result of sleep apnea syndrome determined by the SAS determination unit 14 . The output unit 15 is configured by, for example, a display device, and displays the determination result of sleep apnea syndrome. Alternatively, the output unit 15 may be configured as a recording device to record the determination result of the sleep apnea syndrome together with the sleeping state of the night. Moreover, when the output unit 15 displays or records, not only the sleep apnea syndrome determination result but also the sleep stage determination result may be displayed or recorded at the same time.

Further, the output unit 15 may be another external terminal to transmit the determination result via the network. For example, the output unit 15 may be a pre-registered smartphone, and the determination result may be transmitted.
In addition, the sleep apnea syndrome determination apparatus 10 of this example may perform data acquisition and determination during sleep in real time. On the other hand, in the sleep apnea syndrome determination device 10, the biological data acquisition unit 11 only acquires biological vibration data during sleep, records the acquired data, and uses the recorded data to perform processing up to determination at a later date. You can do it.

[2. Hardware configuration example of sleep apnea syndrome determination device]
FIG. 3 shows a hardware configuration example when the sleep apnea syndrome determination device 10 is configured by a computer device.
The computer device C includes a CPU (Central Processing Unit) C1, a ROM (Read Only Memory) C2, and a RAM (Random Access Memory) C3 connected to a bus C8. Further, computer device C comprises non-volatile storage C4, network interface C5, input device C6, and display device C7.

The CPU C1 reads from the ROM C2 the program code of the software that realizes each function of the biological data processing unit 12, the sleep stage determination unit 13, and the SAS determination unit 14 of the sleep apnea syndrome determination device 10, and executes them. The CPU C1 reads the program for executing the corresponding processing from the ROM C2 and executes the processing for determining the sleep stage and the processing for determining the sleep apnea syndrome. Variables, parameters, etc. generated during the arithmetic processing are temporarily written in the RAM C3.

As the non-volatile storage C4, for example, HDD (Hard disk drive), SSD (Solid State Drive), flexible disk, optical disk, magneto-optical disk, CD-ROM, non-volatile memory and the like are used. The non-volatile storage C4 stores an OS (Operating System), various parameters, and a program for causing the computer device C to function as the sleep apnea syndrome determination device 10 . In addition, the sleep stage determined by the sleep stage determination unit 13 and the data regarding the presence or absence of sleep apnea syndrome determined by the SAS determination unit 14 are also recorded in the nonvolatile storage C4.

For the network interface C5, for example, a NIC (Network Interface Card) or the like is used, and various data can be transmitted and received via a LAN (Local Area Network) to which terminals are connected, a dedicated line, or the like. For example, the computer device C acquires pressure data output by the mattress sensor 2 via the network interface C5. The input device C6 is configured by, for example, a device such as a keyboard, and the input device C6 is used to set the period for determining sleep apnea syndrome by the sleep apnea syndrome determination device 10, to instruct the display format of the determination result, etc. is done. The result of sleep apnea syndrome determination by the sleep apnea syndrome determination device 10 is displayed on the display device C7.
It should be noted that the sleep apnea syndrome determination device 10 is an example of a computer device that functions as a determination device by executing a program (software), and part or all of the sleep apnea syndrome determination device 10 Dedicated hardware for executing

[3. Calculation state of frequency power spectrum and determination example of sleep stage]
Next, processing performed in each part of the sleep apnea syndrome determination device 10 of this example will be described.
First, with reference to FIG. 4, an example in which the biological data processing unit 12 calculates the power spectrum of the frequencies of biological vibrations during sleep for a certain period of time will be described. As already explained, in this example, the biometric data processing unit 12 processes 32 seconds of data at 30 second intervals.
That is, as shown in FIG. 4, the biometric data acquisition unit 11 first acquires the sensor values of the mattress sensor 2 from falling asleep (0 seconds) to 32 seconds, and thereafter acquires sensor values for 32 seconds at intervals of 30 seconds. I will get. The biological data acquisition unit 11 continuously acquires the biological vibration data from falling asleep to waking up, and supplies the acquired biological vibration data to the biological data processing unit 12 .

Then, the biometric data processing unit 12 calculates the frequency power spectrum for 32 seconds as shown in the lower right of FIG. Frequency analysis for calculating this power spectrum is performed by, for example, fast Fourier transform (FFT).
When the power spectrum of the bio-vibration during sleep is calculated as in this example, the frequencies exhibiting a high density are the bio-vibration component due to heartbeat and the bio-vibration component due to respiration. Normally, the heartbeat frequency is around 1 Hz, and the component of bio-vibration due to respiration is around 2 Hz. However, in the case of an SAS patient, there is an apnea section, and therefore there is a period during which no biological vibration due to respiration occurs. In addition, in sections with large body movements such as rolling over, a bio-vibration component is generated due to body movements that are much larger than heartbeats and respirations.

The sleep stage determination unit 13 determines the main sleep stage in 30 seconds from the power spectrum of the biological vibrations as shown in FIG. The method already proposed by the inventors of the present application can be applied as a method for determining sleep stages from the power spectrum of biological vibrations. Although description is omitted here, according to this method, for example, the sleep stage can be determined from the occurrence of different feature amounts for each sleep stage. In the example of FIG. 4, the first 30 seconds after falling asleep are determined to be wakefulness (W), and the next 30 seconds are determined to be stage 2 non-REM (N2).

FIG. 5 is a diagram comparing an example (a) of measuring the sleep stages of a healthy subject and an example (b) of measuring the sleep stages of a patient with sleep apnea syndrome (SAS patient) during a certain sleep period. be.

In FIG. 5, the horizontal axis indicates sleep time, and the vertical axis indicates sleep stages. The sleep stage on the vertical axis shows the lightest wakefulness (WAKE) at the top, and REM sleep (REM), stage 1 non-REM sleep (NREM1), stage 2 non-REM sleep (NREM1) toward the bottom. NREM 2), stage 3 non-REM sleep (NREM 3), and stage 4 non-REM sleep (NREM 4), indicating a deep sleep stage.

In the example of FIG. 5, in both the healthy subject shown in (a) and the SAS patient shown in (b), the deepest sleep stage is stage 3 non-REM sleep (NREM3), stage 4 non-REM sleep (NREM4) has not been reached.
As can be seen by comparing the healthy subject (a) and the SAS patient (b) in FIG. 5, the SAS patient frequently wakes up due to apnea (WAKE).
In the case of an SAS patient, when the patient wakes up (WAKE), the tongue blocks the airway, causing apnea, and its influence appears in changes in pulsation (heart rate).
Therefore, when the sleep stage determination unit 13 determines the sleep stages, it is preferable to perform determination processing that also considers the features that appear when the SAS patient is awake.

Here, the sleep apnea syndrome determination apparatus 10 of this example performs SAS determination according to the processing procedure described below from the state in which these six sleep stages change.

[4. Flow of sleep apnea syndrome (SAS) determination processing]
FIG. 6 is a flow chart showing the flow of processing for determining SAS by the sleep apnea syndrome determination device 10 of this example.
First, the SAS determination unit 14 compares the ratio of the number of occurrences of a specific changing state during 90 seconds during one sleep with the number of times of continuous awakening (WAKE) for 90 seconds (step S11). Here, the specific change state for 90 seconds is the state of stage 2 non-REM sleep (NR2) for the first 30 seconds, the state of wakefulness (WAKE) for the next 30 seconds, and the stage for the last 30 seconds It refers to the state of non-REM sleep (NR1) of 1.

In addition, the SAS determination unit 14 determines whether or not there is a state from non-REM sleep (NR3 or NR4) in stage 3 or stage 4 to wakefulness (WAKE) as a change in sleep stage during one sleep (step S12). .

Then, in the comparison in step S11, the SAS determination unit 14 determines that the specific state change, which is stage 2 non-REM sleep (NR2), wakefulness (WAKE), and stage 1 non-REM sleep (NR1), is a change in wakefulness. It is determined whether (WAKE) is equal to or greater than the number of consecutive 90 seconds. In this determination, if the specific state change is equal to or greater than the number of consecutive wakefulness (WAKE) for 90 seconds, the SAS determination unit 14 determines that the subject is an SAS patient.

Furthermore, in the determination in step S12, the SAS determination unit 14 determines that the subject is an SAS patient if there is at least one stage 3 or 4 non-REM sleep (NR3 or NR4) to wakefulness (WAKE) state. It is determined that there is (step S13). Then, if the subject does not correspond to the SAS patient in these determinations, the SAS determination unit 14 determines that the subject is not the SAS patient. This determination result is output from the output unit 15 .

[5. Verification using actual measurement data]
Next, the fact that the SAS can be correctly determined in the determination in step S13 of FIG. 6 will be described using examples of sleep stage changes obtained from actual measurement data.
FIG. 7 shows a list of changes that appeared during three consecutive measurement periods (90 seconds) during one sleep of nine SAS patients D11 to D19, and the number of occurrences of each change.
FIG. 8 shows a list of changes that appeared during three consecutive measurement periods (90 seconds) during one sleep of nine healthy subjects D21 to D29, and the number of occurrences of each change.
Among the data of SAS patients D11-D19 and healthy subjects D21-D29 shown in FIGS. 7 and 8, the change on the left is the change that occurs most frequently.

7 and 8, for example, the change "WWW" at the left end of each SAS patient D11 in the top row of FIG. , and “14” below it indicates that the number of times the corresponding awakening (WAKE) occurred three times consecutively during one sleep is 14 times. In addition, the numerical value "0.285714" under "14" indicates the rate at which awakening occurs three times in succession. In other words, in this example, the ratio of three consecutive awakenings is about 28.5%.

Below are representative examples of the symbols shown in FIGS. 7 and 8 and the meaning of the three sleep stages that occur in sequence during the 90 seconds they represent.
・WWW: Awakening → Awakening → Awakening ・2WW: Stage 2 NREM sleep → Awakening → Awakening ・2W2: Stage 2 NREM sleep → Awakening → Stage 2 NREM sleep ・2W1: Stage 2 NREM sleep → Awakening → Stage 1 NREM sleep ・1WW: Stage 1 NREM sleep → Awakening → Awakening ・1W1: Stage 1 NREM sleep → Awakening → Stage 1 NREM sleep ・WW1: Awakening → Awakening → Stage 1 NREM sleep ・3W1: Stage 3 NREM sleep → Awakening → Stage 1 NREM sleep ・3WW: Stage 3 NREM sleep → Awakening → Awakening ・4W3: Stage 4 NREM sleep → Awakening → Stage 3 NREM sleep

・RWW: REM sleep → Awakening → Awakening ・RW1: REM sleep → Awakening → Stage 1 non-REM sleep ・RWR: REM sleep → Awakening → REM sleep

An example of analyzing each data shown in FIGS. 7 and 8 will be described in order from FIG. 9 onwards.
FIG. 9 shows the number of times the sleep stage changed in the order of stage 2 NREM sleep → awakening → stage 1 NREM sleep (hereinafter referred to as “2W1” order) for each data of SAS patients D11 to D19 in FIG. and the number of consecutive awakenings (referred to as the order of "WWW").
Similarly, FIG. 10 shows, for each data of healthy subjects D21 to D29 in FIG. This is a comparison.

Here, in the case of SAS patients D11 to D19 in FIG. 9, the number of times the sleep stages changed in the order "2W1" was the same or greater than the number of times the sleep stages changed in the order "WWW". As for SAS patients, six SAS patients D12, D13, D14, D17, D18, and D19 out of the nine are SAS patients. In the case of the SAS patient D19, the sleep stages did not change in the order of "WWW". In other words, a state in which awakening continues for 90 seconds has not occurred.

On the other hand, in the case of healthy subjects D21 to D29 in FIG. 10, the number of sleep stage changes in the order of "WWW" is greater for all nine than the number of sleep stage changes in the order of "2W1".
Therefore, if the number of times the sleep stage changes in the order of "2W1" is equal to or greater than the number of times the sleep stage changes in the order of "WWW", it is determined that the SAS patient is relatively high. SAS patients can be determined with accuracy. For example, in the case of the example of FIG. 9, the correct rate for determining a patient with SAS is a high value of 66.7%. Further, in the case of the example of FIG. 10, the correct rate of determining that the patient is not an SAS patient is 100%.

It should be noted that the state in which the order change of "2W1" occurs in 90 seconds is a state of temporary awakening for about 30 seconds in the middle of 90 seconds. Temporary wakefulness is likely due to the occurrence of an apnea in SAS patients. Conversely, in the case of a healthy person, the frequency of such short-time awakenings is very low, and the state of "WWW" sequence in which the awakenings continue for 90 seconds occurs relatively frequently.

FIG. 11 shows that for each data of SAS patients D11 to D19 in FIG. 7, a state in which the sleep stage changed from stage 3 non-REM sleep or stage 4 non-REM sleep to wakefulness occurred (hereinafter referred to as "3W / 4W" number of occurrences).
Similarly, FIG. 12 shows the number of occurrences of “3W/4W” for each data of healthy subjects D21 to D29 in FIG.

This "3W/4W" occurs in seven SAS patients D11, D12, D13, D15, D16, D17 and D19 out of nine SAS patients D11 to D19 in FIG.
On the other hand, in the case of healthy subjects D21 to D29 in FIG. 12, only one of the nine subjects, D21, has "3W/4W".
Therefore, when "3W/4W" occurs, it is possible to determine an SAS patient with high accuracy by determining that the patient is an SAS patient. For example, in the case of the example of FIG. 11, the accuracy rate for determining a patient with SAS is 100%. Further, in the case of the example of FIG. 12, the correct rate of determining that the patient is not an SAS patient is 89%.

Note that the occurrence of "3W/4W", that is, the change from stage 3 or stage 4 non-REM sleep to a sudden awakening sleep stage is a change from a deep sleep stage to the lightest sleep stage. This change from the deep sleep stage to the lightest sleep stage occurs rarely in healthy subjects, whereas in SAS patients, it is caused by apnea during the deep sleep stage. there's a possibility that.

FIG. 13 shows whether the number of sleep stage changes in the order “2W1” is the same as the number of sleep stage changes in the order “WWW” for the data of the nine SAS patients D11-D19 in FIG. This example shows an example in which an SAS patient is determined to be an SAS patient when either one of the following conditions is satisfied: more than 3W/4W. The determination in FIG. 13 corresponds to the determination in step S13 of the flowchart in FIG.
In the case of the example shown in FIG. 13, the accuracy rate for determining SAS patients is 100%.

FIG. 14 shows the data of nine healthy subjects D21 to D29 in FIG. , , and "3W/4W" are not generated.
In the case of the example shown in FIG. 14, the correct rate of determining that the patient is not an SAS patient is 89%.

Therefore, according to the sleep apnea syndrome determination device 10 of this example, it is possible to determine a patient with sleep apnea syndrome with very high accuracy from changes in sleep stages.
In addition, in the embodiment described so far, the sleep stage is acquired from the measurement data of the unconstrained mattress sensor 2, but the acquisition of the sleep stage is not limited to this example. For example, in an all-night polysomnography test (PGS test), electrodes are attached to the person to be measured to acquire sleep stages, and from the changes in the acquired sleep stages, the sleep apnea syndrome determination device 10 of this example detects when sleeping Patients with apnea syndrome may be discriminated. In addition, the sleep stage is acquired from the measurement data of a wearable terminal such as a restraint type smart watch, and from the change in the acquired sleep stage, the sleep apnea syndrome determination device 10 of this example is a patient with sleep apnea syndrome. can be determined.

[6. Modification]
It should be noted that the processing described in the above-described embodiment is a preferred example, and the processing is not limited to that described in the embodiment.
For example, in the above-described embodiment, the sleep apnea syndrome determination device 10 determines the sleep stage every 30 seconds, and determines whether the patient is an SAS patient from the change in the sleep stage every 30 seconds. made it On the other hand, the sleep apnea syndrome determination device 10 may determine whether or not the patient is an SAS patient from the result of determining the sleep stage for each relatively long period of time, such as about 60 seconds or about 90 seconds. In the sleep apnea syndrome determination device 10 of the present embodiment, if the sleep stages are determined for each period of at least 30 seconds or longer, changes specific to SAS patients can be captured.

Further, in the above-described embodiment, the number of times the sleep stage changed in the order of stage 2 non-REM sleep → awakening → stage 1 non-REM sleep, the number of times when three consecutive awakenings occurred, and stage 3 non-REM sleep Alternatively, the presence or absence of a change from stage 4 non-REM sleep to wakefulness is combined.

Furthermore, in the above-described embodiment, the number of times the sleep stage changed in the order of stage 2 NREM sleep → awakening → stage 1 NREM sleep, the number of times when awakening occurred three times in succession, and stage 3 NREM sleep Alternatively, although the presence or absence of Stage 4 non-REM sleep is determined, it may be determined whether or not the subject is an SAS patient based on changes in other sleep stages.

FIGS. 15 and 16 show possible combinations other than the examples described in FIGS. 9-14. FIG. 15 shows, for data D11 to D19 of nine SAS patients shown in FIG. →Awakening→Stage 1 non-REM sleep (“1W1” state). As described above, in the case of SAS patients, REM sleep and stage 1 non-REM sleep temporarily lead to awakening, and then return to the original sleep stage occurs a certain number of times.

FIG. 16 shows the number of occurrences of the state of awakening → awakening → stage 1 non-REM sleep (“WW1” state) in 90 seconds for the data D21 to D29 of nine healthy subjects shown in FIG. It shows the number of occurrences of the stage 2 non-REM sleep → awakening → awakening state (“2WW” state).
In this way, in the case of SAS patients, after being in an awake state continuously for about 60 seconds or more, they enter stage 1 non-REM sleep, or from stage 2 non-REM sleep to an awake state continuously for about 60 seconds or more. has occurred a certain number of times.

Therefore, from REM sleep or Stage 1 NREM sleep, you will be temporarily awakened, and then return to the original sleep stage, or after being continuously awake for about 60 seconds or more, you will be in Stage 1 NREM sleep. Alternatively, it is possible to determine whether or not the subject is an SAS patient by detecting that the subject is in a state of wakefulness continuously for about 60 seconds or longer from stage 2 non-REM sleep.
The determinations shown in FIGS. 15 and 16 may be performed alone, but by combining them with the determinations shown in FIGS. 9 to 14 described above, it is expected that the accuracy of determination of whether or not a patient is an SAS patient can be improved.

Furthermore, as described in one embodiment, the point of determining the sleep stage from the biological vibrations obtained using the mattress sensor 2 is an example, and the present invention is based on the results of determining the sleep stage by various methods. Similarly, a change in sleep stage may be determined to determine whether or not the patient is an SAS patient.

1 ... bed, 2 ... mattress sensor, 10 ... sleep apnea syndrome determination device, 11 ... biological data acquisition unit, 12 ... biological data processing unit, 13 ... sleep stage determination unit, 14 ... sleep apnea syndrome determination unit ( SAS determination unit), 15... output unit, A... subject, C... computer device, C1... CPU, C2... ROM, C3... RAM, C4... non-volatile storage, C5... network interface display unit, C6... input device , C7... display device, C8... bus

Claims (9)

A sleep apnea syndrome determination unit that determines that the subject has sleep apnea syndrome based on changes in the sleep stages as a result of determining the sleep stages of the subject at regular intervals. Respiratory Syndrome Judgment Device. The sleep apnea syndrome determination unit compares the number of times the sleep stage change occurs in the first specific change and the number of times the sleep stage changes in the second specific change during sleep of the subject, and The apparatus for determining sleep apnea syndrome according to claim 1, wherein when the frequency of the first specific change is greater, it is determined that the subject has sleep apnea syndrome. The first specific change is a change that appears in the order of stage 2 NREM sleep, wakefulness, and stage 1 NREM sleep,
The apparatus for determining sleep apnea syndrome according to claim 2, wherein the second specific change is a change in which wakefulness appears continuously at three constant times. The sleep apnea syndrome determining unit detects that the sleep stage change determined during sleep of the subject becomes a third specific change, the subject has sleep apnea syndrome. The device for determining sleep apnea syndrome according to claim 1. The sleep apnea syndrome determination device according to claim 4, wherein the third specific change is a change from stage 3 or stage 4 non-REM sleep to wakefulness. The sleep apnea syndrome determination unit compares the number of times the sleep stage changes determined during sleep of the subject undergoes a first specific change and the number of times a second specific change occurs, When the number of times of the first specific change is larger, or when the sleep stage change determined during sleep of the subject satisfies either one of the cases where the third specific change is satisfied, The apparatus for determining sleep apnea syndrome according to claim 1, wherein the subject is determined to have sleep apnea syndrome. The device for determining sleep apnea syndrome according to any one of claims 1 to 6, wherein the certain period of time for determining a sleep stage is at least 30 seconds. Sleep apnea syndrome determination processing for determining that the subject has sleep apnea syndrome based on changes in sleep stages as a result of determining the sleep stage of the subject at regular intervals Respiratory syndrome determination method. A sleep apnea syndrome determination procedure for determining that the subject has sleep apnea syndrome based on changes in sleep stages as a result of determining the sleep stage of the subject at regular intervals;
A sleep apnea syndrome judgment program that causes a computer to execute.

Images