JP7159099B2 - Sleep state detection device, sleep state detection method, and sleep state detection program - Google Patents

Description

The present invention relates to a sleep state detection device, a sleep state detection method, and a sleep state detection program.

BACKGROUND ART Sleep apnea syndrome (SAS), which is a disease that causes respiratory arrest or hypopnea during sleep, is known. As a method for diagnosing sleep apnea syndrome, there is a polysomnography test in which biological activities such as electroencephalogram, eye movement, electrocardiogram, electromyogram, breathing curve, snoring, and arterial blood oxygen saturation are measured overnight. However, there was a problem that the apparatus was large-scaled, the subject had to be hospitalized, and the measurement could not be easily performed. In addition, it requires comprehensive judgment by a doctor or clinical laboratory technologist. Therefore, there is a high need for a sleep state detection device that can easily diagnose sleep apnea syndrome.

It is known that respiratory arrest or hypopnea during sleep is associated with heart rate variability. Observation of heart rate variability during sleep reveals that, compared to heart rate variability during normal sleep, when respiratory arrest or hypopnea occurs, there is a characteristic heart rate variability that alternates between bradycardia and tachycardia, the so-called periodicity. cyclic variation of heart rate (CVHR) occurs.

Japanese Patent Laid-Open No. 2002-200001 discloses a method for detecting periodic heart rate variability to determine a respiratory arrest or hypopnea state. Further disclosed is the use of changes in the activity ratio of the sympathetic and parasympathetic nerves as an index reflecting the individual health of the subject to improve the accuracy of respiratory arrest or hypopnea.

Further, Patent Document 2 discloses a sleep state determination method for estimating a sleep state such as REM sleep or non-REM sleep by obtaining an activity ratio of sympathetic nerves and parasympathetic nerves from heartbeat intervals.

JP 2010-051387 A JP-A-7-143972

However, in the detection method described in Patent Document 1, in the case of REM sleep, etc., it is known that heart rate fluctuations similar to periodic heart rate fluctuations are obtained. It may be falsely detected as hypopnea.

Further, the estimation method described in Patent Document 2 can determine REM sleep and non-REM sleep, but does not disclose determination of respiratory arrest or hypopnea during sleep.

SUMMARY OF THE INVENTION An object of the present invention is to provide a simple sleep state detection device capable of detecting respiratory arrest or hypopnea during sleep with high accuracy.

In order to achieve the above object, a sleep state detection device according to the present invention includes an acquisition unit that acquires beat interval data of a subject in a predetermined period including a sleep period, and periodic heart rate variability event data from the beat interval data. an event detection unit that detects an event, an event processing unit that performs exclusion processing to exclude periodic heart rate variability event data in a period in which the beat interval is within a predetermined range from the periodic heart rate variability event data, and an exclusion processing of the periodic heart rate variability event data an output for outputting an apnea index signal associated with the heart rate variability event data.

The sleep state detection apparatus according to the present invention further includes an autonomic nerve index output unit that outputs autonomic nerve index data that indicates an autonomic nerve index based on the beat interval data, and the event processing unit outputs the excluded periodic Preferably, the heart rate variability event data further excludes periodic heart rate variability event data whose autonomic nerve indicator data is below a predetermined threshold.

A method of detecting a sleep state according to the present invention includes acquiring beat interval data of a subject in a predetermined period including a sleep period, detecting periodic heart rate variability event data from the beat interval data, performing an exclusion process for excluding periodic heart rate variability event data for a period in which the beat interval is within a predetermined range from the variability event data; and an apnea index signal associated with the excluded periodic heart rate variability event data. and outputting.

A sleep state detection program according to the present invention is a program for operating a computer, which acquires beat interval data of a subject in a predetermined period including a sleep period, and obtains periodic heart rate variability event data from the beat interval data. performing exclusion processing for excluding periodic heart rate variability event data in a period in which the beat interval is within a predetermined range from the periodic heart rate variability event data; and removing the periodic heart rate variability event data. and outputting an apnea indicator signal associated with the data.

INDUSTRIAL APPLICABILITY The sleep state detection device according to the present invention can easily improve the detection accuracy of respiratory arrest or hypopnea during sleep.

FIG. 2 is a diagram for explaining an electrocardiogram showing heartbeats and a heartbeat interval, which is a time interval between heartbeats. (a) is an electrocardiogram waveform, and (b) is a graph of heart beat interval (RRI). FIG. 4 is a diagram for explaining a conventional method of estimating periodic heart rate variability from pulse wave interval variability. FIG. 10 is a diagram showing an example of a conventional algorithm for estimating a periodic heart rate variability flag from pulse wave intervals; FIG. 10 is a graph depicting the relationship between smoothed pulse intervals and periodic heart rate variability events; It is a figure which shows an example of the process outline|summary of the sleep state detection apparatus of 1st Embodiment. It is a figure which shows an example of a structure of the sleep state detection apparatus of 1st Embodiment. It is a figure which shows an example of the flowchart of the sleep state detection process performed with a sleep state detection apparatus. It is a figure which shows an example of an autonomic nerve index (LF/HF). It is a figure which shows an example of the process outline|summary of the sleep state detection apparatus of 2nd Embodiment. It is a figure which shows an example of a structure of the sleep state detection apparatus of 2nd Embodiment. 4 is a diagram showing an example of a flowchart of sleep state estimation processing performed by the sleep state detection device 3. FIG. FIG. 10 illustrates the effect of periodic heart rate variability detection according to the present invention;

A sleep state detection device according to one aspect of the present disclosure will be described below with reference to the drawings. However, it should be noted that the technical scope of the present disclosure is not limited to those embodiments, but extends to the invention described in the claims and equivalents thereof. In the following description and drawings, components having the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description.

[Heart beat and heartbeat interval]
FIG. 1 is a diagram for explaining an electrocardiogram showing heartbeats and a heartbeat interval, which is a time interval between heartbeats. (a) is an electrocardiogram waveform, and (b) is a heartbeat interval graph.

It is well known that the heart beats "slower" when a person is at rest, such as lying down, and "fastens" when the person is exercising or tense. The phenomenon that the heart beats "fast" or "slow" can be expressed, for example, by a heartbeat interval, which is the time between one beat and the next beat. When the heart beats fast, the beat-to-beat interval time decreases, and conversely, when the heart beats slowly, the beat-to-beat interval time increases.

FIG. 1(a) is a diagram showing an electrical signal generated when the heart beats, called an electrocardiogram. The sharpest peak of an electrocardiogram, R, is an electrical signal generated when the ventricles of the heart contract rapidly to pump blood out of the heart. ; R-R Interval).

FIG. 1(b) is a graph plotting the heart rate interval (RRI). It can be seen that the heart rate interval (RRI) is not always constant but fluctuates. Not only does exercise increase heart rate and change heart rate interval (RRI), variations in heart rate interval (RRI) are also observed during rest and sleep. In the upper figure, the heartbeat interval (RRI) fluctuates between 950 ms and 900 ms. Such variation in heartbeat interval is called heart rate variability.

It is known that when a subject is in an apnea or hypopnea sleep state, a characteristic heart rate variability that alternates between bradycardia and tachycardia, so-called periodic heart rate variability, occurs. However, there are various factors in periodic heart rate fluctuations, and it is necessary to simply and accurately estimate whether the subject is in an apnea or hypopnea sleep state. In this embodiment, the heart rate interval (RRI) and the pulse interval (PPI) are collectively referred to as the beat interval. Pulse interval (PPI), for example, refers to the time interval between the sharpest peaks of the pulse wave. The electrocardiogram captures the potential changes that occur with the activity of the myocardium of the heart, while the pulse wave is the measurement of fluctuations in the blood flow of the blood pumped by the heart through the arteries to the periphery. PPI is information acquired from each, and it is known that they show almost the same value when relatively at rest such as during sleep. The following embodiments will be described based on pulse wave intervals.

[Conventional method for detecting periodic heart rate variability from interpulse interval variability]
FIG. 2 is a diagram for explaining a conventional method of estimating periodic heart rate variability from interpulse wave variability.

The upper graph in FIG. 2 is a graph in which the pulse wave interval value is plotted on the vertical axis and the sleep time is plotted on the horizontal axis. A solid line indicates an actual pulse wave interval, and a dashed line indicates a pulse wave interval smoothed by, for example, a moving average. A drop in the smoothed pulse wave interval is called a dip. The lower graph in FIG. 2 is a graph showing the periodic heart rate variability flag estimated from the smoothed pulse wave interval, with the sleep time as the horizontal axis. A periodic heart rate variability flag is a flag indicating that the subject is in an apnea state.

FIG. 3 is a diagram showing an example of a conventional algorithm for estimating a periodic heart rate variability flag from pulse wave intervals. First, a shape near the minimum value of the smoothed pulse wave interval is extracted as a dip candidate (ST401). The extracted dip shape is determined, for example, it is determined whether the dip shape resembles a predetermined parabolic shape (ST402). Further, the depth of each dip is calculated as the difference between the depth of the dip, for example, the value at the center time of the minimum value and the average value of the maximum values of the moving averages before and after, and the threshold determination is performed (ST403). Further, the width of the dip is determined, for example, it is determined whether or not the adjacent dip has a dip width within a predetermined range (ST404).

Furthermore, the similarity of dips is determined (ST405). For example, a dip having a dip width and a dip height within a predetermined range with respect to adjacent dips, and a dip width to dip height ratio within a predetermined range with respect to adjacent dips. are identified as dips with similarity. Furthermore, the periodicity of dips is determined (ST406). For example, for all two adjacent dips out of four consecutive dips, the time difference between the two adjacent dips is within a predetermined time range, and the variation in magnitude of the time difference is within a predetermined range. , the four dips are periodic. A dip that satisfies all of the above conditions is acquired as a periodic heart rate variability flag (ST407). The cyclic heart rate variability flag indicates that the subject is in an apnea state and a cyclic heart rate variability event has occurred at the time corresponding to the cyclic heart rate variability flag.

FIG. 4 is a graph depicting the relationship between smoothed pulse intervals and periodic heart rate variability events.

It can be seen that the subject's smoothed pulse wave interval is stable in the left portion A, but fluctuates greatly in the right portion B. FIG. The left part A obtains the cyclic heart rate variability flags c(1, t1), c(2, t2), c(3, t3) by a conventional algorithm for estimating cyclic heart rate variability from pulse interval variability. Here, c(n, tn) is the n-th periodic heart rate variability flag and indicates detection at time tn (n: natural number). Furthermore, in the right part B, periodic heart rate variability flags c(4, t4), c(5, t5), c(6, t6), c(7, t7), c(8, t8) are obtained.

In the right part B, where the smoothed pulse wave interval fluctuates greatly, the subject is not in an apnea state, but in another sleep state such as REM sleep. It was obtained by comparing with the detected periodic heart rate variability flag. The present invention eliminates periodic heart rate variability flags in which the subject is not in an apnea state from the periodic heart rate variability flags obtained by a conventional algorithm for obtaining periodic heart rate variability from pulse interval variability, thereby detecting the subject's inactivity. This improves the detection accuracy of the respiratory sleep state.

[Outline of processing of the first embodiment of the sleep state detection device]
FIG. 5 is a diagram showing an example of an overview of processing of the first embodiment of the sleep state detection device according to the present invention.

A wristband 1 having an optical pulse wave sensor 11 for detecting the beating state of the subject's heart, for example, the pulse, is attached to the subject's wrist. The optical pulse wave sensor 11 has, for example, an optical pulse wave sensor IC equipped with an LED driver and a green detection photodiode. A pulse wave signal can be obtained by measuring, with a photodiode, the intensity of the reflected light when the LED is directed toward the living body. The pulse wave sensor may be, for example, a pulse pressure detection type pulse wave sensor as well as an optical type.

In this embodiment, the wristband 1 is attached to the subject's wrist, but it may be attached to other parts of the subject.

A pulse signal detected by the optical pulse wave sensor 11 of the wristband 1 is transmitted to the sleep state detection device 2 by wireless communication such as WiFi or Bluetooth (registered trademark). The pulse signal may not be transmitted in real time. For example, after the subject wakes up from sleep, the pulse signal may be transmitted to the sleep state detection device 2 all at once. In addition, it is not necessary to directly wirelessly transmit the pulse signal from the wristband 1 to the sleep state detection device 2, and the pulse signal may be transmitted to the sleep state detection device 2 via, for example, a smart phone or an access point.

The heartbeat signal transmitted from the wristband 1 is received by the communication interface 22 via the antenna 21 of the sleep state detection device 2 . Acquisition unit 221 acquires beat interval data of the subject in a predetermined period including a sleep period from the beat signal.

The event detector 222 smoothes the beat interval data. Further, the event detector 222 detects cyclical heart rate variability events using the algorithm of estimating the cyclical heart rate variability flag from the conventional pulse interval shown in FIG. The detected periodic heart rate variability event data is input to the event processing section 223 .

The event processing unit 223 excludes periodic heart rate variability event data corresponding to a period in which the beat interval is within a predetermined range from the detected periodic heart rate variability event data. In periodic heart rate variability associated with apnea, the sympathetic nervous system is dominant, and the average beat interval tends to be short. However, the average beating interval tends to be longer than in the case of apnea, so the predetermined range is set around the average beating interval during REM sleep, and this range is excluded. Select only cyclic heart rate variability event data that show .

Based on the detection result by the event processing unit 223, the output unit 224 outputs periodic heart rate variability event data as an apnea index signal. Instead of, or in conjunction with, the cyclic heart rate variability event data, an index of respiration per detected unit time, e.g. Apnea Hypopnea Index) may be output.

The apnea index signal is input to a display unit (not shown) built into the sleep state detection device 2, and a value corresponding to, for example, the apnea hypopnea index is displayed on the display screen. Also, the apnea index signal may be written in a hard disk (not shown) or a removable memory card built into the sleep state detection device 2 . Furthermore, the value corresponding to the apnea-hypopnea index may be output to a printer, information terminal device, or server device connected to the sleep state detection device 2 . It may also be transmitted over a network, for example to a server device having a subject database.

The optical pulse wave sensor 11 may transmit the pulse interval signal instead of the pulse wave signal, or may transmit the pulse wave signal and the pulse interval signal.

According to the sleep state detection device 2 of the present embodiment, it is possible to easily improve the detection accuracy of respiratory arrest or hypopnea during sleep.

[Configuration example of the sleep state detection device of the first embodiment]
FIG. 6 is a diagram showing an example of the configuration of the sleep state detection device according to the first embodiment.

The sleep state detection device 2 has a communication interface 22 that communicates with the outside via an antenna 21 and a control unit 24 that connects to the communication interface 22 . Furthermore, it may have a storage medium interface 23 for connecting with an external storage medium. For example, the storage medium interface 23 allows the sleep state detection device 2 to read detection data stored in the storage medium of the wristband 1 .

The control unit 24 has a control storage unit 210 and a control processing unit 220 . The control storage unit 210 is composed of one or more semiconductor memories. For example, it has at least one of RAM and non-volatile memory such as flash memory, EPROM, and EEPROM. The control storage unit 210 stores driver programs, operating system programs, application programs, data, and the like used for processing by the control processing unit 220 .

For example, the control storage unit 210 stores a device driver program that controls the communication interface 22 as a driver program. The computer program may be installed in the control storage unit 210 from a computer-readable portable recording medium such as CD-ROM, DVD-ROM, etc. using a known setup program or the like. Alternatively, it may be downloaded from a program server or the like and installed.

Furthermore, the control storage unit 210 may temporarily store temporary data related to predetermined processing. The control storage unit 210 includes a threshold table 211 such as a dip determination threshold, a beat interval threshold, a beat interval range threshold, and a beat interval average threshold, a time table 212 such as an estimated unit time, a detection unit time, etc., and a table for statistical processing. It stores the statistics master 213 and the like.

A threshold table 211 such as a beat interval threshold, a beat interval range threshold, and a beat interval average range threshold, and a time table 212 such as an estimated unit time, a detection unit time, and a calculated time stored in the control storage unit 210 are: A unique set value may be used. Alternatively, the threshold table 211 and the time table 212 may be statistically set based on the average, variance, etc. of statistical values obtained by taking statistics of apnea or hypopnea sleep states of multiple subjects. Furthermore, biometric information of the subject, such as age, sex, and set values according to weight, may be stored in the statistics master 213 and used to estimate the apnea or hypopnea sleep state of the subject.

The control processing unit 220 has one or more processors and their peripheral circuits. The control processing unit 220 comprehensively controls the overall operation of the sleep state detection device 2, and is, for example, an MCU (Micro Controller Unit).

The control processing unit 220 executes processing based on programs (operating system program, driver program, application program, etc.) stored in the control storage unit 210 . Also, the control processing unit 220 may execute a plurality of programs (application programs, etc.) in parallel. The control processing unit 220 has an acquisition unit 221, an event detection unit 222, an event processing unit 223, an output unit 224, and the like.

These units included in the control processing unit 220 may be implemented in the control unit 24 as independent integrated circuits, circuit modules, microprocessors, or firmware.

[Sleep state estimation processing flow of the first embodiment]
FIG. 7 is a diagram showing an example of a flowchart of sleep state detection processing performed by the sleep state detection device.

Acquisition section 221 acquires beat interval data indicating the beat interval using the beat signal received from subject's wristband 1 (ST701).

Event detection section 222 smoothes the beat interval data (ST702). Further, event detection section 222 detects a periodic heart rate variability event using the conventional algorithm for estimating a cyclic heart rate variability flag from the pulse wave interval shown in FIG. 3 (ST703). Event processing section 223 calculates the moving average (mv) of the beat interval data (ST704). The event processing section 223 calculates an average value (An) for a predetermined period, for example, every hour or for one sleep period (ST705).

Event processing section 223 determines whether the moving average (mv) is equal to or less than the average value (An) at time tk when the periodic heart rate variability flag is detected (ST706). When the moving average (mv) is equal to or less than the average value (An) (ST706: YES), event processing section 223 accumulates periodic heart rate variability flag c(k, tk) at time tk as a periodic heart rate variability flag. (ST707). When the moving average (mv) exceeds the average value (An) (ST706: NO), event processing section 223 returns to ST706 and calculates the moving average (mv) and average value (An) of the next periodic heart rate variability flag. compare. Here, it is assumed that the moving average (mv) is equal to or less than the average value (An), but this is not limitative, and the average value (An) may be multiplied by a constant (for example, 1.1). Also, An is the average value in one sleep period, but it may be the average in the ultradian rhythm of sleep, for example, every 1.5 hours.

Event processing section 223 determines whether time tk has exceeded the sleep time (ST708). When the time tk has not exceeded the sleep time (ST708: NO), the process returns to ST706. When time tk exceeds the sleep time (ST708; YES), output section 224 outputs an apnea index signal based on the accumulated periodic heart rate variability flag (ST709). The process flow ends.

[Outline of processing of the second embodiment of the sleep state detection device]
In the first embodiment, the sleep state of a subject was estimated based on beat-to-beat data from a sleeping subject. In the second embodiment, autonomic nerve index data is further output from the beat interval data, and the sleep state of the subject is detected based on the beat interval data and the autonomic nerve index data.

[Example of autonomic nerve index]
It is known that the sympathetic activity of the subject is activated when the subject enters an apnea sleep state. It is known that the activity level of sympathetic nerve activity can be grasped by analyzing heart rate variability, for example.

FIG. 8 is a diagram showing an example of an autonomic nerve index (LF/HF). An example of results of frequency analysis of heart rate variability is shown. For example, it is a graph showing the relationship between the frequency (horizontal axis) and the power (vertical axis) obtained by frequency analysis of the heartbeat interval (RRI) over a predetermined period of time, for example, 5 minutes. Let LF be the peak value in the low frequency section (eg, 0.05 Hz-0.15 Hz), and let HF be the peak value in the high frequency section (eg, 0.15 Hz-0.4 Hz).

LF mainly indicates the activity of the sympathetic nerve function, and HF indicates the activity of the parasympathetic nerve function. However, since LF is involved not only in sympathetic nerves but also in parasympathetic nerves, the ratio of LF to HF (LF/HF) is often used as an index of sympathetic nerve activity. Used as an index of neural activity. Also, instead of the peak value, the ratio of the power spectral densities of the low frequency section and the high frequency section may be used as the autonomic nerve index (LF/HF). Using the autonomic nerve index (LF/HF), which indicates the degree of sympathetic nerve activity, it is possible to increase the accuracy of estimation when the subject is in an apnea sleep state.

FIG. 9 is a diagram showing an example of a processing outline of the second embodiment of the sleep state detection device according to the present invention.

A wristband 1 having an optical pulse wave sensor 11 for detecting the beating state of the subject's heart, for example, the pulse, is attached to the subject's wrist. A pulse signal detected by the optical pulse wave sensor 11 is transmitted to the sleep state detection device 3 by wireless communication. The pulse signal may not be transmitted in real time. For example, after the subject wakes up from sleep, the pulse signal may be transmitted to the sleep state detection device 3 all at once. Further, it is not necessary to wirelessly transmit the heartbeat signal directly from the wristband 1 to the sleep state detection device 3, and it may be transmitted to the sleep state detection device 3 via, for example, a smart phone or an access point.

The heartbeat signal transmitted from the wristband 1 is received by the communication interface 32 via the antenna 31 of the sleep state detection device 3 . Acquisition unit 321 acquires beat interval data of the subject in a predetermined period including a sleep period from the beat signal. The acquired beat interval data is input to the event detector 322 . Furthermore, the beat interval data is input to the autonomic nerve index output section 325 .

The event detector 322 smoothes the beat interval data. Further, the event detector 322 detects cyclical heart rate variability events using the algorithm of estimating the cyclical heart rate variability flag from the conventional pulse interval shown in FIG. The detected periodic heart rate variability event data is input to the event processing section 323 .

The autonomic nerve index output unit 325 calculates autonomic nerve index (LF/HF) data based on the beat interval data. Using the autonomic nerve index (LF/HF) that indicates the activity level of the sympathetic nerves of the subject, it is possible to increase the accuracy of estimation when the subject is in an apnea sleep state. The autonomic nerve index data output from the autonomic nerve index output unit 325 is input to the event processing unit 323 .

The event processing unit 323 excludes periodic heart rate variability event data corresponding to a period in which the beat interval is within a predetermined range from the detected periodic heart rate variability event data. In periodic heart rate variability associated with apnea, the sympathetic nervous system is dominant, and the average beat interval tends to be short. However, the average beating interval tends to be longer than in the case of apnea, so the predetermined range is set around the average beating interval during REM sleep, and this range is excluded. Select only cyclic heart rate variability event data that show .

The event processing unit 323 further filters the excluded periodic heart rate variability event data indicating apnea based on the autonomic nerve index data. The filtering determines, for example, the periodic heart rate variability event data when the autonomic nerve index is greater than or equal to a predetermined autonomic nerve index threshold as the periodic heart rate variability event data indicating apnea. When the autonomic nerve index (LF/HF) is high, ie, when the sympathetic nervous system is activated, it is estimated that the subject is likely to be in an apnea or hypopnea sleep state.

Based on the detection result by the event processing unit 223, the output unit 224 outputs periodic heart rate variability event data as an apnea index signal. Instead of, or in conjunction with, the cyclic heart rate variability event data, a respiratory index per detection unit time, such as an apnea-hypopnea index, which is the combined number of apneas and hypopneas per hour. You can print the value.

The apnea index signal is input to a display unit (not shown) built into the sleep state detection device 2, and a value corresponding to, for example, the apnea hypopnea index is displayed on the display screen. Also, the apnea index signal may be written in a hard disk (not shown) or a removable memory card built into the sleep state detection device 2 . Furthermore, the value corresponding to the apnea-hypopnea index may be output to a printer, information terminal device, or server device connected to the sleep state detection device 2 . It may also be transmitted over a network, for example to a server device having a subject database.

The apnea index signal is input to a display unit (not shown) built into the sleep state detection device 3, and the value of the apnea hypopnea index, for example, is displayed on the display screen. Also, the apnea index signal may be written in a hard disk (not shown) or a removable memory card built into the sleep state detection device 3 . Furthermore, the apnea index signal may be output to a printer, information terminal device, or server device connected to the sleep state detection device 3 . It may also be transmitted over a network, for example to a server device having a subject database.

According to the sleep state detection device 3 of the present embodiment, it is possible to easily improve the detection accuracy of respiratory arrest or hypopnea during sleep.

[Configuration example of the sleep state detection device of the second embodiment]
FIG. 10 is a diagram showing an example of the configuration of the sleep state detection device of the second embodiment.

The sleep state detection device 3 has a communication interface 32 that communicates with the outside via an antenna 31 and a control unit 34 that connects to the communication interface 32 . Furthermore, it may have a storage medium interface 33 for connecting with an external storage medium. For example, the storage medium interface 33 allows the sleep state detection device 3 to read detection data stored in the storage medium of the wristband 1 .

The control unit 34 has a control storage unit 310 and a control processing unit 320 . The control memory unit 310 is composed of one or more semiconductor memories. For example, it has at least one of RAM and non-volatile memory such as flash memory, EPROM, and EEPROM. The control storage unit 310 stores driver programs, operating system programs, application programs, data, etc. used for processing by the control processing unit 320 .

For example, the control storage unit 310 stores a device driver program that controls the communication interface 32 as a driver program. The computer program may be installed in the control storage unit 310 from a computer-readable portable recording medium such as CD-ROM, DVD-ROM, etc. using a known setup program or the like. Alternatively, it may be downloaded from a program server or the like and installed.

Furthermore, the control storage unit 310 may temporarily store temporary data related to predetermined processing. The control storage unit 310 includes a threshold table 311 such as a dip determination threshold, a beat interval threshold, a beat interval range threshold, a beat interval average threshold, and an autonomic nerve index threshold; a time table 312 such as an estimated unit time and a detection unit time; Stores the statistics master 313 and the like for statistical processing.

Threshold table 311 such as dip determination threshold value, beat interval threshold value, beat interval range threshold value, beat interval average threshold value, autonomic nerve index threshold value, and time such as estimated unit time and detection unit time stored in the control storage unit 210 The table 312 may be unique set values. Also, the threshold table 311 and the time table 312 may be statistically set based on the average, variance, etc. of statistical values obtained by taking statistics of apnea or hypopnea sleep states of multiple subjects. Furthermore, biometric information of the subject, such as age, sex, and set values according to weight, may be stored in the statistics master 313 and used to estimate the apnea or hypopnea sleep state of the subject.

The control processing unit 320 has one or more processors and their peripheral circuits. The control processing unit 220 comprehensively controls the overall operation of the sleep state detection device 3, and is, for example, an MCU.

The control processing unit 320 executes processing based on programs (operating system program, driver program, application program, etc.) stored in the control storage unit 310 . Also, the control processing unit 320 may execute a plurality of programs (application programs, etc.) in parallel. The control processing unit 320 includes an acquisition unit 321, an event detection unit 322, an event processing unit 323, an output unit 324, an autonomic nerve index output unit 325, and the like.

Each of these units of the control processing unit 320 may be implemented in the control unit 24 as independent integrated circuits, circuit modules, microprocessors, or firmware.

[Sleep state estimation processing flow of the second embodiment]
FIG. 11 is a diagram showing an example of a flowchart of sleep state estimation processing performed by the sleep state detection device 3. As shown in FIG.

Acquisition section 321 acquires beat interval data indicating the beat interval using the beat signal received from subject's wristband 1 (ST801).

Autonomic nerve index output section 325 outputs autonomic nerve index data based on the beat interval data (ST802).

Event detection section 322 smoothes the beat interval data (ST803). Further, event detection section 322 detects a periodic heart rate variability event using the conventional algorithm for estimating a cyclic heart rate variability flag from the pulse wave interval shown in FIG. 3 (ST804). Event processing section 323 calculates the moving average (mv) of the beat interval data (ST805). The event processing section 323 calculates an average value (An) for a predetermined period, for example, every hour or for one sleep period (ST806).

Event processing section 323 determines whether the moving average (mv) is equal to or less than the average value (An) at time tk when the periodic heart rate variability flag is detected (ST807). When the moving average (mv) is equal to or less than the average value (An) (ST807: YES), event processing section 323 determines whether the autonomic nerve index at time tk is equal to or greater than a predetermined autonomic nerve index threshold Th (ST808). . When the moving average (mv) exceeds the average value (An) (ST807: NO), the event processing section 323 returns to ST807 and calculates the moving average (mv) and the average value (An) of the periodic heart rate variability flag at the next time. ).

When the autonomic nerve index at time tk is greater than or equal to the predetermined autonomic nerve index threshold value Th (ST808: YES), event processing section 323 changes the periodic heart rate variability flag c(k, tk) at time tk to the periodic heart rate variability flag. (ST809). When the autonomic nerve index at time tk is lower than the predetermined autonomic nerve index Th (ST808: NO), event processing section 323 returns to ST807 and calculates the moving average (mv) and average value of the periodic heart rate variability flag at the next time. Compare (An).

Event processing section 323 determines whether time tk has exceeded the sleep time (ST810). When the time tk has not exceeded the sleep time (ST810: NO), the process returns to ST807. When time tk exceeds the sleep time (ST810; YES), output section 324 outputs an apnea index signal based on the accumulated periodic heart rate variability flag (ST811). The process flow ends.

[Effect of periodic heart rate variability detection by the present invention]
FIG. 12 is a diagram showing the effect of periodic heart rate variability detection according to the present invention.

The upper portion of FIG. 12 shows the pulse interval (PPI) for one sleep period of the subject, for example, 7 hours of sleep. The moving average (mv) is indicated by a solid line and the average value (An) is indicated by a dashed line.

(i) AHI at the bottom shows the actual state of apnea or hypopnea measured by a polysomnograph in a strip shape. (ii) CVHR (original) on the second row from the bottom shows a belt-like state when it is estimated that a periodic heart rate variability event has occurred using the conventional algorithm. Comparing (i) and (ii), it can be seen that (ii) includes many states that are not actually apnea or hypopnea.

(iii) CVHR (modified) on the third row from the bottom shows the effect of detecting the sleep state of the subject by the first embodiment of the present application. FIG. 11 shows periodic heart rate variability events in strips when excluding the case where the moving average (mv) is longer than the average value (An) of the pulse interval (PPI) from (i). It can be seen that the estimation is improved by excluding the cyclic heart rate variability event data for periods where the beat-to-beat interval is within a predetermined range from the cyclic heart rate variability event data detected using conventional algorithms.

In the (iv) LF/HF filter in the fourth row from the bottom, the gaps in the horizontal thick lines correspond to the autonomic nerve indices of the portions where the autonomic nerve index data are greater than or equal to the predetermined autonomic nerve index threshold. This is the part to do.

(v) CVHR (modified) + LF/HF filter in the fifth row from the bottom shows the effect of detecting the sleep state of the subject by the second embodiment of the present application. iii) shows the periodic heart rate variation in consideration of (iv) in a belt shape. It can be seen that (v) is closer to the actual apnea or hypopnea condition of (i).

In the above description, the pulse is used as an example of the beating state of the subject's heart, but the heartbeat may be used as the beating state of the subject's heart.

The sleep state detection device according to the present invention may be embodied by cooperation between a server device and a client device.

It should be understood by those skilled in the art that various changes, substitutions and modifications can be made thereto without departing from the spirit and scope of the present invention.

1 wristband 2, 3 sleep state detection device 21, 31 antenna 22, 32 communication interface 23, 33 storage medium interface 24, 34 control unit 210, 310 control storage unit 220, 320 control processing unit 221, 321 acquisition unit 222, 322 Event detection unit 223, 323 Event processing unit 224, 324 Output unit 325 Autonomic nerve index output unit

Claims (4)

an acquisition unit that acquires beat interval data of a subject in a predetermined period including a sleep period;
an event detector that detects periodic heart rate variability event data from the beat interval data;
an event processing unit configured to perform exclusion processing for excluding, from the periodic heart rate variability event data, the periodic heart rate variability event data in a period in which the beat interval falls within a predetermined range;
an output unit for outputting an apnea indicator signal associated with the excluded periodic heart rate variability event data;
A sleep state detection device comprising: further comprising an autonomic nerve index output unit that outputs autonomic nerve index data indicating an autonomic nerve index based on the beat interval data;
2. The sleep according to claim 1, wherein the event processing unit further excludes periodic heart rate variability event data during a period in which the autonomic nerve index data is below a predetermined threshold from the excluded periodic heart rate variability event data. State detection device. obtaining beat-to-beat data of a subject for a predetermined period of time including sleep periods;
detecting cyclic heart rate variability event data from the beat-to-beat interval data;
performing an exclusion process for excluding from the periodic heart rate variability event data the periodic heart rate variability event data in a period in which the beat interval is within a predetermined range;
outputting an apnea indicator signal associated with the excluded periodic heart rate variability event data;
A sleep state detection method comprising: A program that operates a computer,
obtaining beat-to-beat data of a subject for a predetermined period of time including sleep periods;
detecting cyclic heart rate variability event data from the beat-to-beat interval data;
performing an exclusion process for excluding from the periodic heart rate variability event data the periodic heart rate variability event data in a period in which the beat interval is within a predetermined range;
outputting an apnea indicator signal associated with the excluded periodic heart rate variability event data;
a sleep state detection method comprising:
A sleep detection program that causes a computer to run

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