WO2009150765A1 - Appareil de surveillance des conditions de sommeil, système de surveillance et programme informatique - Google Patents

Appareil de surveillance des conditions de sommeil, système de surveillance et programme informatique Download PDF

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Publication number
WO2009150765A1
WO2009150765A1 PCT/JP2008/072697 JP2008072697W WO2009150765A1 WO 2009150765 A1 WO2009150765 A1 WO 2009150765A1 JP 2008072697 W JP2008072697 W JP 2008072697W WO 2009150765 A1 WO2009150765 A1 WO 2009150765A1
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Prior art keywords
interval
sleep
beat
unit
waveform
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PCT/JP2008/072697
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English (en)
Japanese (ja)
Inventor
博明 鈴木
和義 坂本
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ハートメトリクス株式会社
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Priority to JP2009141394A priority Critical patent/JP2011115188A/ja
Publication of WO2009150765A1 publication Critical patent/WO2009150765A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6892—Mats
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4029—Detecting, measuring or recording for evaluating the nervous system for evaluating the peripheral nervous systems
    • A61B5/4035—Evaluating the autonomic nervous system

Definitions

  • the present invention relates to a sleep state monitoring device, a monitoring system, and a computer program.
  • sleep is classified into six categories, namely, awakening, REM (Rapid Eye Movements) sleep, and stage 1 (S1) to stage 4 (S4).
  • REM Rapid Eye Movements
  • S1 to S4 stage 4
  • electrical measurement is usually used.
  • awakening, REM sleep, and S1 to S4 increase the depth of sleep in this order.
  • REM sleep does not occur immediately after the start of sleep, and deep sleep (S3 or When the sleep becomes shallower toward awakening after S4), the REM sleep state is entered.
  • electrodes are attached to several parts of a person's (user or subject) body, for example, an electroencephalogram from the scalp, a myoelectric potential from the jaw, an ocular potential from the periphery of the eye, And you need to use ECG.
  • a technique for determining a sleep state from an electroencephalogram is known as the Rechtschaffen-Kales (RK) method.
  • a technique using an electrocardiogram is disclosed in, for example, Patent Document 1 (Japanese Patent Laid-Open No. 2003-225211).
  • Non-Patent Document 1 a method of defining the body vibration magnitude as an index
  • Non-Patent Document 3 discloses that an autonomic component ratio is obtained based on an electrocardiogram waveform and a determination is made between shallow sleep (S1 or S2) and deep sleep (S3 or S4).
  • Non-Patent Document 5 discloses changes in the spectrum of heartbeats in heart disease
  • Non-Patent Document 6 applies the power spectrum of heartbeat variability to provide an autonomic nervous function at an early stage due to diabetes. It is disclosed that a failure can be detected.
  • a device or method that allows the user to easily check the quality of sleep while staying at home without the assistance of a specialist or health care professional makes it possible to grasp his / her health condition and manage his / her health. Becomes easier. Moreover, if a decrease in the quality of sleep can be detected easily, accurate information for diagnosis by a specialist or a healthcare professional can be given.
  • An object of the present invention is to solve the above several problems.
  • a pressure waveform acquisition unit that obtains a pressure waveform as an electric signal by a pressure detection unit that is in direct contact with any part of the user's body or through clothing, and a predetermined process is performed on the pressure waveform.
  • a pulsation extraction unit that extracts a pulsation waveform of the user and calculates a pulsation interval that is a time interval from the previous pulsation for each pulsation in the pulsation waveform;
  • a sleep state monitoring device including a sleep stage determination unit that determines a sleep stage that is a stage of the user's sleep depth from the beat interval.
  • the sleep state monitoring of the present invention can also be implemented as a method or as a computer program in a computer connected to the pressure detection unit.
  • the sleep state is used to indicate all states of awakening, REM sleep, awakening and sleep of stage 1 (S1) to stage 4 (S4), and the sleep stage refers to awakening from a sleeping state.
  • the exclusion ie, REM sleep, S1-S4.
  • the pulsation includes the pulsation of the heart and vibrations generated by the pulsation, for example, vibrations of the body surface due to arterial pulsations.
  • a pressure waveform acquisition unit that obtains a pressure waveform as an electric signal by a pressure detection unit that is in direct contact with any part of the user's body or through clothing, and a predetermined process is performed on the pressure waveform.
  • a pulsation extraction unit that extracts a pulsation waveform of a user, a pulsation interval calculation unit that calculates a pulsation interval that is a time interval from the previous pulsation for each pulsation in the pulsation waveform, and a computer
  • a monitoring unit connected to a network including a data transmission unit for transmitting data through the network; and receiving time series data including at least a pulsation interval from the device through the computer network a plurality of times.
  • a sleep state monitoring system having a storage unit for recording the time series data corresponding to a user, the computer network comprising: Any computer connected to the work displays the time-series data received multiple times in association with the user as a graph with respect to time, and changes in fluctuation of the user's pulsation interval in a past period
  • a sleep state monitoring system including a data display unit that presents This sleep state monitoring system can also be implemented as a program in a computer that can be connected to the monitoring unit through a computer network.
  • any embodiment of the present invention which sleep is awake or sleeping, or which user is sleeping, without restraining the user and affecting the sleep state It becomes possible to monitor a sleep state such as whether it is a stage. In addition, according to any embodiment of the present invention, it is possible to monitor the health condition of the user based on fluctuations in the pulsation interval.
  • the flowchart which shows embodiment of the sleep state monitoring method of this invention. The flowchart which shows the process which determines whether it is sleep or awakening in embodiment of the sleep state monitoring method of this invention.
  • FIG. 9 is an example of a computer screen showing a result of obtaining a power spectrum at an RR interval from body vibration obtained from a subject in an example of the present invention, and is an arousal state (FIG. 9A) and S1 to S4 (FIG. 9B).
  • FIG. 9 (E)) and the power spectrum in the case of REM sleep FIG. 9 (F)).
  • the example of the computer screen which shows the result of having determined the sleep state by the body vibration obtained from the test subject in the Example of this invention.
  • the example of the computer screen which compares and shows the result of having determined the sleep state automatically by the body vibration obtained from the subject in the embodiment of the present invention and the result determined by the conventional method.
  • the graph which shows the fluctuation (SDNN) of RR interval measured from the test subject in the Example of this invention.
  • the sleep state is determined from the body vibration waveform.
  • the body vibration waveform is acquired by the sleep state monitoring apparatus 1 including the pressure waveform acquisition unit 10 and the computer 20.
  • the pressure waveform acquisition unit 10 includes an air pressure sensor 12, a transducer unit 14 that converts pressure into an electrical signal, and an A / D converter 16 that performs analog / digital conversion of the electrical signal. These are collectively referred to as a pressure waveform acquisition unit 10.
  • the air pressure sensor 12 is disposed on the bed 100, for example, and is made so that a user can lie on the air pressure sensor 12.
  • a sponge-like stuffing is enclosed in a highly air-tight bag-like envelope, and the overall thickness is about 1 cm to 3 cm, and the cushion shape is a square shape of 30 cm square. It is designed to be flexible and soft enough that you can sleep even when you lie down.
  • a tube that can communicate with internal air and propagate pressure is connected to one peripheral portion of the envelope, and the tube extends to the transducer unit 14.
  • the transducer section 14 has an airtight chamber to which the tube is connected.
  • An opening is provided on one side of the airtight chamber, and a ceramic piezoelectric element is disposed so as to close the opening. .
  • a pressure wave inside the air pressure sensor 12 is transmitted to the piezoelectric element to generate an analog value piezoelectric signal, which becomes a pressure wave signal and is amplified by an appropriate amplifier circuit.
  • the amplified pressure wave signal is input to the A / D converter 16, and a digitized pressure wave signal is output.
  • the digitized pressure wave signal is input to the computer 20.
  • the computer 20 operates as a set of functional blocks that perform predetermined processing in accordance with programmed instructions. That is, the computer 20 includes a pulsation extraction unit 22, a pulsation interval calculation unit 24, a sleep stage determination unit 26, a respiration waveform extraction unit 32, a respiration interval calculation unit 34, an awakening determination unit 36, and sleep.
  • the result is presented as the user's sleep state by a technique such as outputting to the monitor 44 and visually indicating the result.
  • the sleep state monitoring apparatus according to the embodiment of the present invention is configured by the pressure waveform acquisition unit and each functional block realized by the computer 20.
  • data can be accumulated by appropriate storage means provided in the computer 20, and the start, stop, end, interruption, etc. of the process can be controlled by the input / output means. .
  • the sleep state monitoring apparatus of the present invention can determine a sleep state consisting of determination of sleep or awakening and determination of a sleep stage. Specifically, as shown in FIG. 2, in order to determine the sleep state, body vibration data is first acquired (S10). And it is judged whether it is sleep or awakening (S20). If it is sleep (S30, YES), the sleep stage is further determined (S40). After that, or when it is not sleep (when it is awakening, S30, NO), a determination result is output.
  • the process for determining the sleep state using the body vibration in the embodiment of the present invention is divided into a process for determining whether the person is awake or sleeping and a process for determining the sleep stage.
  • this series of processing is repeated at regular intervals (for example, every minute) to periodically perform the process from data acquisition to sleep state determination, or Only the processing to be acquired can be performed periodically, the data can be stored in an appropriate storage means, and the determination processing can be performed afterwards.
  • FIG. 3 shows details of the step of determining whether it is sleep or awakening (FIG. 2, S20), and FIGS. 4 to 6 show details of the step of determining the sleep stage (S40).
  • the present invention can be implemented by a form in which a respiratory waveform is extracted from a body vibration waveform and used.
  • the range of the respiration waveform is naturally determined from the respiration rate of the person at rest. For example, a frequency domain component of 0.13 to 0.70 Hz in body vibration can be used as the respiration waveform. For this reason, the body vibration waveform is filtered and a frequency domain component of 0.13 to 0.70 Hz is extracted (FIG. 3, S202). From the respiration waveform, the fluctuation of the respiration interval is extracted.
  • the time when the respiration waveform shows the maximum value is calculated, and that time is set as the time representative of the respiration timing (respiration peak time) (S204).
  • respiration interval data is generated (S206).
  • a coefficient of variation (CV) for the breathing interval is obtained (S208), and this is used as an index for the uniformity of the breathing interval.
  • CV coefficient of variation
  • the CV of the breathing interval is set as a threshold value of about 0.2 (S210), and when it is larger than that, it can be determined as an awake state (S212), and when smaller, it can be determined as a sleep state (S214). .
  • the process of determining the sleep stage includes the step of identifying the timing of the pulsation by the R wave of the pulsation due to the heartbeat or pulsation (S402), and the data of the time interval (RR interval) between a certain heartbeat and the immediately preceding heartbeat The process is divided into the step (S404) of obtaining the power spectrum, the step of calculating the power spectrum of the RR interval (S406), and the step of determining the sleep stage (S42).
  • R wave timing S402
  • ECG electrocardiogram
  • the description of the R wave uses an expression of the R wave in the ECG for easy understanding, and is not limited to the one that directly corresponds to the EC G R wave.
  • any characteristic waveform can be used as long as it is determined as a timing representative of each beat. is there. For this purpose, it is necessary to extract a wave corresponding to a pulsation from the body vibration waveform (FIG.
  • the body vibration waveform is band-limited to a range of 0.1 Hz to 30 Hz by a band pass filter.
  • the body vibration waveform after the filter is subjected to a second-order differentiation with respect to time, and data of acceleration of body vibration at each time is calculated.
  • the time-dependent data of the acceleration of the body vibration is further band-limited to a range of 3 to 30 Hz by a bandpass filter, and the time when the waveform data shows the peak value is specified.
  • the pulsation extraction unit 22 may include a differential peak processing unit 22A for that purpose.
  • the present invention corresponds to the time of the vibration due to the ventricular excitement of the heart beat, that is, the time of the R wave in the electrocardiogram Guesses.
  • the time may be affected not only by vibration due to the heartbeat but also by vibration (pulsation) caused by the artery due to the heart.
  • RR interval data Time series data of the RR interval (time interval between a certain beat and the immediately preceding beat) is obtained from the R wave time data thus obtained (FIG. 4, S404; FIG. 1, beat interval). Calculation unit 24).
  • the RR interval is given by subtracting the R wave time data of the immediately preceding beat from the R wave time data of each beat.
  • the data of the RR interval is time data at each time of the unequal interval at which the pulsating R wave is detected. This data is conveniently converted into time-series data with a constant sampling interval for later processing, and interpolation processing is performed to obtain time-series data with an RR interval. For this, for example, processing such as secondary interpolation can be performed.
  • the sleep depth is determined from the time-series data of the RR interval obtained as described above.
  • the sleep stage is determined (FIG. 4, S42; FIG. 1, sleep stage determination unit 26).
  • the determination of the sleep stage is a process for determining whether the sleeping state is REM sleep or Non-REM sleep (S1 to S4).
  • the present invention can be implemented by two embodiments.
  • One is an embodiment (spectrum pattern method) in which the sleep stage is determined from the power spectrum pattern of the time series data of the RR interval, and the other is an index indicating the function of the autonomic nervous function from the time series data of the RR interval. Is calculated to determine the sleep stage (autonomic nerve component ratio method).
  • the sleep stage determined by the conventional sleep stage determination method is associated with the power spectrum of the RR interval, and a typical spectrum pattern (“ A typical pattern ”) can be obtained.
  • the sleep stage can be determined by determining which typical pattern is similar to the pattern of the power spectrum of the RR interval measured for a subject (hereinafter, “unknown pattern”).
  • n is set to 2 (S424), and a value of 1 / n of the maximum value of the power spectrum in the RR interval is obtained (S426).
  • a frequency that is 1 / n of the maximum value is obtained, and a difference (frequency width) between the frequencies is calculated (S428).
  • This process is repeated by incrementing n (S432) and repeating until a predetermined upper limit N (S430). Since the frequency width at that time is given to each n, the n dependence of the frequency width can be obtained.
  • the frequency width of the unknown pattern is compared with the n dependence of the frequency width obtained in the same manner for the typical pattern, and the difference between the frequency width of the typical pattern and the unknown pattern is calculated as a residual (S434).
  • the coincidence degree of the unknown pattern with respect to the typical pattern of each sleep stage can be obtained as a numerical value.
  • the peaks of the LF range of the unknown pattern are matched so as to match the peaks of the pattern of the LF range of the typical pattern as much as possible.
  • the peaks of the HF range of the unknown pattern are matched with the peaks of the pattern of the HF range of the typical pattern.
  • no matching is performed between different frequency ranges.
  • a sleep stage that becomes a typical pattern showing the highest degree of coincidence (small residual) with respect to the unknown pattern is set as the sleep stage of the unknown pattern (S436).
  • the frequency ranges of the LF range and the HF range described here are typical, including the description in the autonomic nerve component ratio method described later, and are not limited to the above-described frequency range strictly. For example, Setting the LF range to 0.05 to 0.15 Hz is also part of the disclosure of the present invention.
  • the spectral pattern method can determine the sleep stage, but cannot determine whether the sleep stage is awake or wakefulness, that is, the REM sleep or the awakening.
  • the inventor of the present application has a VLF range (Very Low Frequency: 0.01 Hz to 0.04 Hz) in addition to the above LF range and HF range for the power spectrum of the RR interval.
  • VLF range Very Low Frequency: 0.01 Hz to 0.04 Hz
  • the spectrum in the VLF range is a spectrum that appears at a lower frequency than the LF range in the power spectrum of the RR interval.
  • the time series data of the RR interval needs to have data corresponding to the corresponding period, and the reciprocal of the lower limit frequency of the necessary spectrum range needs to be the window period.
  • this window period in order to correctly obtain a power spectrum of 0.01 Hz, it is effective to set this window period to an appropriate time of about 100 seconds. When actually measuring, for example, it is set to 2 minutes or more. If the spectrum in the VLF range is not used, a shorter time, for example, 60 seconds can be used.
  • the present invention can be implemented by any pattern determination method.
  • a learning process is performed on a neural network corresponding to a result (one of sleep stages) to be determined for a spectrum pattern, and the neural network is applied to an unknown pattern.
  • a so-called supervised neural network learning system for determination can be used. Any other supervised learning technique can be implemented.
  • the sleep stage can also be determined by the autonomic nerve component ratio indicating the function of the autonomic nerve function.
  • an LF component and an HF component are obtained in the power spectrum of the RR interval.
  • the ratio between the LF component and the HF component that is, the LF / HF ratio (autonomic nerve component ratio) is calculated. This is because the function of the subject's autonomic nerve function can be evaluated by the LF / HF ratio (see Non-Patent Documents 2 to 4). In this analysis, as shown in FIG.
  • the power spectrum of the LF range is calculated to obtain the integral value of the range to obtain the LF component (S442), and the power spectrum of the HF range is calculated to integrate the range.
  • a value is obtained and set as an HF component (S444).
  • an LF / HF ratio obtained by dividing the LF component by the HF component is obtained (S446).
  • the sleep stage is determined by comparing the typical LF / HF ratio in each sleep stage with this LF / HF ratio (S448). The smaller the LF / HF ratio is, the deeper the sleep is, the deeper the sleep is, and the lighter is the sleep. However, it is not possible to determine whether the sleep is wake or wake.
  • each sleep stage of S1 to S4 and the LF / HF ratio for example, the correspondence relationship with the determination result of the sleep stage by the spectral pattern method is measured in advance. Adapting to each user is also effective.
  • the inventors of the present application have found that the data acquired at a time when the HF component does not reach a certain value has few fluctuations of high frequency in the HF component, that is, the RR interval, and an error is likely to occur in the sleep stage determination. Yes. An error hardly occurs in the determination of the sleep stage when the value of the HF component is, for example, 300 msec 2 or more. For this reason, it is possible to estimate the superiority or inferiority of the accuracy of the sleep stage determination by determining whether the HF component is larger than a predetermined value before determining the sleep stage. In particular, setting a lower limit for the value of such an HF component and using it in advance is particularly effective when it is desired to accurately determine whether or not it is in the deep sleep stage S3 or S4.
  • a sleep state monitoring system or method is realized that hardly affects the sleep state of the user, can be used at home by the user, and can determine whether it is sleep or awakening. .
  • FIG. 7 shows a configuration for performing this monitoring process.
  • the computer 50 and the pressure waveform acquisition unit 10 are collectively referred to as the monitoring unit 2.
  • the body vibration waveform is acquired by acquiring the pressure waveform acquisition unit 10, that is, the air pressure sensor 12, the transducer unit 14 that converts pressure into an electrical signal, and the A / A that converts the electrical signal into analog / digital.
  • a D converter 16 is used.
  • the air pressure sensor 12 can be disposed on the bed 100.
  • the transducer unit 14 and the A / D converter 16 are connected to output a digitized pressure wave signal.
  • the digitized pressure wave signal is input to the computer 50.
  • the computer 50 operates as a set of functional blocks that perform predetermined processing in accordance with programmed instructions. That is, the computer 50 includes a pulsation extraction unit 22, a pulsation interval calculation unit 24, and a data transmission unit 46.
  • the data transmission unit 46 can upload data to the storage unit 70 through a computer network 60 such as the Internet or an intranet.
  • Data associated with the user's ID is uploaded to the storage unit 70 a plurality of times, and the upload is performed a plurality of times by connecting the data received a plurality of times in any computer connected to the computer network 60.
  • Displayed data can be displayed in one graph.
  • a data generation unit 80 for searching and connecting data of the same user is used. For example, it can be displayed on the monitor 86.
  • Uploaded data is arbitrary time series data including beat interval data.
  • time series data of the pulsation interval may be used alone, or when a sleep stage determination unit (not shown) similar to the sleep stage determination unit 26 of FIG. In addition to data, it can be time series data of the sleep stage. If functional blocks (not shown) corresponding to the respiratory waveform extraction unit 32, the respiratory interval calculation unit 34, the awakening determination unit 36, and the sleep state determination unit 42 of FIG. In addition, time-series data including a determination result of arousal or sleep can be used.
  • the data generation unit 80 detects an obvious change that is different from the normal in the intensity of fluctuations found in the data of the beat interval, and generates an alarm signal.
  • a fluctuation detecting unit 82 for outputting can be provided.
  • the fluctuation detection unit 82 is a part of the data generation unit 80. However, if the fluctuation detection unit 82 is connected to the computer network 60 and can call up data for each user from the storage unit 70, the fluctuation detection unit 82 can be arbitrarily connected to the computer network 60. It can be provided in computer equipment.
  • the fluctuation detection unit 82 monitors a change in the pulsation interval in a certain period of time determined according to the purpose (for example, an arbitrary predetermined period such as one month when data for one year is accumulated in the storage unit 70). is doing.
  • the pulsation interval can be, for example, “SDNN” (Standard Deviation of Normal-to-Normal Intervals) data, which is the standard deviation value of the pulsation interval data, and the coefficient of variation. It is arranged so that it becomes a numerical value such as the value of and is monitored in terms of whether the value falls below a certain threshold. Then, for example, an alarm signal is transmitted when the value falls below the threshold value, and is fed back to the user by appropriate means.
  • SDNN Standard Deviation of Normal-to-Normal Intervals
  • an alarm signal is transmitted when the value falls below the threshold value, and is fed back to the user by appropriate means.
  • a method of displaying an alert on an automatically transmitted e-mail or a personalized management Web page
  • the present inventors when the health state is monitored by the value of SDNN, the present inventors have found that it is effective to combine the determination result of the sleep stage. That is, when using the pulsation interval data acquired at the time when the sleep stage of the subject is S1 or S2, the SDNN calculated therefrom may not be a good reflection of the health condition of the subject.
  • the SDNN calculated from the beat interval data acquired at the time when the stage is S3 or S4 is a good reflection of the health condition of the subject.
  • the inventor of the present application has a sleep state of shallow S1 and S2, and the sleep state of the subject is not stable.
  • the brain stage, electromyogram, electrooculogram, and electrocardiogram were collected from subjects and the sleep stage was determined by a conventional method.
  • a plate electrode was attached to each part of the subject's body. Specifically, electrodes are applied to the top of the head for electroencephalograms, to the jaws for electromyograms, to the left and right sides of the eye for electrocardiograms, and to both wrists by ankle contact for electrocardiograms. Attached.
  • a low-frequency amplifier was connected to the plate electrode to amplify the voltage signal, converted to a digital value by an AD converter, and recorded by a data logging computer.
  • the sleep stage was determined by an electroencephalogram, and the method of Rechtschaffen-Kales (RK) described above was followed.
  • the pressure waveform acquisition unit 10 and the computer 20 which are the devices described in FIG. 1 are used, and in the computer, each functional block as shown in FIG.
  • a software module for extracting a heartbeat component and a respiratory component from body vibration, and an R wave from the heartbeat waveform to extract a spectrum wave A software module for analysis and a software module for performing the autonomic nerve component ratio method were used.
  • FIG. 8 shows an example in which R-wave timing is specified from body vibration.
  • the body vibration waveform C8A-10 and the respiration waveform C8A-20 calculated therefrom are also shown.
  • a waveform C8A-2 shown in the upper part of the center line C8A-00 is obtained by detecting a candidate time of the R wave from the body vibration waveform C8A-10. This detection was performed by the pulsation extraction unit 22. At this time, among the peaks P1 to P8 of the waveform C8A-2, the peak P2 ′ is a false detection.
  • processing for removing the erroneous detection peak is performed. This process is performed when the time point when a candidate with an R wave is obtained and the time point when the next candidate is obtained are less than or equal to a preset time (for example, 0.5 seconds), the next candidate is This is processing that is not employed, such as processing that does not employ a waveform that is highly likely to be obtained regardless of pulsation as an R wave. This processing is performed to remove erroneous detection peaks from the candidates, and the time of the R wave is specified as shown below the center line C8A-00. In this figure, the phenomenon of 8 seconds is enlarged and displayed.
  • the generation time was compared between the R wave obtained from the electrocardiogram by the conventional method and the R wave specified from the body vibration data obtained by the embodiment of the present invention.
  • the detection accuracy of the R wave generation time point obtained from the body vibration waveform measured by the air pressure sensor was 87% on average compared to the R wave generation time point obtained from the electrocardiogram.
  • the correlation coefficient between the RR interval determined from the body vibration and the RR interval according to the electrocardiogram for the R-wave time-series data calculated in this way (secondarily interpolated at 0.1 second intervals) is:
  • the average correlation coefficient was 0.87 between 0.80 and 0.95.
  • the waveform C8B-10 appearing in the chart C8B in the middle of FIG. 8 is a respiratory waveform
  • the horizontal axis is a waveform of 11 minutes in time
  • the vertical axis is a pressure waveform (arbitrary unit).
  • the waveform C8B-20 in the chart C8B is the estimated RR interval.
  • the power spectrum C8C-10 shown here is a result in a deep sleep stage, and the frequency range displayed in the graph is in the range of 0.01 to 0.40 Hz, and the power spectrum has a high frequency (HF) range.
  • HF high frequency
  • Spectral pattern method Specifically, the power spectrum obtained by the method described in relation to FIG. 8 is obtained from a subject in an unknown sleep state, and the sleep pattern is determined by applying the spectral pattern method there. did. First, the observation result of the spectrum to be determined by the spectrum pattern method will be described.
  • FIG. 9A to 9E show power spectra in the case of awakening and sleep states S1 to S4.
  • FIG. 9F shows a power spectrum of REM sleep.
  • the frequency range to be used is 0.01 Hz to 0.40 Hz.
  • the power spectrum of the RR interval has irregular fluctuations in the RR interval at the time of awakening in FIG.
  • the power spectrum of the RR interval has only a small component in the LF and HF ranges, and the VLF range of 0.04 Hz or less is the main spectrum.
  • the sleep stage S1 is in a state of transition from the awakening stage to the S2 stage, and a distribution close to the characteristics at the time of awakening, that is, a distribution of small power spectrum values over LF and HF was observed. Since the power spectrum of the awake state may be mixed into the sleep stage S1, it is difficult to distinguish the awake state from S1 only from the actual power spectrum. On the other hand, in the sleep stage S2 shown in FIG. 9C, the spectrum of the HF range appears more clearly than the spectrum of S1 although the spectrum of the LF range exists. Easier than state identification.
  • FIG. 9 is a power spectrum pattern of REM sleep.
  • REM sleep that appears when sleep approaches wakefulness, the pattern is similar to wakefulness and S1, but the spectrum of the LF range is larger and more complex than in S1.
  • REM sleep and awakening can be distinguished by obtaining a coefficient of variation (CV) from time series data of RR intervals.
  • CV of respiration was also considered in the estimation of arousal. This method was effective in determining sleep awakening, mid-wake awakening, and awakening after the end of sleep. The characteristics when the observer directly observes the power spectrum at the RR interval have been described.
  • FIG. 10 shows body vibration waveform C10A-10 and respiratory waveform C10A-20 (top chart C10A) obtained from the air pressure sensor, and detected R wave time series C10B-10 (second chart C10B from the top). Respiration rate C10C-10 and heart rate C10C-20 (third chart C10C from the top), RR interval power spectrum C10D-10 (bottom left chart C10D), and sleep stage determination example C10E-10 and The LF / HF ratio is C10E-20 (the bottom right chart C10E).
  • FIG. 11 compares the determination result of the sleep state, the broken line R0 is a result determined by a conventional method using an electroencephalogram, and the broken line R1 (broken line) is a result of automatic determination by a spectrum pattern method.
  • the line R1 of the automatic determination result is the same as that shown in the chart C10E of FIG. 10, and the vertical axis represents awakening, REM sleep, and S1 to S4 in order from 1 to ⁇ 4. It was obtained from the measurement data of the minute body vibration. The degree of agreement between the conventional method and the method of the example was about 75%.
  • the autonomic component ratio method was used to distinguish between the shallow sleep stages (S1 and S2) and the deep sleep stages (S3 and S4) from the body vibration waveform acquired under the above conditions. At this time, shallow sleep and deep sleep were judged as shallow sleep when the LF / HF ratio was greater than 0.3, and deep sleep was judged when 0.3 or less. In the range of the acquired data, the degree of coincidence between the determination result and the determination result of the sleep stage by the electroencephalogram was 80% or more.
  • the above-mentioned determination standard is an example.
  • the value for each user used for the determination can be separately recorded in a referable database.
  • the HF component value itself reaches a predetermined value as described above, that is, when the sleep stage determination accuracy reaches a certain level, control is performed so that the sleep stage is determined. It is also possible to do.
  • the HF component does not reach a predetermined value for example, 300 msec 2 , that is, when the sleep stage determination accuracy does not reach a certain level, the result of the sleep stage determination is obtained.
  • a flag indicating that the accuracy is not good when outputting, it is possible to attach a flag indicating that the accuracy is not good.
  • the monitoring unit 2 performs an RR interval calculation process for R wave data.
  • the data of the RR interval is uploaded once a day to a data server that manages the storage unit 70.
  • any data communication means can be used for uploading, for example, it can be performed by putting using FTP (File Transfer Protocol).
  • the form of data storage in the storage unit 70 is arbitrary. For example, a computer file in CSV (Comma Separated Values) format for each user ID and each date using a file system folder (directory) of the data server. Multiple data can be stored. As another example, data can be stored in an appropriate database using a database management system or the like.
  • an optical sensor (not shown) is connected to the computer 50, and the timing of measurement start can be controlled by ambient illuminance information. Furthermore, in order to limit the measurement time to two sleep cycles after falling asleep, only data for 3 hours after the start of measurement can be acquired. In addition, in order to be able to collect beat interval data only when sleeping at a certain depth or more, and to perform data processing using the determination result of the sleep stage later, the RR interval at each time The determination result of the sleep stage at each time can be acquired together with the data. Thereby, for example, using the pulsation data when the sleep stages are S3 and S4, it is possible to acquire SDNN data that favorably reflects the health condition of the subject.
  • the fluctuation of the RR interval is calculated from the data of the RR interval included in the CSV file. This fluctuation can be calculated by calculating a standard deviation from the data of the RR interval acquired during a certain period (for example) and calculating it as SDNN, or by calculating a coefficient of variation (CV).
  • the data of each day divided into a plurality of CSV files are concatenated and calculated, and the fluctuation of the RR interval can be displayed following the date.
  • the fluctuation detection unit 82 includes, for example, a short-term fluctuation monitoring module (not shown) that monitors fluctuations in the RR interval during the past month, and a long-term fluctuation that monitors fluctuations in the RR interval over the past year.
  • a monitoring module (not shown) can be implemented.
  • the short-term fluctuation monitoring module and the long-term fluctuation monitoring module monitor how the fluctuation appearing in the fluctuation of the RR interval changes according to each period.
  • RR interval data acquired in the time in a sleep state suitable for monitoring can be extracted from the sleep time.
  • the sleep state monitoring system 6 shown in FIG. 7 includes the sleep stage determination unit 26, the respiratory waveform extraction unit 32, the respiratory interval calculation unit 34, the awakening determination unit 36, and the sleep state determination shown in FIG.
  • the operations of the short-term fluctuation monitoring module and the long-term fluctuation monitoring module can be controlled by outputs from these functional blocks.
  • SDNN was measured with a pulsation monitoring device prepared in this example for one subject with a high blood glucose level. The measurement is performed twice on the day corresponding to the first day when the subject starts exercise therapy and the day corresponding to the 105th day after starting exercise therapy, and the subject takes about 100 minutes of sleep during each day. RR interval data was acquired during the sleep. Then, RR interval data was accumulated for 60 seconds, a standard deviation was calculated from the RR interval data for 60 seconds, and SDNN was obtained every 60 seconds. At this time, in order to determine the sleep stage in accordance with the SDNN data, sleep stage data was also acquired.
  • the sleep stage was determined by the above-described spectrum pattern method using the power spectrum of the RR interval obtained by the window period of 60 seconds in order to meet the above conditions. Since the window period is 60 seconds, the power spectrum in the VLF range is not used.
  • the said test subject continued for 3 days or more as a exercise therapy between the 1st day and the 105th day, and the walking where the walk distance of each week becomes about 40 km.
  • FIGS. 12A and 12B are both SDNN data on the first day and the 105th day, the horizontal axis is the elapsed time after sleep (unit: minutes), and the vertical axis is SDNN (unit: milliseconds). ).
  • FIG. 12A is a graph in which SDNN measured every minute is plotted as it is, and FIG. 12B is obtained at the time when the subject is in the sleep stage S3 or S4 among the measured SDNN. It is the graph which plotted data. Looking at the trend from FIG. 12A alone, it can be said that the SDNN on the first day has a smaller value than the SDNN on the 105th day. On the other hand, for example, there is no significant difference until about 30 minutes after falling asleep. In addition, this relationship is reversed after 90 minutes after falling asleep.
  • FIG. 12 (B) in which only the cases where the sleep stage is S3 and S4 are plotted, the data greatly deviated in each data series is excluded from the data of any day, Also, data immediately after falling asleep and in the latter half are also excluded.
  • the SDNN plotted in FIG. 12B is distributed over 20 to 40 msec for the first day, whereas it is distributed over about 40 msec for the 105th day.
  • the deep time zone of sleep on day 1 and day 105 is relatively early at 25 to 80 minutes after falling asleep on the first day.
  • the 105th day is also supported by the lateness of 40-100 minutes after falling asleep. That is, the above experimental result means that it is difficult to extract the SDNN of only deep sleep depending on the elapsed time after falling asleep without determining the sleep stage based on the measurement result.
  • heart rate variability was extracted from the air pressure sensor, the R wave was detected, and the R wave could be specified with high accuracy in comparison with the electrocardiogram.
  • sleep and wakefulness could be appropriately distinguished by the coefficient of variation (CV) of the respiratory peak and the coefficient of variation of the R wave.
  • the power spectrum of the RR interval was calculated from the specified R wave, and it was shown that the sleep stage can be accurately determined by the spectrum pattern method and the autonomic component ratio method.
  • a suitable artificial intelligence system is used to learn the relationship between the power spectrum pattern of the RR interval and the determination result made by the conventional method at the same time, and the sleep stage determination result is obtained from the power spectrum. Obtainable.
  • a determination adapted to each subject can be performed using a file that determines a determination criterion for each subject.
  • the system shown in FIG. 7 can monitor and transmit changes in the interval between pulsations to the user and medical staff.
  • the user can take measures such as receiving an examination at a medical institution based on the information.

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Abstract

La présente invention concerne la mise en œuvre de soins de santé grâce à l’utilisation d’un appareil de surveillance des conditions de sommeil. Ledit appareil inclut les éléments suivants : une partie d’obtention de forme d’onde de pression (10) destinée à obtenir une forme d’onde de pression à partir d’une partie de détection de pression en contact avec un corps d’utilisateur ; une partie d’extraction de pulsation (22) destinée à extraire une onde de forme de pulsation en réalisant un traitement prédéterminé sur la forme d’onde de pression ; une partie de calcul d’intervalle de pulsation (24) destinée à calculer un intervalle de pulsation ; et une partie de détermination d’un niveau de sommeil (26) destinée à déterminer un niveau de sommeil – c’est-à-dire un niveau de profondeur de sommeil de l’utilisateur à partir de l’intervalle de pulsation – et à recueillir des données sur l’intervalle de pulsation durant le sommeil, par le biais d’un réseau informatique, en vue de mesurer les conditions de sommeil à domicile.
PCT/JP2008/072697 2008-06-13 2008-12-12 Appareil de surveillance des conditions de sommeil, système de surveillance et programme informatique WO2009150765A1 (fr)

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JP5874489B2 (ja) * 2012-03-27 2016-03-02 富士通株式会社 睡眠状態判定装置及び睡眠状態判定方法
JP6622455B2 (ja) * 2014-11-19 2019-12-18 シャープ株式会社 睡眠状態判定システム
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JP6439729B2 (ja) 2016-03-24 2018-12-19 トヨタ自動車株式会社 睡眠状態推定装置
JP6875981B2 (ja) * 2016-11-22 2021-05-26 パラマウントベッド株式会社 端末装置、出力方法及びコンピュータプログラム
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