WO2016093347A1 - Dispositif et programme informatique pour analyser un état biologique - Google Patents

Dispositif et programme informatique pour analyser un état biologique Download PDF

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WO2016093347A1
WO2016093347A1 PCT/JP2015/084810 JP2015084810W WO2016093347A1 WO 2016093347 A1 WO2016093347 A1 WO 2016093347A1 JP 2015084810 W JP2015084810 W JP 2015084810W WO 2016093347 A1 WO2016093347 A1 WO 2016093347A1
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frequency
series waveform
state
time series
time
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PCT/JP2015/084810
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English (en)
Japanese (ja)
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藤田　悦則
小倉　由美
良香 延廣
可南子 中島
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株式会社デルタツーリング
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Priority to JP2016563749A priority Critical patent/JP6588035B2/ja
Publication of WO2016093347A1 publication Critical patent/WO2016093347A1/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
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators

Definitions

  • the present invention relates to a biological state analyzing apparatus and a computer program for analyzing a human state from biological signals, and in particular, a biological body capable of estimating the possibility of sleep-related diseases, in particular, the possibility of sleep apnea syndrome.
  • the present invention relates to a state analysis apparatus and a computer program.
  • Patent Document 1 collects snoring sound associated with breathing, cuts out a waveform in a time window corresponding to one cycle of human breathing, and correlates the waveform of snoring sound between each cycle.
  • a technique is disclosed in which a function is obtained, whether or not a snoring sound is included is determined to be non-stationary, and sleep apnea syndrome is determined when an unsteady sound is included.
  • Patent Document 2 a sensor sheet having a plurality of pressure-sensitive elements is attached to a bedding, a respiratory signal is extracted from a load signal that captures a load change applied to the subject's bedding, and respiratory disorders such as sleep apnea syndrome are started.
  • a technique for detecting the above is disclosed.
  • Patent document 1 collects snoring sound and determines whether or not it is sleep apnea syndrome. Compared with attaching a plurality of sensors to the body surface before that, it is simpler and shorter in time. A snoring sound can be identified. Patent Document 2 obtains information using a load sensor that captures a change in the weight of a subject on bedding.
  • Patent Document 1 describes that it is suitable for screening for the purpose of simple determination in a health checkup, etc. by roughly determining disease symptoms and leaving more accurate determination to the determination by a doctor.
  • a predetermined examination time is required, and a method for easily and promptly determining a sleep-related disease risk such as sleep apnea syndrome has not been established.
  • the present invention has been made in view of the above, and can screen a sleep-related disease risk while performing daily activities without laying the subject, and in particular, collects back body surface pulse waves from the upper body in a non-contact manner.
  • An object of the present invention is to provide a biological state analyzer and a computer program capable of estimating sleep-related disease risks including sleep apnea syndrome.
  • the present inventor has conducted intensive research, and as a result, the biological condition analysis method proposed by the present applicant, i.e., the biological signal measurement device provided in the seat back portion of the driver's seat of the vehicle By collecting the driver's back body surface pulse wave by contact and capturing the biological state determined from the time-series waveform of this back body surface wave wave and the circadian rhythm of sleep, the sleep-related disease risk In particular, the inventors have found that the risk of sleep apnea syndrome can be determined, and have completed the present invention.
  • the biological signal analyzer is a biological state analyzer that analyzes a biological signal collected by the biological signal measuring device and determines a biological state, and includes a predetermined condition including fatigue, reduced attention, or drowsiness.
  • Arousal level reduction state determination means for determining the appearance timing of the low arousal level state, and the number of appearances per unit time of the low arousal level state determined by the awakening level reduction state determination means, It is characterized by having a disease risk estimation means for estimating a risk of a disease related to sleep in relation to the number of appearances and the circadian rhythm of sleepiness.
  • the disease risk estimation means when the number of appearances per unit time of the state of reduced wakefulness is a predetermined number of times or more in a time zone in which the wakefulness level is higher than a predetermined level in the circadian rhythm of sleepiness, sleep apnea It is preferable to include means for estimating that the risk of the syndrome is high.
  • the time zone in which the arousal level in the circadian rhythm of drowsiness is higher than a predetermined value is preferably a time zone including a switching point where the drowsiness is lowered and the drowsiness is increased.
  • the arousal level lowering state determining means obtains a time series waveform of a frequency using a zero cross point or a peak point in the time series waveform of the biological signal, and slides the obtained time series waveform of the obtained frequency to calculate a slope of the frequency. It is preferable to have frequency gradient time-series waveform calculating means for obtaining a time-series waveform, and to determine the appearance timing of the state of reduced arousal level based on the frequency gradient time-series waveform obtained from the frequency gradient time-series waveform calculating means. .
  • the arousal level lowering state determining unit is configured to determine the arousal level when the frequency convergence time series waveform obtained from the frequency gradient time series waveform calculation unit has a continuous amplitude convergence and expansion tendency with respect to a predetermined reference. It is preferable to determine the appearance timing of the lowered state.
  • the arousal level lowering state determining means obtains a time series waveform of a frequency using a zero cross point or a peak point in the time series waveform of the biological signal, and slides the obtained time series waveform of the obtained frequency to calculate a slope of the frequency.
  • Frequency gradient time series waveform calculating means for obtaining a time series waveform, and a function adjustment signal having a frequency lower than the frequency at which the fluctuation characteristics of the cardiovascular system are switched from the frequency gradient time series waveform obtained by the frequency gradient time series waveform calculating means
  • the frequency component belonging to the VLF band is extracted from the ULF band corresponding to the fatigue acceptance signal having a frequency higher than that of the function adjustment signal and the activity adjustment signal having a frequency higher than that of the fatigue acceptance signal, and each of these frequency components is extracted.
  • Distribution rate calculation means for obtaining the distribution rate in time series, wherein the function adjustment signal, fatigue If the variation of the distribution ratio of the signal and activities adjustment signal falls below a predetermined criterion, it is preferable to determine the appearance timing of the awareness decrease condition to cause a disturbance of the biological rhythm.
  • the biological signal collected by the biological signal measuring device is preferably a back body surface pulse wave.
  • a computer program according to the present invention is a computer program that causes a computer as a biological state analyzer to analyze a biological signal collected by a biological signal measuring device and execute a procedure for determining a biological state.
  • the number of appearances per unit time of the arousal level reduced state determination procedure for determining the appearance timing of a predetermined low arousal level state including reduction or drowsiness, and the arousal level reduced state determined by the arousal level reduced state determination procedure
  • a disease risk estimation procedure for estimating a risk of a disease related to sleep based on a relationship between the number of appearances per unit time and a circadian rhythm of sleepiness is performed.
  • the disease risk estimation procedure includes sleep apnea when the number of appearances per unit time of the state of reduced wakefulness is a predetermined number of times or more in a time zone in which the degree of wakefulness is higher than a predetermined value in the circadian rhythm of sleepiness It is preferred to perform a procedure that estimates that the risk of the syndrome is high.
  • the time zone in which the arousal level in the circadian rhythm of drowsiness is higher than a predetermined value is preferably a time zone including a switching point where the drowsiness is lowered and the drowsiness is increased.
  • the wakefulness-decreasing state determination procedure obtains a time-series waveform of a frequency using a zero-cross point or a peak point in the time-series waveform of the biological signal, and slides the obtained time-series waveform of the obtained frequency to calculate the slope of the frequency.
  • a frequency gradient time-series waveform calculation procedure for obtaining a time-series waveform is executed, and the appearance timing of the state of reduced arousal level is determined based on the frequency gradient time-series waveform obtained by executing the frequency gradient time-series waveform calculation procedure. Is preferred.
  • the arousal level lowering state determination procedure is performed when the frequency inclination time series waveform obtained by executing the frequency inclination time series waveform calculation procedure has an amplitude convergence tendency and an expansion tendency with respect to a predetermined reference. It is preferable to determine the appearance timing of the state of reduced arousal level.
  • the wakefulness-decreasing state determination procedure obtains a time-series waveform of a frequency using a zero-cross point or a peak point in the time-series waveform of the biological signal, and slides the obtained time-series waveform of the obtained frequency to calculate the slope of the frequency.
  • Frequency slope time series waveform calculation procedure for obtaining a time series waveform, and a function of a frequency lower than the frequency at which the fluctuation characteristics of the cardiovascular system are switched from the frequency slope time series waveform obtained by executing the frequency slope time series waveform calculation procedure.
  • Each frequency component belonging to the VLF band is extracted from the ULF band corresponding to the adjustment signal, the fatigue acceptance signal having a frequency higher than that of the function adjustment signal, and the activity adjustment signal having a frequency higher than that of the fatigue acceptance signal.
  • the distribution rate calculation procedure for obtaining each distribution rate in time series is executed, and the distribution rate calculation procedure is used to obtain the distribution rate calculation procedure.
  • Ability adjustment signal if there is a change in the distribution ratio of the fatigue-receiving signal and the activity adjusted signal falls below a predetermined criterion, it is preferable to determine the appearance timing of the awareness decrease condition to cause a disturbance of the biological rhythm.
  • the present invention it is possible to determine a sleep-related disease risk, particularly a sleep apnea syndrome risk.
  • a sleep-related disease risk particularly a sleep apnea syndrome risk.
  • the present invention can be determined using the back body surface pulse wave collected without contact, it can be estimated whether the driver may have a risk of sleep apnea syndrome, and safe driving It can contribute to improvement.
  • FIG. 1 is a perspective view showing an example of a back body surface pulse wave measuring apparatus which is a biological signal measuring apparatus for measuring a back body surface pulse wave used in an embodiment of the present invention.
  • FIG. 2 is a diagram schematically showing the configuration of the biological state analyzer according to the embodiment of the present invention.
  • FIG. 3 is a diagram illustrating a typical example of a waveform (waveform of frequent accident determination) in which convergence and expansion continue in a frequency gradient time-series waveform.
  • FIG. 4 is a diagram showing a typical example of a characteristic waveform (jet lag determination waveform) seen in the time-series waveform of the distribution rate when the biological rhythm is disturbed.
  • FIG. 1 is a perspective view showing an example of a back body surface pulse wave measuring apparatus which is a biological signal measuring apparatus for measuring a back body surface pulse wave used in an embodiment of the present invention.
  • FIG. 2 is a diagram schematically showing the configuration of the biological state analyzer according to the embodiment of the present invention.
  • FIG. 3 is
  • FIG. 5 shows the number of occurrences by time of a waveform (waveform of frequent accident determination) in which convergence and expansion continue in a frequency gradient time-series waveform obtained by analyzing all data of drivers 1 to 4 in the experimental example. It is the figure which showed the relationship of the circadian rhythm of sleepiness.
  • FIG. 6 (a) is a diagram showing a representative example of the frequency gradient time series waveform of the driver 4 (SAS patient) at the 10 o'clock range
  • FIG. 6 (b) is a diagram of 10 of the driver 3 (healthy person). It is the figure which showed the typical example of the time-sequential frequency inclination time series waveform.
  • FIG. 6 (a) is a diagram showing a representative example of the frequency gradient time series waveform of the driver 4 (SAS patient) at the 10 o'clock range
  • FIG. 6 (b) is a diagram of 10 of the driver 3 (healthy person). It is the figure which showed the typical example of the time-sequential frequency inclination time series
  • FIG. 7 (a) is a diagram showing a representative example of a time-series waveform of the distribution rate of the 10 o'clock range for driver 4 (SAS patient), and FIG. 7 (b) is a diagram of driver 3 (healthy person). It is the figure which showed the representative example of the time-sequential waveform of the distribution rate of 10:00.
  • FIG. 8 shows a waveform (jet lag determination waveform) that is determined to be a disturbance of the biological rhythm in the time-series waveform of the distribution rate obtained by analyzing all the data of the drivers 1 to 4 in the experimental example. It is the figure which showed the relationship between the appearance frequency according to time slot
  • FIG. 9 is a diagram for explaining a procedure for conversion to a determination frequency fluctuation waveform.
  • FIG. 10 is a diagram in which the accident occurrence determination frequency waveform of FIG. 5 is converted into a determination frequency fluctuation waveform by the procedure of FIG.
  • FIG. 11 is a diagram obtained by converting the jet lag determination frequency waveform of FIG. 8 into a determination frequency fluctuation waveform by the procedure of FIG.
  • FIG. 12 is a diagram showing the average number of determinations per day obtained from all the data of drivers 1 to 4 in the experimental example
  • FIG. 12A is a diagram regarding the number of frequent accident determinations
  • FIG. These are figures regarding the number of jet lag determinations.
  • FIG. 10 is a diagram in which the accident occurrence determination frequency waveform of FIG. 5 is converted into a determination frequency fluctuation waveform by the procedure of FIG.
  • FIG. 11 is a diagram obtained by converting the jet lag determination frequency waveform of FIG. 8 into a determination frequency fluctuation waveform by the procedure of FIG.
  • FIG. 13 is a diagram showing the average number of determinations per hour obtained from all the data of the drivers 1 to 4 in the experimental example.
  • FIG. 13A is a diagram regarding the number of frequent accident determinations
  • FIG. ) Is a diagram relating to the number of jet lag determinations.
  • FIG. 14 is a diagram showing the average number of judgments per day according to the number of consecutive working days from the end of the holidays of the drivers 1 to 4 in the experimental example
  • FIG. FIG. 14B is a diagram relating to the number of jet lag determinations.
  • FIG. 15 is a diagram showing the average number of determinations per hour for each consecutive working day from the end of the breaks of drivers 1 to 4 in the experimental example, and FIG. 15 (a) shows the number of frequent accident determinations.
  • FIG. 15B is a diagram relating to the number of jet lag determinations.
  • FIG. 16 is a diagram illustrating the number of times of determination for each operation related to frequent accident determination of drivers 1 to 4 in the experimental example.
  • FIG. 17 is a diagram showing the number of determinations for each operation related to the jet lag determination of the drivers 1 to 4 in the experimental example.
  • Examples of the biological signal collected in the present invention include fingertip volume pulse wave, back body surface pulse wave (APW), and the like, and preferably back body surface pulse wave (APW).
  • the back body surface pulse wave (APW) is sound / vibration information generated from the motion of the heart and aorta detected from the upper back of a person.
  • Information on ventricular systole and diastole, blood circulation auxiliary pump, The blood vessel wall elasticity information and the blood pressure elasticity information are included.
  • the signal waveform associated with heart rate variability includes sympathetic and parasympathetic nervous system activity information (parasympathetic activity information including the compensation of sympathetic nerves), and the signal waveform associated with aortic oscillation is sympathetic. Contains information on neural activity.
  • a biological signal measuring device for collecting a biological signal can use a fingertip plethysmograph if it is a fingertip plethysmogram, and if it is a back body surface pulse wave (APW), for example, a pressure sensor
  • AW back body surface pulse wave
  • a waveguide type sensor used in a drowsy driving warning device (Sleep Buster (registered trademark)) manufactured by Delta Touring Co., Ltd. can be preferably used.
  • FIG. 1 shows a schematic configuration of a back body surface pulse wave measuring apparatus 1 comprising this waveguide type sensor.
  • the back body surface pulse wave measuring device 1 includes a core pad 11 made of a plate-like bead foam, and a tertiary placed in two through-holes 11a formed in the core pad 11 with a portion corresponding to the spine interposed therebetween.
  • the original three-dimensional knitted fabric 12, a sensor 13 including a microphone sensor attached to the three-dimensional three-dimensional knitted fabric 12, and films 14 and 15 disposed on both sides of the three-dimensional three-dimensional knitted fabric 12 are configured.
  • plate-like foams 16 and 17 made of bead foam are laminated on the front and back surfaces of the core pad 11.
  • the back body surface pulse wave measuring device 1 is used, for example, attached to a seat back of a vehicle seat or attached in the vicinity corresponding to the back of a bed.
  • the back body surface pulse wave measuring device 1 When the back body surface pulse wave measuring device 1 comes into contact with the back of a person, sound and vibration through the body surface due to a biological signal cause membrane vibration in the core pad 11 and the films 14 and 15 via one plate-like foam 16. Then, string vibration is generated in the connecting yarn of the three-dimensional solid knitted fabric 12, and film vibration is generated in the other plate-like foam 17 to be propagated.
  • the back body surface pulse wave measuring device 1 has a function of substantially amplifying a weak biological signal by such membrane vibration and string vibration, and the biological signal is reliably detected by the sensor 13.
  • the biological state analysis apparatus 100 is configured to include a wakefulness reduction state determination unit 110, a disease risk estimation unit 120, and the like, and detection of sound / vibration information obtained from the sensor 13 of the back body surface pulse wave measurement device 1 by them.
  • the back body surface wave (APW) included in the signal is analyzed.
  • the biological state analysis apparatus 100 includes a computer (including a microcomputer), and causes a storage unit of the computer to execute a wakefulness reduction state determination procedure that functions as the wakefulness reduction state determination means 110, thereby causing disease risk estimation means.
  • a computer program for executing a disease risk estimation procedure functioning as 120 is set. Note that the computer program may be stored in a recording medium.
  • the program can be installed in the computer, for example.
  • the recording medium storing the program may be a non-transitory recording medium.
  • the non-transitory recording medium is not particularly limited, and examples thereof include a recording medium such as a flexible disk, a hard disk, a CD-ROM, an MO (magneto-optical disk), a DVD-ROM, and a memory card. It is also possible to install the program by transmitting it to the computer through a communication line.
  • the arousal level lowering state determination means 110 uses an index obtained by analyzing the back body surface pulse wave to detect a state of reduced arousal level of a living body that appears as a result of a decrease in arousal level including fatigue, reduced attention, or drowsiness. Detect the appearance timing.
  • the arousal level decreased state determining means 110 obtains a time-series waveform of the frequency using the zero cross point or peak point in the time-series waveform of the back body surface pulse wave, and obtained.
  • a frequency gradient time series waveform calculating unit 111 is provided that calculates a frequency gradient time series waveform (frequency gradient time series waveform) by sliding calculation of the frequency time series waveform.
  • the frequency gradient time-series waveform calculating means 111 converts the time-series waveform of the back body surface pulse wave (APW) into a frequency time-series waveform, and further slide-calculates the obtained time-series waveform to obtain a frequency gradient. Find the series waveform.
  • the back body surface pulse wave to be calculated is subjected to predetermined processing in a preprocessing unit that receives a detection signal of sound / vibration information obtained from the sensor 13 of the back body surface pulse wave measuring device 1. , Extracted from the detection signal.
  • the back body surface pulse wave is obtained by filtering the detection signal of the sensor 13 with a bandwidth of about 10 to 30 Hz, rectifying the filtered waveform, and enveloping the envelope waveform from the rectified waveform.
  • a time-series waveform in the vicinity of 1 Hz is extracted by applying a filtering process in a low frequency band of 5 Hz or less (for example, a bandwidth of around 1 to 2 Hz) to the formed and envelope waveform.
  • the time series waveform of the back body surface pulse wave is disclosed.
  • a method using a point (zero cross point) switching from positive to negative (zero cross point) and a time series waveform using a local maximum value (peak point) by smoothing and differentiating the time series waveform of the back body surface pulse wave (APW) There are two methods of obtaining a waveform (peak detection method).
  • the zero cross point when the zero cross point is obtained, it is divided every 5 seconds, for example, and the reciprocal of the time interval between the zero cross points of the time series waveform included in the 5 second is obtained as the individual frequency f.
  • the average value of the individual frequency f is adopted as the value of the frequency F for 5 seconds. Then, by plotting the frequency F obtained every 5 seconds in time series, a time series waveform of frequency fluctuation is obtained.
  • the maximum value is obtained from the time series waveform of the back body surface pulse wave (APW) by, for example, the smoothing differential method using Savitzky and Golay.
  • AW back body surface pulse wave
  • the local maximum value is divided every 5 seconds, the reciprocal of the time interval between the local maximum values of the time-series waveform included in the 5 seconds is obtained as the individual frequency f, and the average value of the individual frequency f in the 5 seconds is calculated This is adopted as the value of the frequency F for 5 seconds.
  • a time series waveform of frequency fluctuation is obtained.
  • the frequency gradient time series waveform calculating means 111 has a predetermined overlap time (for example, 18 seconds) and a predetermined time width (for example, 180 seconds) from the time series waveform of the frequency fluctuation obtained by the zero cross method or the peak detection method.
  • a time window is set, a frequency gradient is obtained for each time window by the method of least squares, and a time series waveform of the gradient is output. This slide calculation is sequentially repeated to output the APW frequency gradient time-series change as a frequency gradient time-series waveform.
  • the dorsal body surface wave is a biological signal mainly including the state of control of the heart, which is the central system, that is, the state of sympathetic innervation of the artery, and the appearance information of the sympathetic nervous system and the parasympathetic nervous system.
  • the frequency slope time series waveform obtained by the zero cross method is related to the state of control of the heart and reflects the appearance state of the sympathetic nerve, but the frequency slope time series waveform obtained by the peak detection method is More related to heart rate variability. Therefore, in order to detect a biological phenomenon related to a state of reduced arousal level, it is preferable to use a frequency gradient time series waveform obtained by using the zero cross method.
  • the arousal level lowering state determination means 110 is a state of reduced arousal level when the frequency convergence time series waveform obtained from the frequency slope time series waveform calculation means 111 has an amplitude convergence tendency and an expansion tendency continuous with respect to a predetermined reference. It is preferable to set to determine that it is the appearance timing.
  • the state of reduced arousal level here means not only the timing at which the arousal level is significantly reduced and the appearance of the urgent sleep phenomenon or the onset of sleep phenomenon, but also the fatigue that is presumed to be the state before the onset of the urgent sleep or onset of sleep phenomenon The case where it is determined as the rising period of the degree or the continuation period of the same biological state is included.
  • the frequency gradient time-series waveform using the zero-cross detection method at the timing determined to be a state of reduced wakefulness shows a tendency that the amplitude converges and becomes a short period, and thereafter, the amplitude tends to increase in a longer period. It is characteristic that the waveform components shown are seen.
  • the amplitude increases in the time-series waveform of the frequency gradient, and if the amplitude converges after that, it indicates that the sympathetic nerve activity has decreased, and the amplitude further increases
  • the most converged time point can be determined as an imminent sleep phenomenon (see Japanese Patent Application Laid-Open No. 2014-117425 by the present applicant).
  • the waveform is as shown in FIG.
  • such a waveform component in which the amplitude tends to converge and expand is not as clear as the sleep onset phenomenon or the impending sleep phenomenon, but may appear even in a favorable state or a conscious state.
  • the convergence and expansion of the amplitude value depends on whether the ratio value indicating the relationship with the amplitude of the previous predetermined time zone is within a predetermined range in the amplitude of the determination time zone, for example. It can be determined as a tendency, and conversely, it can be determined as an expansion tendency depending on whether or not the amplitude of the determination time zone is a predetermined multiple or more in relation to the amplitude of the previous predetermined time zone.
  • the distribution rate calculation unit 112 is used to determine the appearance level of the arousal level reduction state. can do.
  • the frequency gradient time series waveform calculation means 111 and the distribution rate calculation means 112 can be used in combination.
  • the determination of the state of reduced arousal level by both means is basically the same, but if both are used in combination and it is determined that the appearance level of the reduced state of arousal level is detected in either one, the reduced level of arousal level in the other side Regardless of whether or not it is determined as the appearance timing, the configuration in which it is determined that the state of arousal level is reduced can suppress omission of determination.
  • the distribution ratio calculation means 112 has been proposed by the present applicant in Japanese Patent Application Laid-Open Nos. 2011-167362 and 2012-179202, and is based on the following knowledge. That is, human constancy is maintained with fluctuations, and the frequency bands are in the ULF band and the VLF band. On the other hand, in atrial fibrillation, which is one of heart diseases, the frequency at which the characteristics of fluctuations in the cardiovascular system are switched is said to be 0.0033 Hz. By detecting fluctuations in the vicinity of 0.0033 Hz, homeostasis is obtained. Information on maintenance can be obtained.
  • the frequency bands below 0.0033 Hz and below 0.0053 Hz are mainly related to body temperature regulation, and the frequency band from 0.01 to 0.04 Hz is said to be related to autonomic nerve control. Yes. Then, a frequency-gradient time series waveform for calculating these low-frequency fluctuations inherent in the biological signal is actually obtained and subjected to frequency analysis. As a result, the frequencies near 0.0017 Hz and 0.0033 Hz are lower than 0.0033 Hz. It was confirmed that there were fluctuations in the frequency band centered on 0.0035 Hz and, in addition to these two, fluctuations in the frequency band centered on 0.0053 Hz.
  • the distribution rate calculating means 112 first analyzes the frequency inclination time series waveforms obtained from the frequency inclination time series waveform calculating means 111, respectively, and from the above 0.0033 Hz, which is the frequency at which the fluctuation characteristics of the cardiovascular system are switched. Each frequency component belonging to the VLF band is extracted from the ULF band corresponding to the lower frequency function adjustment signal, the fatigue acceptance signal having a higher frequency than the function adjustment signal, and the activity adjustment signal having a higher frequency than the fatigue acceptance signal. Next, the distribution ratios of these frequency components are obtained in time series. That is, the ratio of each frequency component when the sum of the power spectrum values of the three frequency components is 1 is obtained as a distribution rate in time series.
  • a frequency component of 0.0017 Hz is used as the function adjustment signal
  • a frequency component of 0.0035 Hz is used as the fatigue acceptance signal
  • a frequency component of 0.0053 Hz is used as the activity adjustment signal.
  • the frequency components of each signal can be adjusted according to individual differences, and the function adjustment signal is preferably within a range of less than 0.0033 Hz. In the range of 0.001 to 0.0027 Hz, the fatigue acceptance signal can be adjusted in the range of 0.002 to 0.0052 Hz, and the activity adjustment signal can be adjusted in the range of 0.004 to 0.007 Hz.
  • the arousal level lowering state determination means 110 is awakening caused by disturbance of the biological rhythm when fluctuations in the distribution ratios of the function adjustment signal, fatigue acceptance signal and activity adjustment signal obtained by the distribution ratio calculation means 112 are below a predetermined standard. It is determined as the appearance timing of the low degree state. If the biological rhythm is normal, the time series waveforms of the distribution ratios of the function adjustment signal, the fatigue acceptance signal, and the activity adjustment signal fluctuate with a predetermined fluctuation, but the present applicant has proposed in Japanese Patent Application No. 2013-253713. Thus, when the biological rhythm is disturbed, the fluctuation of the distribution rate becomes a long period and the change tends to be small. This tendency is particularly prominent in long-cycle frequency components (function adjustment signals).
  • the arousal level lowering state determination unit 110 specifically, the function adjustment signal (0.0017 Hz) as in the time series waveform of the distribution rate shown in FIG. If it is determined that a time period in which the distribution rate of the signal is higher than the distribution rate of the fatigue acceptance signal (0.0035 Hz) and the activity adjustment signal (0.0053 Hz) continues for a predetermined time or more, the disturbance of the biological rhythm is a factor. It is preferable to determine the appearance timing of the state of reduced arousal level.
  • the arousal level lowering state determination means 110 is a time zone in which the distribution ratio of the function adjustment signal is high, and a time slot in which the order of the distribution ratio of the function adjustment signal, the fatigue acceptance signal, and the activity adjustment signal is the same is a predetermined time or more.
  • a time slot in which the order of the distribution ratio of the function adjustment signal, the fatigue acceptance signal, and the activity adjustment signal is the same is a predetermined time or more.
  • the disease risk estimation means 120 first obtains the number of appearances per unit time of the arousal level reduction state determined by the arousal level reduction state determination means 110, and then calculates the number of appearances of the arousal level reduction state per unit time. Compared with the circadian rhythm of sleepiness, it is a means for estimating the risk of sleep-related diseases. As the circadian rhythm of drowsiness, a waveform indicated by a dotted line in FIG. 5 is known, and the circadian rhythm data is stored in the storage unit of the computer constituting the biological state analyzer 1. The circadian rhythm varies from person to person, but it may include a switching point where the sleepiness tends to increase from the tendency to decrease drowsiness during the time period of 1 to 2 hours around 10:00.
  • this switching point is the point where sleepiness is the lowest and the arousal level is high, and in the time zone near the switching point, the appearance of a low arousal level generally occurs compared to other time zones. It is considered difficult to do. In other words, if the appearance of a state of reduced arousal level tends to occur frequently in the time zone near the switching point, some disease risk related to sleep is expected. Although various diseases related to sleep are included, in the experimental examples described below, in subjects who have been diagnosed with sleep apnea syndrome, the state of reduced arousal level is observed in the time zone near the switching point of the circadian rhythm. There was a correlation with the number of occurrences.
  • Driver 1 70 days of operation, 431.9 hours of operation
  • Driver 2 66 days of operation, 334.6 hours of operation
  • Driver 3 73 days of operation, 510.1 hours of operation
  • Hand 4 Operating days 76 days, operating hours 479.5 hours
  • the biological state analysis apparatus 1 of the present embodiment obtains a frequency gradient time series waveform by the frequency gradient time series waveform calculation means 111 for all data to be analyzed by the drivers 1 to 4, and the arousal level reduced state determination means 110 Among the obtained frequency gradient time-series waveforms, the waveform component in which the convergence tendency and the expansion tendency of the amplitude shown in FIG. 3 continue is determined as the appearance timing of the state of reduced arousal level.
  • count of appearance per predetermined unit time of the arousal level fall state determined by the degree fall state determination means 110 was calculated
  • the disease risk estimation means 120 further obtains an average value of the number of appearances per unit time (in this example, for each time zone) of each operation day, and superimposes it with the circadian rhythm data of sleepiness read from the storage unit, thereby forming one graph.
  • FIG. 5 shows the output.
  • the driver 4 indicates that the number of appearances of the state of reduced arousal level at 10 o'clock, which is a switching point where the drowsiness falls from the tendency to decrease drowsiness to the tendency to increase drowsiness, among other circadian rhythms of sleepiness.
  • the data is the largest compared with the data of the driver 4 in other time zones. From this, the driver 4 who is a SAS patient has a low arousal state frequently in a time zone near the switching point of the circadian rhythm where drowsiness is the lowest and the arousal level is high.
  • the disease risk estimation means 120 is a time zone in which the degree of arousal is higher than a predetermined level in the circadian rhythm of sleepiness, preferably a time including a switching point at which the tendency to increase sleepiness from the tendency to decrease sleepiness is switched.
  • the band when it is determined that the number of appearances per unit time of the state of reduced arousal level is greater than or equal to a predetermined number, it is preferable to use means for estimating that the risk of sleep apnea syndrome is high.
  • FIG. 6A is a frequency gradient time-series waveform at the 10 o'clock level of the driver 4 who is a SAS patient obtained by the frequency gradient time-series waveform calculating means 111
  • FIG. 6B is representative data of a healthy person.
  • the driver 4 shows a fluctuation in which the amplitude abruptly attenuates and expands at three positions (A), (B), and (C) in FIG. These correspond to the waveform components in which the convergence tendency and the expansion tendency of the frequency gradient time series waveform shown in FIG. 3 are continuous, and the driver 4 becomes sympathetic or hypoactive due to a decrease in sympathetic nerve activity. I can guess that.
  • FIG. 7A shows the distribution rate at the 10 o'clock level of the driver 4 who is a SAS patient obtained by processing the frequency gradient time-series waveform obtained by the frequency gradient time-series waveform calculating unit 111 by the distribution rate calculating unit 112.
  • FIG. 7B is a time series waveform of the distribution ratio of the driver 3 at the 10 o'clock level as representative data of a healthy person.
  • the driver 3 changes the order of fluctuations of 0.0017 Hz, 0.0035 Hz, and 0.0053 Hz from moment to moment, while the driver 4 has various types in the first half of the 10 o'clock range as the driver 3 does. Although the order of several components fluctuates, in the latter half of the 10 o'clock range, as shown by (E) in FIG. For about 10 minutes from 10 minutes to 10:45 minutes, the distribution rate of 0.0017 Hz is the highest, followed by 0.0053 Hz and 0.0035 Hz, a time zone in which the order of the distribution rate is unchanged. Yes.
  • the driver 4 since the fluctuation of the distribution rate is long and the change is small, the driver 4 has a biological rhythm collapsed in the 10 o'clock range, particularly in the late 10 o'clock range, and the autonomic nerve is deteriorating. Is estimated. Therefore, it can be said that a SAS patient is prone to fall into a state of reduced arousal level such as a hypoxia state because the autonomic nerve function does not function normally and the biological rhythm is likely to collapse. It is appropriate that the determination unit 110 determines the appearance timing of the state of reduced arousal level based on the time series waveform of the distribution rate obtained by the distribution rate calculation unit 112.
  • FIG. 8 shows the frequency gradient time-series waveform calculation means 111 for all data to be analyzed by the drivers 1 to 4, and the frequency gradient time-series waveform is obtained by the distribution ratio calculation means 112 using the frequency gradient time-series waveform.
  • the time series waveform of the distribution rate is obtained, and the arousal level lowering state determining means 110 determines the appearance timing of the state of reduced arousal level from the time series waveform of the distribution rate, and this is determined per predetermined unit time (in this example, time It is a graph compiled by obi.
  • the arousal level reduced state determination means 110 determines the appearance timing of the arousal level decreased state when the time period in which the order of fluctuations of 0.0017 Hz, 0.0035 Hz, and 0.0053 Hz does not change continues for 6 minutes. Judged. Then, in the same manner as when the determination result by the frequency gradient time series waveform shown in FIG. 5 is used by the disease risk estimation means 120, the wakefulness reduction state determined by the wakefulness reduction state determination means 110 per predetermined unit time. The average value of the number of appearances and the number of appearances per unit time for each operation day is further obtained and output as a graph superimposed on the circadian rhythm data of sleepiness read from the storage unit.
  • the number of appearances of the driver 4 in the 10 o'clock range is prominent compared to any of the other time zones of the drivers 1 to 3 and the driver 4, and the distribution rate Even if the state of reduced arousal level is determined using fluctuations, a tendency specific to a SAS patient can be detected.
  • the driver 4 has a unique waveform component of the frequency-gradient time-series waveform that correlates with the timing of frequent accidents (see FIG. 3).
  • a predetermined reference is predetermined.
  • the number of appearances of frequent accident determinations and the number of appearances of jet lag determinations is as described above that the driver 4 is at 10 o'clock, but the next most frequent is at 16:00. Was slightly different from the frequent times of other drivers 1 to 3.
  • the driver 4 was also unique in that a relatively large number of accident determinations and jet lag determinations continued for a long time from 8:00 to 12:00. All drivers tended to increase the number of accident and jet lag determinations during the time just before the end of work, but this was due to the accumulation of fatigue and the close of work. Predicted to be related to sagging.
  • the following table summarizes the time periods in which frequent occurrences of accident determination and jet lag determination occur frequently.
  • FIG. 9 shows an example of the conversion procedure for the frequent occurrence determination waveform of the driver 1 shown in FIG.
  • an approximate curve of the frequent occurrence determination frequency waveform is calculated.
  • the difference between the frequent occurrence determination frequency waveform and the approximate curve is calculated and plotted.
  • the smoothed waveform obtained by calculating and smoothing the midpoint between two adjacent points between the calculated approximate curve and the waveform of the difference in the number of determinations is defined as the determination number variation waveform.
  • FIG. 10 and 11 show the determination frequency fluctuation waveform obtained by converting the accident frequent occurrence determination waveform of FIG. 5 and the jet lag determination frequency waveform of FIG. 8 by the above procedure.
  • the circadian rhythm waveform of sleepiness is converted in the same procedure.
  • the driver 4 shows a fluctuation rhythm of a 7-hour cycle that peaks at 9 to 10 o'clock, which is the switching point of the sleepy circadian rhythm, for both frequent accidents and jet lag determination. Recognize.
  • the number of accidents of the driver 4 and the fluctuation waveform of the number of determinations of the jet lag are in the range from 8:00 to 13:00, showing a phase opposite to the circadian rhythm of drowsiness, and it can be read that the biological rhythm is broken.
  • driver 2 tends to have fewer accidents and a smaller number of jet lag determinations than other drivers.
  • driver 4 has more jet lag determination times in FIG. 12B than the other drivers.
  • the number of accidents per day by the number of consecutive working days and the number of jet lag determinations are particularly different in the number of jet lag determinations shown in FIG. 14 (b). Compared to the driver, there was a tendency to be less regardless of the number of working days.
  • the number of accidents frequently determined per hour by continuous working days was less for drivers 3 than for other drivers on the sixth day of working days.
  • the driver 4 tended to increase the number of jet lag determinations at the end of the holiday (1st day) and before the holiday (5th and 6th days). In particular, on the 5th and 6th days, the number of judgments was about 1.5 to 2.5 times that of other drivers.
  • the driver 4 has a tendency to increase both the number of accidents frequently determined and the number of jet lag determinations regardless of the operation time.
  • the number of accidents frequently determined for the drivers 2, 3, 4 suddenly increased when the operation time exceeded 200 minutes.
  • Driver 2 has less frequent accident determinations than other drivers within 100 minutes of driving time, but the number of determinations increases in more than 100 minutes, and the number of determinations tends to increase rapidly after 200 minutes. It was in.
  • the number of jet lag determinations for drivers 1, 3 and 4 increased rapidly when the driving time exceeded 200 minutes.
  • the driver 4 is a SAS patient with the unique appearance tendency shown in the graphs according to time zones shown in FIG. 5 and FIG.
  • the driver 4 shows a unique appearance tendency not seen by other drivers in various analyzes in the accident frequent occurrence waveform and jet lag determination. Therefore, the appearance tendency peculiar to each determination of the driver 4, for example, the appearance tendency in the daily average jet lag determination number in FIG. 12B and the average hourly jet lag determination number in FIG.
  • the SAS patient can be identified with higher accuracy.
  • the present invention can be applied to other than the identification of a SAS patient based on the number of accident occurrence waveforms or the number of jet lag determinations. For example, if the number of jet lag determinations tends to increase when driving for a long time like the driver 1, it can be estimated that the biological rhythm is likely to collapse due to long-time driving. Even if the number of jet lag determinations is small, such as the driver 2, if the number of frequent accident determinations tends to increase due to long-time driving, it tends to cause drowsiness and reduced attention due to long-time driving. It can be estimated that there is.
  • both the number of accident frequent determinations and the number of jet lag determinations tend to increase, the biological rhythm is likely to be disturbed by long-time driving, and sleepiness and attention It can be estimated that it tends to cause a decrease.

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Abstract

L'invention a pour objet de prédire facilement le risque de maladie lié au sommeil, notamment le syndrome de l'apnée du sommeil. La présente invention : analyse des signaux biologiques recueillis par un dispositif de mesure de signaux biologiques; détermine le moment d'apparition de fatigue, de baisse de l'attention ou d'un état de veille diminué prédéterminé, par exemple un état de somnolence; calcule le nombre d'occurrences de celui-ci par unité de temps; et estime le risque de maladie liée au sommeil à partir de la relation entre le nombre calculé d'occurrences et un rythme circadien de la somnolence.
PCT/JP2015/084810 2014-12-12 2015-12-11 Dispositif et programme informatique pour analyser un état biologique WO2016093347A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110049724A (zh) * 2016-12-14 2019-07-23 三菱电机株式会社 状态估计装置
US20230397880A1 (en) * 2020-12-15 2023-12-14 ResMed Pty Ltd Systems and methods for determining untreated health-related issues

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005160650A (ja) * 2003-12-01 2005-06-23 Terumo Corp 無呼吸症候群判定装置
JP2011167362A (ja) * 2010-02-18 2011-09-01 Delta Tooling Co Ltd 生体状態推定装置及びコンピュータプログラム
JP2012095779A (ja) * 2010-10-29 2012-05-24 Delta Tooling Co Ltd 生体状態推定装置及びコンピュータプログラム
WO2014020465A1 (fr) * 2012-08-01 2014-02-06 Koninklijke Philips N.V. Estimation du temps de conduite sûre ou de distance restant
JP2014117425A (ja) * 2012-12-14 2014-06-30 Delta Tooling Co Ltd 運転時生体状態判定装置及びコンピュータプログラム
JP2015112117A (ja) * 2013-12-07 2015-06-22 株式会社デルタツーリング 生体状態判定装置及びコンピュータプログラム

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005160650A (ja) * 2003-12-01 2005-06-23 Terumo Corp 無呼吸症候群判定装置
JP2011167362A (ja) * 2010-02-18 2011-09-01 Delta Tooling Co Ltd 生体状態推定装置及びコンピュータプログラム
JP2012095779A (ja) * 2010-10-29 2012-05-24 Delta Tooling Co Ltd 生体状態推定装置及びコンピュータプログラム
WO2014020465A1 (fr) * 2012-08-01 2014-02-06 Koninklijke Philips N.V. Estimation du temps de conduite sûre ou de distance restant
JP2014117425A (ja) * 2012-12-14 2014-06-30 Delta Tooling Co Ltd 運転時生体状態判定装置及びコンピュータプログラム
JP2015112117A (ja) * 2013-12-07 2015-06-22 株式会社デルタツーリング 生体状態判定装置及びコンピュータプログラム

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110049724A (zh) * 2016-12-14 2019-07-23 三菱电机株式会社 状态估计装置
US20230397880A1 (en) * 2020-12-15 2023-12-14 ResMed Pty Ltd Systems and methods for determining untreated health-related issues

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