JP2011083564A - Instrument and method for estimating autonomic nerve component index - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an autonomic nerve component index estimation instrument which permits autonomic nerve component indexes to be accurately estimated before one falls asleep and contributes to quantification of living improvement effects by sleep. <P>SOLUTION: The autonomic nerve component index estimation equipment includes a heartbeat strength computation section 5 computing the strength of a heartbeat signal of a subject; a variance value computation section 6 computing a variance value showing data variation during a prescribed period for data on computed heartbeat strength; a sleep stage decision section 7 deciding about the subject's sleep stage on the basis of a computed variance value; a sleep evaluation index computation section 8 computing a sleep evaluation index showing the quality of sleep on the basis of a decided sleep stage; an autonomic nerve component index estimation section 9 estimating an autonomic nerve component index in a condition where there is no body movement before one falls asleep on the basis of a computed sleep evaluation index. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an autonomic component index estimating apparatus and an autonomic component index estimating method for estimating an autonomic component index indicating an activity state of an autonomic nerve, and more particularly to a technique focusing on the relationship between sleep and an autonomic component index.

  Biological activities that maintain life such as heartbeat and respiration, which are important in human physical activity, depend on autonomic nerves. The autonomic nervous system consists of a sympathetic nervous system and a para-switching nervous system. The sympathetic nervous system is active during tension (when active), and the para-switching nervous system is active during relaxation (when stopping). To be active. In particular, sympathetic nervous system activities have basic functions that govern movements such as heartbeat and breathing, which are basic biological activities. By grasping sympathetic nervous system activities, body activities can be accurately grasped. It becomes possible. Therefore, grasping the activity of the sympathetic nervous system in real time is important for health management.

  Conventionally, the activity of the autonomic nervous system has been grasped by looking at the distribution of the power spectral density obtained by frequency analysis of the data of the heart rate RR interval. That is, in the conventional method, the sympathetic nervous system is active when the power spectrum density is high in the low frequency region (LF), and the paraswitching nervous system is active when the power spectrum density is high in the high frequency region (HF). It was determined that there was. However, such a conventional method has a problem that it requires complicated arithmetic processing and is difficult to grasp in real time.

  Therefore, as in Patent Document 1, the inventor of the present application calculates a variance value of data within a predetermined time of the heart rate intensity signal calculated based on the heart rate signal of the subject, and based on the variance value, the sympathetic nerve activity is calculated. Propose technology to grasp.

  Moreover, as a technique for measuring an autonomic nerve component index during sleep, for example, those described in Patent Document 2 and Patent Document 3 have been proposed.

  JP 2008-73478 A   JP 2008-93416 A   JP 2008-237574 A

  By the way, it is generally known that the sympathetic nerve is strongly stimulated when the stress is increased during the daytime and / or for a long time. On the other hand, since it is known that there is some relationship between sleep and the autonomic nervous component index as described in Patent Document 1 to Patent Document 3 and the like described above, the quality of sleep is the daytime. It is inferred that it changes according to the living conditions of the people.

  However, in determining the relationship between sleep, the autonomic component index, and the daytime living conditions, considering that the stress received during the daytime due to sleep is eliminated, the autonomic component index before sleep is defined as sleep. An accurate relationship cannot be determined unless it is accurately determined based on the relationship. In other words, if you do not use the caring person's autonomic nerve component index obtained at the appropriate timing from the relationship with sleep in order to manage the caregiver's health at the care site, etc., life improvement by sleep will be improved. It cannot be quantified and the necessary care method cannot be optimized.

  The present invention has been made in view of such circumstances, and can estimate an autonomic nerve component index before falling asleep with high accuracy and contribute to quantifying the life improvement effect due to sleep. It is an object of the present invention to provide an index estimation device and an autonomic component index estimation method.

  As a result of earnest research on the relationship between sleep quality and autonomic component index, the inventor of the present application has a predetermined correlation between them, particularly between the autonomic component index and sleep quality before falling asleep. The present inventors have found that there is a characteristic relationship and have completed the present invention.

  That is, the autonomic nerve component index estimation apparatus according to the present invention that achieves the above-described object includes a biological signal detection unit that detects a biological signal of a subject, and a biological body that calculates the strength of the biological signal detected by the biological signal detection unit. The signal intensity calculation means, the variance value calculation means for calculating the variance value indicating the variation of the data for a predetermined time, and the variance value calculation means for the vital sign signal strength data calculated by the vital sign signal strength calculation means. A sleep evaluation index for calculating a sleep evaluation index indicating the quality of sleep based on the sleep stage determined by the sleep stage determination means and the sleep stage determination means determined by the sleep stage determination means Autonomy in a state without body movement before falling asleep based on the sleep evaluation index calculated by the calculation means and the sleep evaluation index calculation means It is characterized in that it comprises a autonomic component index estimating means for estimating a through component index.

  The autonomic nerve component index estimation method according to the present invention that achieves the above-described object includes a biological signal detection step of detecting a biological signal of a subject by a predetermined biological signal detection means, and the biological signal detection by a processor that performs signal processing. A variance indicating a variation in data for a predetermined time with respect to the vital signal strength calculation step of calculating the strength of the vital signal detected in the step and the vital signal strength data calculated in the vital signal strength calculation step by the processor; A dispersion value calculation step of calculating a value, a sleep stage determination step of determining the sleep stage of the subject based on the dispersion value calculated by the processor in the dispersion value calculation step, and the sleep stage determination by the processor Sleep evaluation index calculation that calculates a sleep evaluation index indicating the quality of sleep based on the sleep stage determined in the process And an autonomic nerve component index estimating step for estimating an autonomic nerve component index in a state of no body movement before sleep based on the sleep evaluation index calculated by the processor in the sleep evaluation index calculating step. It is characterized by.

  Such an autonomic component index estimation apparatus and autonomic component index estimation method according to the present invention calculate a sleep evaluation index indicating the quality of sleep based on a variance value of the biological signal strength of the subject, Based on the correlation with the nerve component index, the autonomic nerve component index in the state of no body movement before sleep corresponding to the calculated sleep evaluation index is estimated.

  In the present invention, it is possible to accurately calculate a sleep evaluation index, and furthermore, based on this sleep evaluation index, it is possible to estimate an autonomic nervous component index in a state without body movement before falling asleep with high accuracy. The improvement effect can be quantified, and can greatly contribute to the health management of the subject.

  It is a figure which shows the structure of the autonomic nerve component parameter | index estimation apparatus shown as embodiment of this invention.   It is a figure which shows the structure of the autonomic nerve component parameter | index estimation apparatus shown as embodiment of this invention, and is a partial cross section figure when it sees from the arrow direction in FIG.   It is a flowchart which shows a series of procedures at the time of estimating an autonomic nerve component parameter | index in the autonomic nerve component parameter | index estimation apparatus shown as embodiment of this invention.   (A) shows time series data of heart rate intensity, (b) is a diagram showing time series data of variance value of heart rate intensity.   It is a figure which shows the time series data obtained by performing a 300 point moving average process with respect to the time series data of the variance value of the heart rate intensity shown in FIG.4 (b).   It is a figure which shows the generation frequency for every dispersion value division.   It is a figure which shows the relationship between the dispersion | distribution value of a heart rate intensity, and a sympathetic nerve component parameter | index.   It is a figure which shows the relationship between a sleep evaluation parameter | index and a sympathetic nerve component normalization parameter | index.   It is a figure which shows the structure of another biological signal detection part.

  Hereinafter, specific embodiments to which the present invention is applied will be described in detail with reference to the drawings.

  This embodiment is an autonomic nerve component index estimating device that estimates an autonomic nerve component index indicating the activity state of an autonomic nerve. In particular, the autonomic nerve component index estimating device estimates an autonomic nerve component index, particularly a sympathetic nerve component index with high accuracy based on the quality of sleep obtained from a biological signal of a cared person at a predetermined timing.

  FIG. 1 shows a configuration representing processing of the autonomic nerve component index estimation device shown as an embodiment of the present invention as a block, and FIG. 2 shows a partial cross-sectional view when viewed from the direction of the arrow in FIG. Yes. That is, the autonomic nerve component index estimation device includes a biological signal detection unit 1 that detects a biological signal of a subject, a signal amplification unit 2 that amplifies the biological signal detected by the biological signal detection unit 1, and the signal amplification unit 2 Filter unit 3 that performs a filtering process on the biological signal amplified by the automatic gain control unit 4 that automatically performs gain control on the heartbeat signal that has passed through the filter unit 3, and calculates the intensity of the heartbeat signal The heart rate signal intensity calculating unit 5, the variance value calculating unit 6 for calculating the variance value of the heart rate intensity calculated by the heart rate intensity calculating unit 5, and the heart rate intensity variance value calculated by the variance value calculating unit 6 A sleep stage determination unit 7 for determining a sleep stage of the subject based on the sleep stage, and a sleep evaluation index for calculating a sleep evaluation index indicating the quality of sleep based on the sleep stage determined by the sleep stage determination unit 7 It includes an index calculator 8, and autonomic component index estimation unit 9 estimates the autonomic component index in the absence of falling asleep before the body motion based on the sleep evaluation index calculated by the sleep evaluation index calculating unit 8. Of these units, at least the heartbeat signal intensity calculation unit 5, the variance value calculation unit 6, the sleep stage determination unit 7, the sleep evaluation index calculation unit 8, and the autonomic component index estimation unit 9 are, for example, signal processing A dedicated processor such as a DSP (Digital Processing Unit) mounted on an expansion board that can be mounted on a computer, or implemented as a program that can be executed using hardware such as a CPU (Central Processing Unit) or memory in a computer And can be implemented.

  The biological signal detection unit 1 is a non-invasive sensor that detects minute biological signals of a subject. Specifically, the biological signal detection unit 1 includes a pressure detection tube 1a and a minute differential pressure sensor 1b that is a sensor that detects minute pressure fluctuations in the air accommodated in the pressure detection tube 1a. And constitutes a non-invasive biological signal detection means.

  As the pressure detection tube 1a, a tube having an appropriate elasticity so that the internal pressure fluctuates corresponding to the pressure fluctuation range of the biological signal is used. Further, as the pressure detection tube 1a, it is necessary to appropriately select the volume of the hollow portion of the tube in order to transmit the pressure change to the fine differential pressure sensor 1b at an appropriate response speed. When the pressure detection tube 1a cannot satisfy the appropriate elasticity and the volume of the hollow portion at the same time, the hollow portion of the pressure detection tube 1a is loaded with a core wire of an appropriate thickness over the entire length of the tube, and the volume of the hollow portion is set appropriately Can be taken.

  Such a pressure detection tube 1 a is disposed on a hard sheet 12 laid on the bed 11. In the autonomic nerve component index estimation device, an elastic cushion sheet 13 is laid on the hard sheet 12, and the subject lies on the pressure detection tube 1a. In addition, the pressure detection tube 1a is good also as a structure which stabilizes the position of the pressure detection tube 1a by setting it as the structure incorporated in the cushion sheet | seat 13 grade | etc.,.

  The minute differential pressure sensor 1b is a sensor that detects minute fluctuations in pressure. In the present embodiment, a low-frequency condenser microphone type sensor is used as the fine differential pressure sensor 1b. However, the present invention is not limited to this, and any sensor having an appropriate resolution and dynamic range may be used. Good. The low-frequency condenser microphone used in the present embodiment is replaced with a general acoustic microphone that does not consider the low-frequency region, and a low-frequency region characteristic is provided by providing a chamber behind the pressure-receiving surface. Is significantly improved, and is suitable for detecting minute pressure fluctuations in the pressure detection tube 1a. In addition, this condenser microphone is excellent for measuring minute differential pressure, has a resolution of 0.2 Pa and a dynamic range of about 50 Pa, and is compared with a fine differential pressure sensor using a ceramic that is usually used. Therefore, it is suitable for detecting a minute pressure applied to the pressure detection tube 1a through a biological signal passing through the body surface. The frequency characteristic shows a substantially flat output value between 0.1 Hz and 20 Hz, and is suitable for detecting minute biological signals such as heartbeat and respiration.

  In the present embodiment, two sets of pressure detection tubes 1a are provided so that one detects a biological signal of a part of the subject's chest and the other detects a part of the subject's buttocks. It is configured to detect a biological signal regardless of the posture. In the autonomic nerve component index estimation device, the pressure detection tube 1a may be arranged only in one of the chest region and the buttocks region. The biological signal detected by such a biological signal detection unit 1 is supplied to the signal amplification unit 2. The autonomic nerve component index estimation device can be easily used in daily life by adopting such a non-invasive configuration for detecting a biological signal, and is particularly suitable for use in nursing of elderly people. .

  The signal amplification unit 2 amplifies the signal detected by the biological signal detection unit 1 so that it can be processed in a later processing step. The biological signal amplified by the signal amplifying unit 2 is supplied to the filter unit 3.

  The filter unit 3 extracts a heartbeat signal by removing unnecessary signals such as a respiratory signal from the biological signal amplified by the signal amplification unit 2 using a bandpass filter or the like. That is, the biological signal detected by the biological signal detection unit 1 is a signal in which various vibrations emitted from the human body are mixed, and various signals such as a heartbeat signal, a respiratory signal, and a signal indicating a turn are included therein. include. Among these, the heartbeat signal is a signal in which a change in pressure (that is, blood pressure) based on the pump function of the heart becomes vibration and is included in the biological signal. In the autonomic nerve component index estimation device, the filter unit 3 extracts it and recognizes it as a heartbeat signal. The heartbeat signal that has passed through the filter unit 3 is supplied to the automatic gain control unit 4.

  The automatic gain control unit 4 is a so-called AGC circuit that automatically performs gain control so that the output of the filter unit 3 falls within a predetermined signal level range. The gain control by the automatic gain control unit 4 sets the gain so that the amplitude of the output signal becomes small when the peak value of the signal exceeds a predetermined upper limit threshold, and the peak value falls below the predetermined lower limit threshold. In such a case, the gain is set so that the amplitude increases. The automatic gain control unit 4 supplies the value (coefficient) of gain when such gain control is performed to the heartbeat signal strength calculation unit 5.

  The heartbeat signal intensity calculation unit 5 calculates the intensity of the heartbeat signal based on the gain control coefficient applied to the heartbeat signal in the automatic gain control unit 4. The gain value obtained from the AGC circuit described above is set to be small when the signal magnitude is large and large when the signal magnitude is small, so that the signal intensity can be indicated using the gain value. It is desirable to set a function indicating the signal strength so as to be inversely proportional to the value of the gain. In order to generalize the calculated heart rate intensity by eliminating individual differences, the heart rate signal intensity calculation unit 5 normalizes the calculated heart rate intensity into a percentage expression value and supplies the normalized expression value to the variance calculation unit 6.

  The variance calculation unit 6 calculates, for the heart rate intensity data calculated by the heart rate intensity calculation unit 5, a variance value indicating a variation in data for a predetermined time. In this embodiment, when an index indicating a variation in data sampled within a certain time until a certain time point is referred to as a variance value, the standard deviation of the data is adopted as the variance value. Yes. Specifically, assuming that the heart rate intensity data is measured every second, the variance calculation unit 6 calculates a variance value of data for 60 seconds out of a series of heart rate intensity data. A process of calculating data for 60 seconds retroactively from a certain point of time, that is, calculating a variance value of 60 heart rate intensity data, and then calculating a data dispersion value for 60 seconds retroactively after the next one second. Repeat. As a result, the variance calculation unit 6 can obtain time-series data at intervals of 1 second with respect to the variation (variance value) of the heart rate intensity. The variance calculation unit 6 supplies the time series data thus obtained to the sleep stage determination unit 7.

  The sleep stage determination unit 7 determines the sleep stage of the subject based on the time-series data of the dispersion value of the heart rate intensity supplied from the dispersion value calculation unit 6. That is, the sleep stage determination unit 7 determines the sleep stage of the subject during sleep, that is, the type of REM sleep and four stages of non-REM sleep, based on the tendency (variation tendency) of the variance of the heart rate intensity. When there is a body motion, the signal shakes greatly and the variance value of the heart rate intensity also increases. Therefore, the sleep stage determination unit 7 performs abnormal value processing such as replacing the variance value of the heart rate intensity exceeding the predetermined value with the predetermined value in order to remove the influence of such an abnormal value. And the sleep stage determination part 7 performs a moving average process for every data of the predetermined number of dispersion values, such as 300 points, for example, and performs a smoothing process. Then, the sleep stage determination unit 7 converts the time series data of the dispersion value subjected to the smoothing process into five stages corresponding to the above-described REM sleep and four-stage non-REM sleep according to a predetermined dispersion value category. And the occurrence frequency (occurrence time) for each category is calculated. At this time, as will be described in detail later, the sleep stage determination unit 7 sets the case where the dispersion value of the heart rate intensity is about 2.5% to the non-REM sleep stages 3 and 4 which are deep sleep in the international sleep depth determination standard. By making it correspond, the method by this invention and the international sleep depth criterion are linked | related. The sleep stage determination result determined by the sleep stage determination unit 7 is supplied to the sleep evaluation index calculation unit 8.

  The sleep evaluation index calculation unit 8 calculates a sleep evaluation index indicating the quality of sleep based on the sleep stage determined by the sleep stage determination unit 7. Specifically, the sleep evaluation index calculation unit 8 performs predetermined weighting on the occurrence frequency for each category of the time-series data of the variance value described above, and calculates the value as the sleep evaluation index. The processing by the sleep evaluation index calculation unit 8 will be described in detail later together with the processing by the sleep stage determination unit 7.

  Based on the sleep evaluation index calculated by the sleep evaluation index calculation unit 8, the autonomic nerve component index estimation unit 9 estimates an autonomic nerve component index, particularly a sympathetic nerve component index, in a state where there is no body movement before falling asleep. Specifically, the autonomic nerve component index estimation unit 9 stores map data indicating the correspondence between the sleep evaluation index and the autonomic nerve component index in a memory or the like (not shown), and is calculated by the sleep evaluation index calculation unit 8. In response to the sleep evaluation index, the autonomic component index is read from this memory or the like. The correspondence between the sleep evaluation index and the autonomic nerve component index will be described in detail later. The autonomic nerve component index estimation unit 9 outputs the estimated autonomic component index together with the above-described time-series data of dispersion values, sleep evaluation indices, etc., and displays them on a display device (not shown) or prints them by a printing device, Or stored as data in a storage device.

  Such an autonomic component index estimation apparatus estimates an autonomic component index according to a series of procedures as shown in FIG.

  First, in the autonomic nervous component index estimation apparatus, as shown in FIG. 3, a heart rate intensity signal is captured in step S1. That is, in the autonomic nerve component index estimation device, the biological signal detected by the biological signal detection unit 1 is amplified by the signal amplification unit 2, and unnecessary signals such as respiratory signals are removed by the filter unit 3 by a bandpass filter or the like. To detect the heartbeat signal. In the autonomic nerve component index estimation device, the peak value is controlled by performing gain control on the detected heartbeat signal by the automatic gain control unit 4, and the automatic gain control unit at this time is controlled by the heart rate intensity calculation unit 5. The heart rate intensity is calculated using the gain value of 4. Thereby, time-series data of heart rate intensity as shown in FIG.

  Subsequently, in the autonomic nerve component index estimation device, the variance value of the heart rate intensity is calculated by the variance value calculation unit 6 in step S2. Specifically, the variance value calculation unit 6 calculates a variance value (standard deviation) of data for 60 seconds retroactively from each time point. Thereby, time-series data of the dispersion value of the heart rate intensity as shown in FIG. 4B is obtained. The unit of the heart rate intensity dispersion value shown in FIG. 4B is a percentage based on the assumed maximum heart rate intensity dispersion value.

  In the autonomic nerve component index estimation apparatus, the sleep stage determination by the sleep stage determination unit 7 and the sleep evaluation index calculation by the sleep evaluation index calculation unit 8 are performed in steps S3 to S6.

  That is, in the autonomic nerve component index estimation device, in step S3, the sleep stage determination unit 7 replaces the variance value of the heart rate intensity exceeding the predetermined value among the time series data of the variance value of the heart rate intensity with the predetermined value. And the like, and a moving average process is performed for each data of a predetermined number of variance values to perform a smoothing process. Thereby, time series data of the variance value of the heart rate intensity after the moving average process as shown in FIG. 5 is obtained.

  Subsequently, in the autonomic nervous component index estimation device, in step S4, the sleep stage determination unit 7 converts the time series data of the dispersion values subjected to the smoothing process into five stages according to the predetermined dispersion value categories. And the occurrence frequency (occurrence time) for each category is calculated. FIG. 6 shows a specific example of the result. The five levels of variance values are expressed as a percentage of the maximum variance value (standard deviation) that appears in the subject. Here, 2.5% or less, 2.5 to 3.5%, It is classified into five categories of 3.5-4.5%, 4.5-5.5%, 5.5% or more, and how long the variance value (standard deviation) corresponding to these categories appears. This is a bar graph showing whether to do it. In the autonomic nerve component index estimation device, in step S5, the sleep stage determination unit 7 obtains the ratio of the time of each section to the total sleep time as the determination result of the sleep stage.

  In addition, the variance value classification of heart rate intensity can eliminate the influence of individual differences by determining the boundary value of the classification using the average value and standard deviation (variance value) of the time series data. For example, normalization is performed by using an average value of time series data, a value that is larger than the average value by the standard deviation, a value that is larger than the average value by twice the standard deviation, and a value that is smaller by the standard deviation of the average value. Therefore, it becomes possible to eliminate the influence of individual differences. The heart rate intensity variance values shown in FIG. 6 are determined by normalization in this way.

  Here, in the so-called international sleep stage determination method (polysonograph), the sleep depth is determined on the basis of the ratio of the δ wave component. It has been found that the ratio of the wave component and the dispersion value of the heart rate intensity are approximately inversely proportional. When the δ wave component is 20% or more, it is determined that the sleep is deep, which corresponds to a variance value of the heart rate intensity of about 2.5% or less. As described above, in the autonomic nerve component index estimation device, the case where the variance value of the heart rate intensity is about 2.5% is made to correspond to the non-REM sleep stages 3 and 4 which are deep sleep in the international sleep depth criterion. For important deep sleep standards, we are processing in line with international sleep depth criteria.

  As can be seen from FIG. 4B and FIG. 6, the smaller the variance value of the heart rate intensity is, the deeper sleep is considered to be mentally stable. Therefore, the variance value of the heart rate intensity is 2.5% or less. When the value of is shown, it may be considered that the person is mentally stable and does not sleep well with shallow sleep as the dispersion value increases.

Then, in the autonomic nervous component index estimation device, the sleep evaluation index calculation unit 8 calculates the sleep evaluation index in step S6. At this time, the sleep evaluation index calculation unit 8 calculates the sleep evaluation index using the following formula (1).
S = α · t ₁ + β · t ₂ + γ · t ₃ + δ · t ₄ + ε · t ₅ (1)

In the above formula (1), t ₁ , t ₂ , t ₃ , t ₄ , and t ₅ have dispersion values of 2.5% or less, 2.5 to 3.5%, and 3.5 to 4.5, respectively. %, 4.5 to 5.5%, and the total time when the variance value corresponding to the category of 5.5% or more occurs. Further, α, β, γ, δ, and ε are weighting factors associated with the international sleep depth criterion, and are set to be larger as the variance value is smaller. When the frequency distribution shown in FIG. 6 is obtained, the sleep evaluation index is calculated by setting α = 10, β = 4, γ = 2, δ = 1, and ε = 0.
S = 2.17 × 10 + 1.65 × 4 + 1.07 × 2 + 1.22 × 1 + 1.23 × 0 = 31.66
It becomes.

  In the autonomic nerve component index estimating apparatus, when the sleep evaluation index is calculated in this way, in step S7, the autonomic nerve component index estimating unit 9 refers to map data stored in a memory or the like (not shown), and sleep evaluation index. The autonomic nerve component index in the absence of body movement before sleep corresponding to the sleep evaluation index calculated by the calculation unit 8 is read.

  Here, in Japanese Patent Laid-Open No. 2008-73478 previously proposed by the inventor of the present application, processing focusing on the fact that the variance value of the heart rate intensity and the sympathetic nerve component index are substantially proportional is performed. That is, it has been experimentally confirmed that the sympathetic component index is substantially proportional to the variance value of the heart rate intensity as shown in FIG. In FIG. 7, the average correlation coefficient between the dispersion value of the heart rate intensity and the sympathetic component index is 0.746, and the p value in the non-correlation test by the two-sided test is less than 0.0001. Show.

  On the other hand, in the method of the present invention, the fact that the sleep evaluation index calculated in step S6 has a correlation with the autonomic nerve component index in a state without body movement before falling asleep is used. That is, the inventor of the present application uses the acceleration pulse wave measurement system “Pulse Analyzer Plus” to determine the autonomic nerve component index obtained based on the finger pulse wave measured for 5 minutes for 7 subjects without body movement before falling asleep. And the relationship between the sleep evaluation index calculated by the method of the present invention and the predetermined number of days. The result is shown in FIG. Note that the autonomic nerve component index is normalized by the maximum value among the autonomic nerve component indices measured during the experiment period to obtain a percentage expression value in order to generalize without affecting by individual differences.

  As is clear from this result, it is understood that a negative substantially proportional relationship is established between the sleep evaluation index and the autonomic component index (sympathetic component index). In this experiment, subjects who tend to sleep are selected. In modern times, it is said that 20% of the population is insomnia, especially in the elderly, many people are insomnia, and 30-35% of the population is insomnia. Yes. Therefore, it is conceivable that a negative substantially proportional relationship is established between the sleep evaluation index and the autonomic component index (sympathetic component index), particularly for people who have such an insomnia tendency. In this way, it is extremely useful to be able to quantify the relationship between the sleep evaluation index and the autonomic component index for people who are particularly insomnia.

  In the autonomic nerve component index estimating apparatus, the autonomic nerve component index can be estimated with high accuracy based on the sleep evaluation index according to such a series of procedures. Since this estimated autonomic component index is in a state of no body movement before falling asleep, it reflects the stress received during the daytime. Therefore, when the caregiver's health management is performed at a nursing care site or the like, the caregiver can determine the caregiver's sleep by browsing the caringee's autonomic nerve component index thus estimated. The life improvement effect can be accurately grasped, for example, the autonomic nerve component normalization index normalized with the maximum value of the autonomic nerve component index estimated over a predetermined period is within a predetermined range such as within 70%. , Guidance to improve caregivers' daytime lifestyles, guidance to bring moderate fatigue and tension through moderate exercise, psychological care, and appropriate sleep guidance. The sleep evaluation index can be effectively used as one means for improving the function due to sleep.

  As described above, in the autonomic component index estimating apparatus shown as the embodiment of the present invention, the sleep evaluation index indicating the quality of sleep is accurately calculated based on the dispersion value of the heart rate intensity, and the sleep evaluation is further performed. Based on the index, it is possible to accurately estimate the autonomic component index in the absence of body movement before falling asleep. Therefore, since the autonomic component index estimation device can quantify the life improvement effect due to sleep, it can greatly help the caregiver's health management and optimize the care method required for each caregiver Can be achieved.

  In addition, when the function of the caregiver is improved by performing rehabilitation and the like, since the sleep evaluation index and the autonomic component index are also recommended ranges, by using this autonomic component index estimating device, It is possible to quantitatively evaluate the effects of care.

  Furthermore, when assessing the effect of bedding function based on the quality of sleep, how to feel sleep changes depending on the daytime situation of the subject, so conventionally, whether the quality of sleep is due to the improvement of bedding function, I couldn't distinguish whether it was due to increase or decrease in daytime stress, and I could not make an optimal evaluation. In contrast, in the autonomic nerve component index estimating device, an autonomic nerve component normalization index obtained by normalizing the autonomic nerve component index estimated over a predetermined number of days is obtained, and when this index is substantially the same, that is, stress By adopting the evaluation result of the bedding function when the degree of the bedding is substantially the same, the true bedding function can be evaluated.

  The present invention is not limited to the embodiment described above.

  For example, in the above-described embodiment, as a method for detecting a heartbeat signal, a method for extracting a heartbeat signal from a biological signal obtained by the unconstrained biological signal detection unit 1 laid under the body of the subject is shown. The present invention is applicable to any detection means that can continuously obtain a heartbeat signal or a signal equivalent to the heartbeat signal. For example, the present invention can be applied as the biological signal detection unit 1 as long as it is a heart rate meter or pulse meter of the type worn on the body such as the wrist or the upper arm, and can record data continuously. is there.

  Further, as the biological signal detection unit 1, an air mat type detection unit as shown in FIG. 9 may be used instead of using the hollow tube described above. That is, the biological signal detection unit 20 shown in FIG. 9 is configured such that an air tube 20b is connected to one end of an air mat 20a in which air is enclosed, and a fine differential pressure sensor 20c is connected to the air tube 20b. . In addition, the thing similar to what was demonstrated in the case of the biosignal detection part 1 using a hollow tube can be used for the micro differential pressure sensor 20c.

  Furthermore, in the above-described embodiment, the standard deviation is adopted as the variance value indicating the variation in heart rate intensity. However, the present invention may employ, for example, statistics such as variance, sum of deviation squares, and a predetermined range.

  Thus, it goes without saying that the present invention can be modified as appropriate without departing from the spirit of the present invention.

DESCRIPTION OF SYMBOLS 1,20 Biosignal detection part 1a Pressure detection tube 1b, 20c Slight differential pressure sensor 2 Signal amplification part 3 Filter part 4 Automatic gain control part 5 Heart rate intensity calculation part 6 Variance value calculation part 7 Sleep stage determination part 8 Sleep evaluation index calculation Part 9 Autonomic nerve component index estimation part 11 Bed 12 Hard sheet 13 Cushion sheet 20a Air mat 20b Air tube

Claims (8)

A biological signal detection means for detecting a biological signal of the subject;
A biological signal intensity calculating means for calculating the intensity of the detected biological signal by the biological signal detecting means,
A dispersion value calculating means for calculating a dispersion value indicating a variation in data for a predetermined time for the data of the biological signal intensity calculated by the biological signal intensity calculating means;
Sleep stage determination means for determining the sleep stage of the subject based on the dispersion value calculated by the dispersion value calculation means;
A sleep evaluation index calculating means for calculating a sleep evaluation index indicating the quality of sleep based on the sleep stage determined by the sleep stage determination means;
An autonomic nerve component index estimating unit that estimates an autonomic nerve component index in a state of no body movement before sleep based on the sleep evaluation index calculated by the sleep evaluation index calculating unit Index estimation device. The autonomic nerve component index estimation unit outputs the autonomic nerve component normalization index normalized by the maximum value among the autonomic nerve component indexes estimated over a predetermined period. Estimating device. The sleep stage determination means classifies the dispersion value of the biological signal intensity calculated by the dispersion value calculation means into a sleep stage in an international sleep depth criterion according to a predetermined dispersion value classification, The sleep stage is determined. The autonomic nerve component index estimation apparatus according to claim 1 or 2. The sleep stage determination means associates the case where the variance value of the biological signal intensity calculated by the variance value calculation means is about 2.5% with the non-REM sleep stages 3 and 4 in the international sleep depth determination standard. autonomic component index estimation apparatus according to claim 3, wherein the classifying manner. The sleep stage determination means calculates the occurrence frequency of the variance value of the biosignal intensity for each of the categories,
The autonomic nervous component index estimation apparatus according to claim 3, wherein the sleep evaluation index calculation means calculates a sleep evaluation index by weighting a variance value of the biological signal intensity for each category. The biological signal detection means detects a biological signal by means laid under the body of the subject, and extracts a heartbeat signal from the detected biological signal. The autonomic nerve component index estimation apparatus according to claim 1. The autonomic nerve component according to claim 6, wherein the biological signal detection means detects a pressure fluctuation of the air accommodated inside the means laid under the body of the subject by a slight differential pressure sensor. Index estimation device. A biological signal detecting step of detecting a biological signal of the subject by a predetermined biological signal detection means,
A biological signal strength calculating step of calculating the strength of the biological signal detected in the biological signal detecting step by a processor that performs signal processing;
A variance value calculating step of calculating a variance value indicating a variation in data of a predetermined time for the data of the vital signal strength calculated in the vital signal strength calculation step by the processor;
A sleep stage determination step of determining the sleep stage of the subject based on the variance value calculated in the variance value calculation step by the processor;
A sleep evaluation index calculating step for calculating a sleep evaluation index indicating the quality of sleep based on the sleep stage determined in the sleep stage determination step by the processor;
An autonomic nerve component index estimating step for estimating an autonomic nerve component index in a state without body movement before falling asleep based on the sleep evaluation index calculated in the sleep evaluation index calculating step by the processor. Autonomic component index estimation method.

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