CN210446992U

CN210446992U - Physical function examination device

Info

Publication number: CN210446992U
Application number: CN201821043326.2U
Authority: CN
Inventors: 苗铁军; 宋军
Original assignee: Beijing Boshi Linkag Technology Co ltd
Current assignee: Beijing Boshi Linkag Technology Co ltd
Priority date: 2018-06-29
Filing date: 2018-06-29
Publication date: 2020-05-05
Anticipated expiration: 2028-06-29

Abstract

The utility model discloses a physical function examination device, which comprises a detection unit, a display unit and a control unit, wherein the detection unit is used for detecting a physical information signal of a body; the signal separation unit is used for separating the body information signal detected by the detection unit into a pulse wave signal, a respiration signal and a body movement signal; a sleep state and stage determination unit for determining that a sleep state and a sleep stage of the body conform to one of a quick-action eye sleep state, a deep sleep state, a light sleep state, and an arousal state, based on the pulse wave signal and the body motion signal; a circulatory organ function evaluation unit for extracting a pulse wave signal of the body in the deep sleep state and evaluating a circulatory organ function of the body; and the brain control function evaluation unit is used for extracting the body movement signal of the body in the quick-action eye sleeping state and evaluating the brain control function of the body.

Description

Physical function examination device

Technical Field

The present invention relates to a physical function test device, and more particularly, to a physical function test device, a physical function test method, and a physical function test system for testing a physical function based on physical information during sleep.

Background

Conventionally, a diagnostic method that is applied only when clinical symptoms and physiological dysfunction symptoms are very significant as a result of examination by medical equipment and medical images has been developed, and a simple, objective, and quantitative method for early diagnosis, which is a method before significant symptoms appear, has not been developed yet.

As a result, although there is a sign that the body rhythm fluctuates or the softness of the body changes before significant symptoms appear, the change is subtle and is greatly disturbed by the physiological instability of the body and the external environment, and it is difficult to accurately capture the physiological function reduction or physiological dysfunction.

In addition, when physical functions are examined by measuring blood pressure or physical examination, it is preferable that the body be in a relaxed state so that the examination can be performed accurately. However, during the day, sympathetic nerve activity dominates, and interference caused by instability inside and outside the body is unavoidable, so that a true relaxed state cannot be realized.

Therefore, various physical function test devices for testing the body function based on the physical information when the body is most stable and in a relaxed state in sleep have been proposed.

For example, patent document 1 proposes a piezoelectric sensor provided on bedding for detecting body information during sleep, a method and an apparatus for measuring health/disease severity, and a measurement system (hereinafter, this technique is referred to as conventional example 1); the measuring method extracts pulse wave beating component data from a raw signal obtained by a piezoelectric sensor, extracts heart rate time series data from the extracted pulse wave beating component data, performs approximate straight line removal on the extracted heart rate time series data, performs fluctuation analysis, and determines the health/disease severity degree from the result.

Patent document 2 proposes a system for measuring a sleep state at home, which uses a sleep state monitoring device for performing health management of a user by collecting data of a jitter interval during sleep via a computer network and detecting a change in a numerical value indicating fluctuation of the jitter interval, the sleep state monitoring device including: a pressure waveform obtaining unit for obtaining a pressure waveform from a pressure detecting unit that is in contact with a body of a user; a pulsation extraction unit configured to perform prescribed processing on the pressure waveform, thereby acquiring a pulsation waveform; a jitter interval calculation unit for calculating a jitter interval; a sleep separation (sleep stage) determination unit for determining a sleep stage, which is a deep sleep stage of a user, from the beat interval (hereinafter, this technique is referred to as conventional example 2).

Patent document 3 proposes a piezoelectric sensor provided on bedding for detecting body information during sleep, and a system thereof; the system presumes a sleep state conforming to the quick-moving eye sleep or the non-quick-moving eye sleep based on the heart rate data obtained from the piezoelectric sensor, while displaying the sleep quality (hereinafter, this technique is referred to as prior example 3).

Patent document 1: japanese laid-open patent publication No. 2008-104529

Patent document 2: japanese patent laid-open publication No. 2011-115188

Patent document 3: japanese patent laid-open No. 2008-104528.

SUMMERY OF THE UTILITY MODEL

The present invention has been made to overcome the above-described problems, and an object of the present invention is to provide a physical function testing device, in conventional example 1, which measures the health and severity of a disease of a body using all data during sleep, but has problems in that the fluctuation of a body rhythm is not uniform, and the fluctuation of the body rhythm is not considered to be an average value due to the difference of time zone and sleep stage, and the determination is made based on the unevenness, so that the reproducibility of the value is poor and the accuracy is low.

In conventional example 2, there is a problem that, since a body rhythm is present during sleep similarly to a circadian rhythm during daytime, evaluation values of body fluctuations at different stages during sleep vary. In addition, the value of the body fluctuation varies depending on the time zone of occurrence not only for different sleep stages but also for the same deep sleep stage. For example, there is a difference in the values of body fluctuations between the first occurring deep sleep stage and the last occurring deep sleep stage. These variations exceed individual differences, and body fluctuations are averaged regardless of the differences in these variations, and the health state is determined based on the averages.

In conventional example 3, only the sleep state and sleep quality of the body were evaluated, and the health state was not evaluated.

The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a physical function testing device, a physical function testing method, and a physical function testing system for testing a physical function with high reproducibility and high accuracy based on physical information during sleep.

The utility model discloses a following technical scheme realizes:

a physical function examination apparatus comprising:

a detection unit for detecting a body information signal of a body;

the signal separation unit is used for separating the body information signal detected by the detection unit into a pulse wave signal, a respiration signal and a body movement signal;

a sleep state and stage determination unit for determining that a sleep state and a sleep stage of the body conform to one of a quick-action eye sleep state, a deep sleep state, a light sleep state, and an arousal state, based on the pulse wave signal and the body motion signal;

a circulatory organ function evaluation unit for extracting a pulse wave signal of the body in the deep sleep state and evaluating a circulatory organ function of the body;

and the brain control function evaluation unit is used for extracting the body movement signal of the body in the quick-action eye sleeping state and evaluating the brain control function of the body.

Further, the detection unit is a piezoelectric sensor that is in contact with a part of the body directly or via clothing.

Further, in the sleep state and stage determination unit, the estimated interval data of the pulse wave signal is estimated by performing fast fourier transform on the pulse wave signal, and Z-Score of the estimated interval data of the pulse wave signal is calculated using an average value and a standard deviation value of data in a predetermined interval before the estimated interval data of the pulse wave signal,

the body motion signal presumes body motion amount time-series data exceeding a prescribed threshold, the body motion amount time-series data calculates Z-Score of the body motion signal using an average value and a standard deviation value of data within a certain interval before,

and determining, from the Z-Score of the estimated interval data of the calculated pulse wave signal and the Z-Score of the calculated body motion signal, that the sleep state and the sleep stage of the body conform to one of a rapid eye movement sleep state, a deep sleep state, a light sleep state, and an arousal state.

Further, the circulatory organ function evaluation unit may evaluate the circulatory organ function of the body by performing approximate linear elimination on the pulse wave signal of the body in the deep sleep state and performing fluctuation analysis.

Further, in the circulatory organ function evaluation unit, the respiratory signal of the body in the deep sleep state is extracted, an average respiratory cycle is calculated, and approximate linear elimination and fluctuation analysis are performed for each integral multiple of the average respiratory cycle.

Further, in the circulatory organ function evaluation unit, the chaotic analysis is performed on the pulse wave signal of the body in the deep sleep state, and the circulatory organ function of the body is evaluated based on the lyapunov index.

Further, in the circulatory organ function evaluation unit, the delay time set in the chaotic analysis is an average period of the respiratory signal.

Further, in the brain control function evaluation unit, a fluctuation analysis is performed on the body motion signal of the body in the rapid-eye movement sleep state, and the brain control function of the body is evaluated based on a rocking trajectory of a pseudo center of gravity.

Further, in the brain control function evaluation unit, the delay time set in the fluctuation analysis is an average period of the respiration signal.

Further, in the circulatory organ function evaluation unit, the pulse wave signal of the body in the deep sleep state occurring at the initial stage in the deep sleep state is extracted, and the circulatory organ function of the body is evaluated.

Further, in the brain control function evaluation unit, a body movement signal of the body in a quick-action eye sleep state occurring at a last stage in the quick-action eye sleep state is extracted, and a brain control function of the body is evaluated.

Further, the device is also provided with a gravity center detection unit for detecting the gravity center of the body.

The utility model discloses a following another technical scheme realizes:

a physical function examination method comprising:

a signal separation step of separating a body information signal detected by a detection unit for detecting a body information signal of a body into a pulse wave signal, a respiration signal, and a body movement signal;

a sleep state and stage determination step of determining that the sleep state of the body corresponds to one of a quick-action eye sleep state, a deep sleep state, a light sleep state, and an arousal state, based on the pulse wave signal and the body movement signal;

a circulatory organ function evaluation step of extracting a pulse wave signal of the body in the deep sleep state and evaluating a circulatory organ function of the body;

and a brain control function evaluation step of extracting the body movement signal of the body in the rapid eye movement sleep state and evaluating the brain control function of the body.

The utility model discloses a following another technical scheme realizes:

a physical function examination system which causes a computer to execute:

a signal separation process of separating a body information signal detected by a detection unit for detecting a body information signal of a body into a pulse wave signal, a respiration signal, and a body movement signal;

a sleep state and stage determination process of determining that a sleep state of the body conforms to one of a quick-action eye sleep state, a deep sleep state, a light sleep state, and an arousal state, based on the pulse wave signal and the body motion signal;

a circulatory organ function evaluation process of extracting a pulse wave signal of the body in the deep sleep state and evaluating a circulatory organ function of the body;

and a brain control function evaluation process of extracting the body movement signal of the body in the quick-action eye sleep state and evaluating the brain control function of the body.

Compared with the prior art, the utility model, according to the utility model provides a physical function inspection device, physical function inspection method and system can realize following effect.

(1) Since the pulse wave signal of the body in the deep sleep state is extracted and the function of the circulatory organs of the body is evaluated, the body is optimally rested during sleep, particularly during deep sleep, sympathetic activity is suppressed to the maximum extent, parasympathetic activity is dominant, and in such a state, conscious activity and internal disturbance are little, and a true state of relaxation that is subconsciously stable is realized, so that body fluctuations can be examined with high accuracy. In addition to the state examination, the abnormality of the functional state of the body can be predicted by detecting the fluctuation of the body and finding the abnormality of the fluctuation before the organic abnormality.

(2) The brain control function of the body is evaluated by extracting a body movement signal of the body in a rapid eye movement sleep state, so that the brain is in a state close to arousal during a steady sleep and controls the movement of the body, and therefore, by accurately checking the abnormality of the brain control center fluctuation, the brain control dysfunction and the deterioration of the subconscious control function can be detected.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:

fig. 1 is a block diagram showing a configuration of a physical function test apparatus according to a first embodiment of the present invention.

Fig. 2 is a flowchart for explaining the operation of the physical function test device and the physical function test method according to the first embodiment of the present invention.

Fig. 3 is a graph showing the separation of the body information signal at-night into the pulse wave signal, the respiration signal, and the body movement signal by the signal separation means, with the horizontal axis representing time (minutes) and the vertical axis representing the signal amplitude value (arbitrary unit).

In fig. 4, (a) is a graph showing time-series data of an electrocardiogram at R-R intervals, and (B) is a graph showing time-series data of a pulse wave signal at P-P intervals.

Fig. 5 is a graph showing a case where a fixed data section W is selected from the graph of the time-series data of the P-P interval.

FIG. 6 is a graph showing a time-series change of Z-score (dimensionless).

Fig. 7 is a graph showing the result of determination of the sleep state of a certain body.

In fig. 8, (a) is a graph showing a pulse wave signal of a healthy person in an initial deep sleep stage, (B) is a graph showing a respiration signal thereof, (C) is a graph showing a pulse wave signal of an old myocardial infarction patient in an initial deep sleep stage, and (D) is a graph showing a respiration signal thereof.

Fig. 9 is a graph for explaining the approximate straight line elimination-fluctuation analysis method.

Fig. 10 is a graph showing the slope1 of the short-time region obtained by the DFA calculation and the slope2 of the long-time region obtained by the DFA calculation, with the slope1 as the fluctuation index 1 and the slope2 as the fluctuation index 2.

Fig. 11 shows the results of fluctuation analysis of time-series data of 3-minute P-P intervals obtained by modifying DFA for healthy persons and patients, wherein (a) shows the fluctuation index 1 of both, i.e., the average of slope1 values, (B) shows the respective distribution values, and (C) shows the groups of circulatory organ dysfunctions.

Fig. 12 shows a graph (a) of time-series data for explaining the outline of the takinson embedding in the chaos analysis and a graph (B) of attractors of a trajectory sequentially drawn in a three-dimensional state space.

Fig. 13 is an explanatory diagram showing a concept of the lyapunov exponent calculation algorithm in a three-dimensional state.

Fig. 14 is an explanatory diagram showing the concept of the lyapunov exponent calculation algorithm using three circles of hyper-sphere radius in a three-dimensional state.

Fig. 15 shows a graph of attractors showing the fluctuation degree of a healthy person having a lyapunov index of 4.31, and a graph of attractors showing the fluctuation degree of a myocardial infarction patient having a lyapunov index of 2.55.

Fig. 16 is an explanatory diagram of a method of constructing a two-dimensional attractor by the ta kens theorem for time-series data of extracted body motion signals.

In fig. 17, (a) is a graph showing time-series data of a body motion signal extracted from a healthy person when the eyes are rapidly moving to sleep, and (B) is a graph showing a pseudo center-of-gravity swing trajectory thereof.

In fig. 18, (a) is a graph showing time-series data of body motion signals extracted when a person having a tendency to get a restless depression quickly sleeps, and (B) is a graph showing a pseudo center of gravity swing trajectory thereof.

Fig. 19 shows a bed used in a physical function test apparatus according to a second embodiment of the present invention, in which (a) is a bottom view thereof and (B) is a side view thereof.

In fig. 20, (a) is a graph showing changes in time-series data of barycentric coordinates of a sleeping body obtained using a barycentric detection sensor, and (b) is a graph showing a rocking locus of the barycentric of the sleeping body.

Reference numbers and corresponding part names in the drawings:

1: physical function inspection device

2: body part

3: detection unit

4: control unit

5: storage unit

6: output unit

7: communication unit

8: bed

9: signal separation unit

10: sleep state and stage determination unit

11: circulatory organ function evaluation unit

12: brain control function evaluation unit

13: display unit

14: printing unit

15: and (5) programming.

Detailed Description

To make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the following examples and drawings, and the exemplary embodiments and descriptions thereof of the present invention are only used for explaining the present invention, and are not intended as limitations of the present invention.

(Structure of physical Performance test apparatus in the first embodiment of the present invention)

As shown in fig. 1, a physical function test device 1 according to a first embodiment of the present invention includes: a detection unit 3 for detecting a body information signal of the body 2; a control unit 4 for performing signal processing on the body information signal detected by the detection unit 3, determining a sleep state and a sleep stage (sleep stage), and evaluating a body function; a storage unit 5; an output unit 6; a communication unit 7.

The detection unit 3 is a piezoelectric sensor that is in contact with a part of the body 2 directly or via clothing, and is provided on a bed 8 for the body 2 to sleep, for example.

The control unit 4 includes: a signal separation unit 9 for separating the body information signal detected by the detection unit 3 into a pulse wave signal, a respiration signal, and a body movement signal; a sleep state and stage determination unit 10 for determining which of a fast-eye sleep state, a deep sleep state, a light sleep state, and an awake state the sleep state and the sleep stage of the body 2 conform to based on the pulse wave signal and the body movement signal; a circulatory organ function evaluation unit 11 for extracting a pulse wave signal of the body 2 in a deep sleep state and evaluating a circulatory organ function of the body 2; a brain control function evaluation unit 12 for extracting a body movement signal of the body 2 in the quick-action eye sleep state and evaluating a brain control function of the body 2.

The storage unit 5 stores various data and is provided with a database and the like.

The output unit 6 outputs various data, has a display unit 13 such as a display screen, a display, etc., and is used for displaying various data; and a printing unit 14 such as a printer for printing various data.

The communication unit 7 transmits and receives various data, and is connected to a communication network such as the Internet (a data transmission network based on TCP/IP (transmission control Protocol/Internet Protocol)) and a lan (local area network), for example, a modem, a terminal adapter, a router, and a dsu (digital Service unit).

Further, the program 15 causes the computer to execute the control processing of the control unit 4 of the physical function test apparatus according to the first embodiment of the present invention. The program 15 may be recorded on a recording medium such as a magnetic disk, a CD-ROM, or a semiconductor memory, or may be downloaded from a communication network.

(operation of the physical function test device and the physical function test method according to the first embodiment of the present invention) fig. 2 is a flowchart for explaining the operation of the physical function test device and the physical function test method according to the first embodiment of the present invention.

First, the body information signal is detected by the detection unit 3 (step S1).

Next, the body information signal detected by the detection unit 3 is separated into a pulse wave signal, a respiration signal, and a body movement signal by the signal separation unit 9 of the control unit 4 (step S2).

Next, the sleep state and stage determination means 10 determines which of the rapid eye movement sleep state, the deep sleep state, the light sleep state, and the wakefulness state the sleep state of the body 2 corresponds to based on the pulse wave signal and the body movement signal (step S3).

Next, the pulse wave signal of the body 2 in the deep sleep state is extracted (step S4), and the circulatory organ function of the body 2 is evaluated by the circulatory organ function evaluation unit 11 (step S5).

In addition, the body motion signal of the body 2 in the quick-action eye sleep state is extracted (step S6), and the brain control function of the body 2 is evaluated by the brain control function evaluation unit 12 (step S7).

Next, the processing performed by the control unit 4 will be described in detail.

(processing of step S2)

Fig. 3 is a graph showing the separation of the body-first-evening information signal into the pulse wave signal, the respiration signal, and the body movement signal by the signal separation means, where the horizontal axis represents time (minutes) and the vertical axis represents the amplitude value of the signal. In the figure, the lower part represents a pulse wave signal, the middle part represents a respiration signal, and the upper part represents a body motion signal.

As shown in fig. 3, the body information signal is divided into three signals, i.e., a pulse wave signal (center frequency 1Hz), a respiration signal (center frequency 0.3Hz), and a body movement signal (2Hz) in frequency bands around the center frequency thereof.

(processing of step S3)

Because the human brain is very developed, rapid eye movement sleep and non-rapid eye movement sleep are differentiated to respectively play different roles. The rapid eye movement sleep is derived from the word that the eyeball rotates in the bone under the closed eyelid, namely the rapid eye movement (the initial of rapid eye movement is REM). The brain during rapid eye movement sleep is in a state of being nearly awake, in which dreams occur frequently, and the body is exhausted although in a state of being dormant. The autonomic nerve functions such as pulse, respiration, blood pressure, etc. vary irregularly, and thus the body moves in a manner different from that upon waking.

The non-snap eye sleep refers to a sleep of the non-snap eye sleep, that is, a steady sleep including a shallow sleep state to a completely deep sleep state (divided into four stages based on brain waves). The muscle of the body keeps relatively tense, and the autonomic nerve functions such as pulse, respiration, blood pressure and the like are stable.

Currently, a medically approved sleep state determination method that has been clinically used is the Polysomnography (PSG) method. The polysomnography is a method of measuring brain waves, eye movements, and chin muscle electricity and determining them from their waveforms. However, in the method of measuring brain waves, myoelectricity, and the like, it is necessary to attach electrodes to the body, which is a great burden on the subject and makes measurement impossible at home. Therefore, the utility model discloses a substitute many leading sleep pictures through the simple and easy method conjecture sleep state's method.

(1) Inferring the P-P interval PPI from the pulse wave signal

Time-series data of a P-P interval corresponding to an R-R interval in an electrocardiogram are estimated from pulse wave signals.

In fig. 4, (a) is a graph showing time-series data of an electrocardiogram at R-R intervals, and (B) is a graph showing time-series data of a pulse wave signal at P-P intervals. Here, the R-R interval is the interval of the R wave of the electrocardiogram, and the P-P interval is the peak interval of the pulse wave signal.

First, in order to measure the peak and peak interval of the pulse wave, a Fast Fourier Transform (FFT) is used. The data of the pulse wave signal is subjected to FFT analysis, and the average frequency (MPF) and the power (P) are calculated.

MPF means an average value of frequency spectra of respective frequencies. The formula is as follows.

Wherein, P is the power value of the power spectrum, f is the frequency, fl and fh are the low frequency value and the high frequency value representing the frequency analysis interval. Using these, interval data PPI of the pulse wave signal is obtained by the following formula (2).

PPI 1/MPF equation (2)

(2) Z-score to predict PPI

Fig. 5 is a graph showing a case where a certain data interval W is selected in the graph of the time-series data of PPIs.

For PPI time-series data, using a certain data interval W before the PPI value, Z-score of PPI is calculated by formula (3) using Mean and standard deviation SD of data within interval W.

Z-score (PPI value-Mean)/SD

Formula (3)

By sliding the data interval W, Z-score time series data of PPIs are generated.

FIG. 6 is a graph showing the change of Z-score (dimensionless) in time series.

As shown in fig. 6, the Z-Score absorbs individual differences by mean and standard deviation, and has a value (dimensionless) within a range of approximately ± 3, and therefore, a general appropriate threshold value (value) can be set.

(3) Z-score to infer body movement

The body motion signal calculates the amount of body motion exceeding a predetermined threshold, and the Z-Score of the body motion signal obtained according to equation (2) is estimated using the Mean value and standard deviation SD of the data of the preceding fixed interval W. By sliding the data interval W, Z-score time series data of the body motion signal is generated.

(4) Determination of sleep stages (wakefulness, deep sleep, light sleep, quick eye movement sleep)

In the deep sleep state, the heart rate fluctuation, i.e., the interval data PPI of the pulse wave signals, has the smallest Z-Score, and at the same time, the body movement is small, i.e., the Z-Score of the body movement is the smallest.

In contrast, in the awake state, both are maximal.

The superficial sleep and the rapid eye movement sleep are positioned between the superficial sleep and the rapid eye movement sleep.

Therefore, if the threshold of the Z-Score of PPI is PT1, PT2, PT3, and the threshold of the Z-Score of body motion signal is MT1, MT2, MT3, then

When the Z-Score of PPI < PT1, and the Z-Score of body movement < T1, it is determined as a deep sleep stage.

When PT2> Z-Score > PT1 of PPI, and MT2 of body movement > Z-Score > MT1 of body movement, it is determined as a shallow sleep stage.

And when PT3> Z-Score > PT2 of PPI and MT3 of body movement > Z-Score > MT2 of body movement are simultaneously, judging the fast-moving eye sleep stage.

When the PPI Z-Score > PT3 and the body movement Z-Score > MT3 are the same, the PPI is judged to be wakeful.

Wherein PT1< PT2< PT3, MT1< MT2< MT 3. Thereby, it can be determined in which sleep stage or in which sleep stage the sleep state is.

The utility model discloses the specific threshold value of people's in-service use as follows:

PT1＝0.6、PT2＝1.2、PT3＝3；

MT1＝0.7、MT2＝1、MT3＝3

(these are merely examples and are not limiting).

(processing in steps S4 and S5)

(1) Signal extraction for deep sleep stages

The frequency band is divided, and the pulse wave signal (center frequency 1Hz) and the respiration signal (center frequency 0.3Hz) after separation are extracted. By the above means, the determination result of the sleep state as shown in fig. 7 is obtained. A number of deep sleep stages are obtained, the initial occurrence of which is indicated by the arrow, which is the most stable true state of relaxation. In addition, a plurality of snap-action eye sleep stages are obtained, the first occurring snap-action eye sleep stage being the sleep state closest to arousal, indicated by arrows.

In fig. 8, (a) is a graph showing a pulse wave signal extracted from the initial deep sleep stage of a healthy person, (B) is a graph showing a respiratory signal thereof, (C) is a graph showing a pulse wave signal extracted from the initial deep sleep stage of a patient with old myocardial infarction, and (D) is a graph showing a respiratory signal thereof.

(2) Analysis method of heartbeat fluctuation (improved DFA method)

From recent research results, heart rate data is subjected to a method of fractal analysis, i.e., approximate linear ablation-fluctuation analysis, i.e., dfa (detrended fluctuationanalysis) analysis that takes into account the mutual influence of heart and lung, whereby the decrease or disorder of physical functions of heart diseases and other diseases and circulatory organs is presumed.

From the pulse waves, interval PPI time-series data are set as X (1), X (2), X (3),.., X (n).

First, an average value of the whole is calculated. The average value is subtracted from each value of the time-series data and integrated to find y (k). According to equation (4), the time-series data is discrete data at each time, and therefore the integral is replaced by a sum.

Wherein, X (i): time-series data [ i ═ 1, …, N ], M: average of X (i).

Then, the time series y (k) after the integration is divided by the division time of the equal interval n, and the least square approximate straight line y is obtained in the division time_n(k) (local tendency). Removing y from y (k)_n(k) Trend and findThe square value is an average value, and a square root is obtained, and f (n) (mean square error) in this case is formula (5). The time scale of the divided time is changed with respect to the entire time scale, f (n) is calculated for each divided time, logn of the divided time is plotted on the horizontal axis, and logf (n) of the mean square error f (n) is plotted on the vertical axis.

In the plot of log n and logf (n) shown in fig. 10, the Slope of the straight line portion is the scale index Slope 1. The slope of the straight line obtained by the least squares method corresponds to the slope.

The utility model discloses a pulse wave interval PPI's fluctuation analysis precision, and the improvement precision method who adopts the DFA method of taking into account the influence that produces breathing in the improvement. Wherein Tr corresponding to the average breathing cycle is used.

When n in equation (5) is selected to be an integer multiple of Tr: when n is Tr,2Tr,3Tr,4Tr, …, f (n) of each division time of the formula (5) jumps at an integral multiple of each respiratory cycle, and the influence of respiration on the PPI fluctuation is reduced, so that a correct PPI fluctuation index Slope1 is obtained.

Through the Slope1, PPI time-series data x (i) are classified as follows.

0< Slope1< 0.5: inverse correlation

Slope1 ═ 0.5: uncorrelated, white noise

0.5< Slope1< 1.0: long range correlation

Slope1 ═ 1: 1/f fluctuation

Slope1> 1: collapse of the straight-line relationship between log n and logF (n)

Slope1 ═ 1.5: random step-over-step brown noise

Fig. 11 shows the results of fluctuation analysis of healthy subjects and those with reduced circulatory organ function using time-series data of 3-minute P-P intervals obtained by improving DFA, where (a) shows the mean value and standard deviation of Slope1 values for both, (B) shows the distribution values of the both, and (C) shows the group of healthy subjects and those with reduced circulatory organ function.

From this analysis, it was found that the Slope1 value was close to 1(1/f fluctuation) when the body was in a healthy state, and the population with reduced circulatory function such as atrial fibrillation was close to 0.5. The population with reduced function of hypertension or angina pectoris is distributed around 0.9, the population with reduced function of diabetes or myocardial infarction is distributed around 0.6-0.7, and the population with reduced function of atrial fibrillation is distributed around 0.5.

That is, in a healthy state, 1/f fluctuation is represented by Slope1 being 1, and as the degree of circulatory organ function deterioration increases, Slope1 gradually decreases, and is generally considered to approach 0.5, which is an uncorrelated white noise. That is, if the fluctuation (fluctuation) due to the autonomic nerve innervation of the pacemaker decreases, the responsiveness and the responsiveness to the rapid disturbance applied to the blood pressure decrease, and the circulatory organ function decreases.

If the Slope1 value increases to 1 or more, it is considered that the mind is in an unhealthy state. If the fluctuation is excessive, the state is a stressful state or a psychological state with poor mood.

(3) Chaos analysis of pulse wave signals

Fig. 12(a) is a graph showing time-series data for explaining the outline of the takens embedding theorem, and (B) is an attractor graph showing tracks sequentially drawn in a three-dimensional state space.

The takens theorem is a commonly used method in chaos analysis. The time-series data of the pulse wave signal is x (k) (0, 1,2,3 …).

When it is desired to recover attractors of m state variables, vectors x (i) ═ x (i), x (i + τ), x (i +2 τ), and … … x (i + m τ) are created using the delay time τ. For example, when the number of state variables is 3, x (i) ═ x (i), x (i + τ), x (i +2 τ) }.

Here, τ is a parameter and is called an embedding delay time. When the vector x (i) (i ═ 0,1,2, …, n) is plotted in this order in the three-dimensional state space (coordinate axes x (i), x (i + τ), and x (i +2 τ)), a trajectory whose shape is called an attractor can be obtained.

The delay time τ is important to select and the attractor of the state variable can be recovered by selecting the optimal time delay. The utility model discloses an optimum delay time tau is the average period of pulse wave signal. Thus, the attractor can be recovered, and the accuracy of the chaotic analysis result of the pulse wave signal is improved.

The lyapunov index is an index indicating the degree of signal fluctuation, and in an attractor drawing track, the distance between two adjacent tracks is a quantity indicating the degree of distance with the passage of time. When calculating the Lyapunov exponent, an approximate calculation method according to the Sano-Sawada method is used.

As shown in fig. 13, when microspheres (hyper spheres) having a radius ∈ are provided as an initial value in the three-dimensional chaotic power system, a substance that is initially spherical is once mapped, stretched in the e1 direction, and flattened in the e3 direction, resulting in an elliptical shape. When the logarithms of the expansion ratios per unit time in the directions e1, e2, and e3 are λ 1, λ 2, and λ 3, λ 1 is a first component, and hence is also referred to as a "first Lyapunov index" or a "maximum Lyapunov index", and is referred to as a Lyapunov index in the present invention (non-patent documents m.sano, y.sawada (1985) Measurement soft hand unapponov specific free time series, Physical Review Letters,55(10) pp 1082-1085).

In the present invention, in order to remove the ambient noise mixed into the signal, the following method is devised in order to improve the accuracy.

Fig. 14 is an explanatory diagram showing the concept of the lyapunov exponent calculation algorithm using three hyper-sphere radius circles in a three-dimensional state.

As shown in fig. 14, another hypersphere radius s3 is added, i.e., as a search condition for attractor neighboring points,

will exist at a hypersphere radius, e1, and,

exists within the hypersphere radius epsilon 2, and,

the points of the attractor existing within the hypersphere radius e3 are taken as neighboring points.

Wherein, the radius of the hyper-sphere 1 is 5 percent of the total size (radius) of the attractor,

when the hyper-spherical radius 2 is set to be 1.5 times the hyper-spherical radius 1,

when the radius of the hyper sphere 3 is 2 times of the radius of the hyper sphere 1,

the noise can be suppressed and the accuracy can be improved.

The reason for this is that it is difficult to make a good look,

1) can escape from the track of the bouncing hypersphere epsilon 2 and the hypersphere epsilon 3.

2) The trajectories of the different behaviors (noise) can be broken and the lyapunov exponent calculated.

In fig. 15, (a) is an attractor graph showing the fluctuation degree of a healthy person having a lyapunov index of 4.31, and (B) is an attractor graph showing the fluctuation degree of a myocardial infarction patient having a lyapunov index of 2.55.

The lyapunov index indicates softness of the body and a degree of body fluctuation, and a healthy person has a high lyapunov index even in a quiet state, and is very flexible, and a graph of a complicated attractor structure is shown in fig. 15 (a).

On the other hand, if the flexibility is gradually lost, the lyapunov index decreases, the physical function decreases, and the risk of circulatory dysfunction such as myocardial infarction is high, and in this case, fig. 15(B) shows a simple periodic attractor structure after narrowing.

(processing in steps S6 and S7)

The last snap-action eye sleep stage is closest to the arousal state in which the control function of the body activity can be examined optimally from the brain, and therefore, by analyzing the body motion fluctuation at that time, the brain control function can be examined with high accuracy. As a method thereof, a signal of a last rapid eye movement sleep stage is extracted during one night, and a potential body imbalance related to a brain control function is checked by performing a fluctuation analysis on a physical activity, so that a psychological disorder can be predicted.

(1) Signal extraction for snap-action eye sleep stages

A body movement signal is extracted from a fast-moving-eye sleep stage determined to occur last in the sleep stages.

(2) Fluctuation analysis of body motion signals

The fluctuation analysis of the body motion signal uses a similar method to the barycentric panning examination.

Fig. 16 is a graph illustrating points constituting a two-dimensional attractor by the takens theorem for time-series data of the extracted body motion signal.

As shown in fig. 16, the two-dimensional attractor is constituted by the extracted body motion signal when the snap eye sleeps by the tackens theorem. The attractor was used as a pseudo center of gravity swing trajectory, and a body center of gravity swing examination method was used while standing.

The utility model discloses an optimum delay time tau is respiratory signal's average period, can reduce respiratory influence, can constitute the two-dimensional attractor accurately.

In fig. 17, (a) is a graph showing time-series data of a body motion signal extracted from the sleep stage of the last snap-action eye of a healthy person, and (B) is a graph showing the pseudo center of gravity swing trajectory thereof.

In fig. 18, (a) is a graph showing time-series data of body motion signals extracted from the last rapid-moving eye sleep stage of a person having a tendency to fidgetiness, and (B) is a graph showing a pseudo-center-of-gravity panning trajectory thereof.

As shown in fig. 17, while a person with a healthy physical function can control the pseudo center-of-gravity swing trajectory with a small trajectory area, as shown in fig. 18, a person with a tendency to be fussy or unhealthy has a large trajectory area due to a reduced brain control function.

(second embodiment)

As shown in fig. 19, in the physical function test apparatus according to the second embodiment of the present invention, a center of gravity detection sensor 21 (weight sensor) for detecting the center of gravity of the body 2 is attached to the lower portion of the leg portion 8a of the bed 8 for the body 2 to sleep.

The length and width of the bed 8 are set to L and D. The center point is the origin (0,0) of the plane coordinates (x, y) of the center of gravity.

The weight sensors on the 4 legs 8a of the bed 8 measure the weight values P1, P2, P3, P4 of the 4 legs, whereby,

during sleep, the x coordinate value of the body gravity center in the short axis direction of the bed is [ (P1+ P2) L/2- (P3+ P4) L/2 ]/Pt;

during sleep, the y coordinate value of the center of gravity of the body in the long axis direction of the bed is [ (P1+ P3) D/2- (P2+ P4) D/2 ]/Pt;

wherein the total weight Pt is P1+ P2+ P3+ P4,

the time-varying data of the x-coordinate value of the center of gravity and the y-coordinate value of the center of gravity are used as the body information signal.

The three body signals are separated according to the respective frequency bands by a filtering method of signal separation. Namely, a pulse wave signal (center frequency of 1Hz), a respiration signal (center frequency of 0.3Hz), and a body motion signal (center frequency of 2 Hz). Using these separation signals, sleep stages were determined by the Z-Score method. As in example 1, by performing fluctuation analysis and chaos analysis on signals in the deep sleep stage and the rapid eye movement sleep stage, a decrease in physical function can be checked.

Further, in the sleep of the body 2, the center of gravity of the body appearing in the horizontal posture is referred to as the horizontal center of gravity of the two-dimensional projection, and the locus of the center of gravity thereof is a graph in which the x-coordinate value and the y-coordinate value of the center of gravity change with time.

Since the pattern of the x-coordinate value of the center of gravity and the y-coordinate value of the center of gravity at each time is the same as that of the two-dimensional attractor constituted by the center of gravity trajectory, the peripheral area evaluation index around the trajectory that can evaluate the control function of the center of gravity swing is calculated by the formula (6).

Area of outer circumference env:

env (max (x) -min (x)) ＊ (max (y) -min (y)) formula (6)

The range of motion of the center of gravity is a rectangle such as a full circle.

The reduction in brain control function can be detected by area analysis of the center of gravity swing trajectory.

In fig. 20, (a) is a graph showing changes in time-series data of barycentric coordinates obtained using a weight sensor, and (B) is a graph showing a rocking trajectory when the body barycenter is set as a horizontal barycenter of a two-dimensional projection.

This method can be used not only for pseudo-center-of-gravity panning, but also for trajectory analysis of the body horizontal center of gravity obtained from the center-of-gravity detection sensor 20 attached to the bed 8 in sleep, and also as a means for detecting a decrease in the body control function or the brain control function.

According to the utility model provides a physical fitness examination device and system can realize following effect.

(1) Since the pulse wave signal of the body in the deep sleep state is extracted and the function of the circulatory organs of the body is evaluated, the body is in the most stable relaxed state during sleep, particularly in deep sleep, the sympathetic activity is suppressed to the maximum extent, the parasympathetic activity is dominant, and in such a state, the conscious activity and internal disturbance are less, and the true relaxed state is realized under subconscious, so that there are few irregularities, and the fluctuation can be examined with high accuracy. In addition, by examining the fluctuation of the body in addition to the state examination, it is possible to find the abnormality of the fluctuation before the organic abnormality, thereby making it possible to predict the decrease in the circulatory function.

(2) The body movement signal in the rapid-moving eye sleep state is extracted and the body control function or the brain control function is evaluated, so that the brain is in a state close to arousal during the steady sleep while the rapid-moving eye sleeps, and the brain control dysfunction or the deterioration of the subconscious control function can be detected in an unconscious state during the sleep by accurately checking the function of controlling the body activity.

The present invention is not limited to the above-described embodiments, and various modifications can be made within the scope of the technical matters described in the claims.

The above-mentioned embodiments, further detailed description of the objects, technical solutions and advantages of the present invention, it should be understood that the above description is only the embodiments of the present invention, and is not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A physical function examination apparatus, comprising:

a detection unit for detecting a body information signal of a body;

2. The physical function test device according to claim 1, wherein the detection unit is a piezoelectric sensor that is in contact with a part of the body directly or via clothing.

3. The physical function test apparatus according to claim 2, wherein the sleep state and stage determination means estimates estimated interval data of the pulse wave signal by performing fast Fourier transform on the pulse wave signal, calculates Z-Score of the estimated interval data of the pulse wave signal using an average value and a standard deviation value of data in a predetermined interval before the estimated interval data of the pulse wave signal,

4. The physical function test apparatus according to claim 3, wherein the circulatory organ function evaluation means evaluates the circulatory organ function of the body by performing approximate linear elimination and fluctuation analysis of the pulse wave signal of the body in the deep sleep state.

5. The physical function test apparatus according to claim 4, wherein the circulatory organ function evaluation means extracts the respiratory signal of the body in the deep sleep state, calculates an average respiratory cycle, and performs approximate linear elimination and fluctuation analysis for each integral multiple of the average respiratory cycle.

6. The physical function examination apparatus according to any one of claims 1 to 5, wherein in the circulatory organ function evaluation unit, a chaotic analysis is performed on the pulse wave signal of the body in the deep sleep state, and circulatory organ function of the body is evaluated based on a Lyapunov index.

7. The physical function examination apparatus according to claim 6, wherein in the circulatory organ function evaluation unit, the delay time set in the chaotic analysis is an average period of the respiration signal.

8. The physical function test apparatus according to claim 7, wherein the brain control function evaluation unit performs fluctuation analysis on the body motion signal of the body in the snap-eye sleep state, and evaluates the brain control function of the body based on a rocking trajectory of a pseudo center of gravity.

9. The physical function examination apparatus according to claim 8, wherein in the brain control function evaluation unit, a delay time set in the fluctuation analysis is an average period of the respiration signal.

10. The physical function examination apparatus according to claim 9, wherein in the circulatory organ function evaluation unit, a pulse wave signal of the body in a deep sleep state occurring at an initial stage in the deep sleep state is extracted, and a circulatory organ function of the body is evaluated.

11. The physical function examination apparatus according to claim 10, wherein in the brain control function evaluation unit, the body motion signal of the body in a rapid-moving-eye sleep state occurring at the last stage in the rapid-moving-eye sleep state is extracted, and the brain control function of the body is evaluated.

12. The physical function test apparatus according to claim 11, further comprising a center of gravity detection unit for detecting a center of gravity of the body.