CN105193431A - Device for analyzing mental stress state of human body - Google Patents

Device for analyzing mental stress state of human body Download PDF

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CN105193431A
CN105193431A CN201510552145.7A CN201510552145A CN105193431A CN 105193431 A CN105193431 A CN 105193431A CN 201510552145 A CN201510552145 A CN 201510552145A CN 105193431 A CN105193431 A CN 105193431A
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interval
human
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CN105193431B (en
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杨静
刘常春
王海彬
张明
于洪良
刘奇
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杨静
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Abstract

A device for analyzing the mental stress state of a human body comprises a sensor module, a physiological signal acquisition module, an embedded system platform and a wireless communication module, wherein the sensor module is connected with the physiological signal acquisition module; the physiological signal acquisition module is connected with the embedded system platform; the embedded system platform is connected with the wireless communication module; the wireless communication module is wirelessly connected with a hand-held terminal; data are transmitted between the embedded system platform and the individual hand-held terminal, the measurement process is controlled on the individual hand-held terminal, and the measurement result is displayed on the individual hand-held terminal and can be shared through a network. The device analyzes the mental state of a subject by detecting electrocardiosignals and pulse signals, the measurement process is noninvasive and nondestructive, the mental state of the human body can be evaluated conveniently, the measurement safety for the human body is guaranteed, adverse effects caused by power frequency interference on signal acquisition can be avoided, and the portability of the device is guaranteed in a data exchange manner of wireless communication.

Description

A kind of human body stress state analysis device
Technical field
The present invention relates to a kind of device for human body stress state analysis, belong to human body stress state analysis technical field.
Background technology
The commonly encountered diseases of modern society is mental illness, mental pressure and stress, and highly developed industrial system has brought abundant material, creates great riches.But we everyone therefrom benefit while, joyless emotion also if the flood spread unchecked is along with the creation of wealth, sweeps over to us.Pressure almost shrouds with it everyone, and in the modern life, everyone experiences stress to some extent.Show according to hygiene department's investigation in 2013, existing more than the 100000000 all kinds of mentally disturbeds of China, wherein nearly 1,600 ten thousand people of major psychosis people .chinese Academy of Sciences's psychological research according to survey data statistical analysis to 31875 different occupation personages, twenty or thirty year youngster be in China each age group, experience the maximum crowd of pressure.
Mental pressure is the main cause causing subhealth state.Pressure is excessive, it is healthy too much to damage.Mental pressure can not discharge in time, will cause mental disease.Pressure is crossed conference and is caused health subhealth state, can cause direct morbidity under serious conditions, and wherein modal disease is a lot, as pressure can cause infertile, cause a lot of people's chronic insomnia, cause a lot of youngster to suffer from gastropathy etc., pressure is our Health Killer maximum at one's side.
But in investigation, the people more than 40% does not take any measure, and oneself is grined and bear it.Sociologist points out, present people's pressure is all very large, and a lot of people does not know how to mediate.Chinese traditional personality compares to be born patiently, is accustomed to thing to be constrained at heart, and people still have misgivings to marriage counselor, even if know that linking up with people is most effective method, he is also unwilling to solve.
For present this situation, be badly in need of a kind of can visual inspection and analyze the instrument of the human body mental status and degree of fatigue, can detect the stress of experimenter, enable the mental status of doctor to patient have one to go deep into and clear and definite understanding, and give and corresponding suggestion according to patient mental's state.
Summary of the invention
The present invention is directed to existing situation, provide a kind of measuring process noinvasive can't harm, can easily to the human body stress state analysis device that the human body mental status is assessed.
Human body stress state analysis device of the present invention, by the following technical solutions:
This device comprises sensor assembly, physiological signal collection module, embedded system platform and wireless communication module;
Sensor assembly, comprise right electrocardiogram acquisition electrode, left electrocardiogram acquisition electrode and pulse wave sensor, all with physiological signal collection model calling, the photoplethysmographic signal that electrocardiosignal and finger for gathering human body are held, and by Signal transmissions to physiological signal collection module;
Physiological signal collection module, be connected with embedded system platform, comprise ECG detecting circuit and pulse wave testing circuit, the electrocardiogram acquisition Electrode connection in ECG detecting circuit and sensor assembly, pulse wave testing circuit is connected with the pulse wave sensor in sensor assembly; Analog-digital conversion is carried out to the simulation physiological signal that testing circuit produces, and transfers to embedded system platform and process;
Wireless communication module is connected with embedded system platform, and wirelessly between embedded system platform and personal hand-held terminal (mobile phone, panel computer, PDA etc.), transmits data;
Embedded system platform, is connected with wireless communication module, for analyzing the data of physiological signal collection module input, reduction electrocardio and pulse wave signal, carry out stress analysis, complete following operation:
(1) resolve by the data of physiological signal collection module transfer, reduction electrocardiosignal and pulse wave signal, carry out filtering and amplification to signal, obtain human body electrocardio figure wave sequence X (K) and pulse waveform sequence Y (k);
(2) analyze pulse waveform sequence Y (K), utilize each characteristic point on this waveform (as ascending branch starting point, peak point or incisura) to calculate pulse frequency PR;
(3) analyze ecg wave form sequence X (K), identify the R ripple in ecg wave form, obtain the RR interval of heart rate and electrocardiosignal, RR interval is analyzed, obtain heart rate variability relevant time domain index, frequency-domain index and nonlinear analysis index; Wherein:
1. time domain index comprises: R-R interval standard deviation (SDNN), adjacent R-R interval difference root-mean-square value (RMSSD) and adjacent normal R-R interval are more than the percentage ratio (pNN of 50ms 50);
The computing formula of R-R interval standard deviation (SDNN) is:
S D N N = Σ i = 1 N ( RR i - m e a n R R ) 2 N ,
Wherein N is the sum of normal heartbeat in omnidistance record, RR ibe i-th R-R interval, meanRR is the meansigma methods of the R-R interval of N number of heartbeat.
The computing formula of adjacent R-R interval difference root-mean-square value (RMSSD) is:
R M S S D = Σ i = 1 N ( RR i + 1 - RR i ) 2 N ,
Wherein N is the sum of normal heartbeat in omnidistance record, RR i+1, RR ifor the length of adjacent two sinus cardiac cycles.
Adjacent normal R-R interval, is more than the percentage ratio (pNN of 50ms 50) computing formula be:
pNN 50 = NN 50 N N × 100 ,
Wherein NN 50in omnidistance record, adjacent R-R interval difference is greater than the number of 50ms, and NN is R-R interval sum.
2. frequency-domain index comprises: gross energy TP, low-frequency component LF, radio-frequency component HF and low frequency/high frequency ratio (LF/HFration);
The computational process of frequency-domain index is:
To RR interval series analysis of spectrum, the Energy distribution situation of quantitative description HRV signal (heart rate variability signals), obtains the power spectral density plot of AR model (a kind of linear prediction);
Low-frequency component LF is the integrated value of power spectral density plot in 0.04-0.15Hz frequency band;
Radio-frequency component HF is the integrated value of power spectral density plot in 0.15-0.4Hz frequency band;
Gross energy TP: the integrated value of power spectral density plot in 0.04-0.4Hz frequency band, equals the summation of low-frequency component LF and radio-frequency component HF;
Low frequency/high frequency ratio (LF/HFration): the ratio of low-frequency component LF and radio-frequency component HF.
To the method for RR interval series analysis of spectrum be autoregression AR model Modern spectral estimation or by classic Power estimation method (as period map method and quickly diaphragm filter (FFT).
The power spectral density plot power spectral density plot of AR model can be calculated by Burg algorithm, Marple algorithm or LevinsonDubin algorithm.
3. nonlinear indicator comprises: vector length index VLI and vectorial angle index VAI;
The computational process of nonlinear indicator is as follows:
With the previous R-R interval length RR of adjacent two sinus cardiac cycles ifor abscissa, a later R-R interval RR i+1for vertical coordinate is drawn a bit, so continue, in regular hour section, be depicted as by each loose scattergram formed, obtain according to this scattergram:
Vector angle index: wherein N is the number of loose point, and a (i) is the vectorial angle angle value of point loose in scatterplot;
Vector length index: wherein N is the number of loose point, and b (i) and b (j) is the vector length angle value of point loose in scatterplot.
(4) analyze heart rate variability metrics R-R interval standard deviation (SDNN), adjacent R-R interval difference root-mean-square value (RMSSD) and radio-frequency component HF and assess human body autonomic nervous system active A NSA, assess autonomic nervous system balance ANSE according to low frequency/high frequency ratio (LF/HFration);
The computing formula of ANSA and ANSE is:
ANSA=k 1* SDNN+k 2* RMSSD+k 3* HF+k 4, wherein k ifor power layer coefficients;
A N S E = k 1 L F H F + k 2 , Wherein k ifor power layer coefficients;
Each power layer coefficients in each index is determined by clinical trial, value 0-1, and the proprietary rights layer coefficients sum in each index is 1.
(5) analyze the stress state of experimenter, comprise following four indexs: emotional state index, anti-pressure ability index, physical fatigue exponential sum pressure state index;
The computing formula of four indexes is as follows:
Emotional state index: M S I = k 1 * L F + k 2 * H F + k 3 L F H F + k 4 , Wherein k ifor power layer coefficients;
Anti-pressure ability index: RPAI=k 1* TP+k 2* RMSSD+k 3* pNN 50+ k 4, wherein k ifor power layer coefficients;
Physical fatigue index: BFI=k 1* VLI+k 2* LF+k 3, wherein k ifor power layer coefficients;
Pressure state index: PSI=k 1* VAI+k 2* HF+k 3, wherein k ifor power layer coefficients;
Each power layer coefficients in each index is determined by clinical trial, value 0-1, and the proprietary rights layer coefficients sum in each index is 1.
Wherein index meaning is as follows:
Emotional state index, whether the spiritual emotion of the current experimenter referred to anxiety, passiveness or normal;
Anti-pressure ability index, (such as this experimenter's pressure ratio is larger now for the anti-pressure ability of the experimenter referred to, if but his anti-pressure ability is stronger, namely health voltage endurance capability is stronger, can not be worn down by pressure in such health short time, otherwise health easily goes wrong);
Physical fatigue index, refers to non-spiritual pressure, refers to the degree of fatigue (this fatigue that such as driver's long-duration driving produces) on health.
Pressure state index, be current residing stress state, generally have three kinds of states: pressure is little, without pressure (normally), pressure is large;
(6) according to the stress index of correlation obtained, the mental status of measured is assessed;
(7) provide measured according to the pulse frequency measured and heart rate and whether suffer from atrial fibrillation or the premature beat that takes place frequently prompting.
It should be noted that, the calculating of indexs all is above all obtained by electrocardiosignal RR interval series, the present invention is not limited thereto, also can analyze, as starting point interval series, peak point interval series and incisura point interval series with the interval series that each characteristic point of pulse wave is formed.
Embedded system platform is also connected with display screen, shows during with logarithm factually.
The present invention is by detecting electrocardiosignal and pulse signal, analyze measured's mental status, measuring process noinvasive can't harm, can assess the human body mental status easily, ensure that the safety of measuring human body, the adverse effect that Hz noise brings to signals collecting can be avoided, be ensure that the portability of device by the data exchange ways of wireless telecommunications.
Accompanying drawing explanation
Fig. 1 is the structural representation of human body stress state analysis device of the present invention.
Fig. 2 is human body signal (comprising electrocardiosignal and the photoplethysmographic signal) schematic diagram measured by human body stress status analysis device of the present invention.
Fig. 3 is the overhaul flow chart of human body stress status analysis device of the present invention.
Fig. 4 is AR power spectral density plot figure.
Fig. 5 is by the loose scattergram formed.
In figure: 1, right side electrocardiogram acquisition electrode, 2, left side electrocardiogram acquisition electrode, 3, pulse wave sensor, 4, physiological signal collection module, 5, touch display screen, 6, embedded system platform, 7, wireless communication module, 8, human body stress status analysis device, 9, handheld terminal.
Detailed description of the invention
As shown in Figure 1, human body stress state analysis device 8 of the present invention, comprises right electrocardiogram acquisition electrode 1, left electrocardiogram acquisition electrode 2, pulse wave sensor 3, physiological signal collection module 4, touch display screen 5, embedded system platform 6 and wireless communication module 7.Right electrocardiogram acquisition electrode 1, left electrocardiogram acquisition electrode 2 are all connected with the input of physiological signal collection module 4 with pulse wave sensor 3.The outfan of physiological signal collection module 4 connects embedded system platform 6, physiological signal collection module 4 comprises the ECG detecting circuit and the pulse wave testing circuit being connected pulse wave sensor that connect electrocardiogram acquisition electrode (right electrocardiogram acquisition electrode 1 and left electrocardiogram acquisition electrode 2), and analog-digital conversion is carried out to the analogue signal that testing circuit produces, and transfer to embedded system platform 6 and process.Touch display screen 5 is connected with embedded system platform 6, and embedded system platform 6 is connected with wireless communication module 7.Wireless communication module 7 is wirelessly connected with handheld terminal 9, data are transmitted between embedded system platform and personal hand-held terminal (mobile phone, panel computer, PDA etc.), control survey process and display measurement result in personal hand-held terminal, and can network sharing measurement result be passed through.
Right electrocardiogram acquisition electrode 1, left electrocardiogram acquisition electrode 2 and pulse wave sensor 3 are affixed on inside the right hand of human body and the wrist of left hand respectively, gather the I of human body and to lead electrocardiosignal.Pulse wave sensor 3 gathers photoplethysmographic signal from the left hand of human body or the forefinger finger tip of the right hand.Physiological signal collection module 4 receives the signal of right electrocardiogram acquisition electrode 1, left electrocardiogram acquisition electrode 2 and pulse wave sensor 3 and amplifies and filtering measuring the signal obtained, and two kinds of signals are sent to embedded system platform 6 after analog-digital conversion.The human ecg signal measured and photoplethysmographic signal are as shown in Figure 2.
Embedded system platform 6 analyzes the data that physiological signal collection module 4 is imported into, carries out stress analysis, and analysis result transfers to personal hand-held terminal 9 by wireless communication module 7.Wireless communication module 7 and personal hand-held terminal 9 carry out communication, send data to personal hand-held terminal 9, and receive control command.
The major function of embedded system platform 6 is data that analysis physiological signal collection module is imported into, and reduction electrocardio and pulse wave signal, carry out stress analysis, as shown in Figure 3, detailed process is as described below.
(1) data being transferred to software by measuring terminals are resolved, reduction electrocardiosignal and pulse wave signal, software filtering and amplification are carried out to signal, obtain human body electrocardio figure wave sequence X (K) and pulse waveform sequence Y (k), and two kinds of waveforms are shown in real time.
(2) analyze pulse waveform sequence Y (K), utilize each characteristic point on this waveform (as ascending branch starting point, peak point or incisura) to calculate pulse frequency PR.Calculate by existing conventional method.
(3) analyze ecg wave form sequence X (K), identify the R ripple in ecg wave form, obtain the RR interval of heart rate and electrocardiosignal, RR interval is analyzed, obtain heart rate variability relevant time domain index, frequency-domain index and nonlinear analysis index; Wherein:
1. time domain index comprises: R-R interval standard deviation (SDNN), adjacent R-R interval difference root-mean-square value (RMSSD) and adjacent normal R-R interval are more than the percentage ratio (pNN of 50ms 50);
The computing formula of R-R interval standard deviation (SDNN) is:
S D N N = Σ i = 1 N ( RR i - m e a n R R ) 2 N ,
Wherein N is the sum of normal heartbeat in omnidistance record, RR ibe i-th R-R interval, meanRR is the meansigma methods of the R-R interval of N number of heartbeat.
The computing formula of adjacent R-R interval difference root-mean-square value (RMSSD) is:
R M S S D = Σ i = 1 N ( RR i + 1 - RR i ) 2 N ,
Wherein N is the sum of normal heartbeat in omnidistance record, RR i+1, RR ifor the length of adjacent two sinus cardiac cycles.
Adjacent normal R-R interval, is more than the percentage ratio (pNN of 50ms 50) computing formula be:
pNN 50 = NN 50 N N × 100
Wherein NN 50in omnidistance record, adjacent R-R interval difference is greater than the number of 50ms, and NN is R-R interval sum.
2. frequency-domain index comprises: gross energy TP, low-frequency component LF, radio-frequency component HF and low frequency/high frequency ratio (LF/HFration), its computing formula are:
RR interval series is carried out to the Modern spectral estimation of autoregression AR model, the Energy distribution situation of quantitative description HRV signal (heart rate variability signals).AR power spectral density plot f (x) is obtained by Burg algorithm, as shown in Figure 4:
Low-frequency component LF: the integrated value of power spectral density plot in 0.04-0.15Hz frequency band;
Radio-frequency component HF: the integrated value of power spectral density plot in 0.15-0.4Hz frequency band;
Gross energy TP: the integrated value of power spectral density plot in 0.04-0.40Hz frequency band, equals the summation of low-frequency component LF and radio-frequency component HF;
Low frequency/high frequency ratio (LF/HFration): the ratio of low-frequency component LF and radio-frequency component HF.
It should be noted that, the spectral analysis method of RR interval series is not limited to the Modern spectral estimation of autoregression AR model, can also by classic Power estimation method, as period map method and quickly diaphragm filter (FFT), wherein the power spectral density plot of AR model calculates and is not limited to Burg algorithm, can also calculate with Marple algorithm and LevinsonDubin algorithm.
3. nonlinear indicator comprises: vector length index VLI and vectorial angle index VAI, and its computing formula is as follows:
With the previous R-R interval length RR of adjacent two sinus cardiac cycles ifor abscissa, a later R-R interval RR i+1for vertical coordinate is drawn a bit, so continue, in regular hour section, be depicted as by the loose scattergram formed, and reveal certain morphological characteristic because of the difference table of RR interval, as shown in Figure 5:
Vector angle index: wherein N is the number of loose point, and a (i) is the vectorial angle angle value of point loose in scatterplot;
Vector length index: wherein N is the number of loose point, and b (i) and b (j) is the vector length angle value of point loose in scatterplot.
(4) analyze heart rate variability metrics R-R interval standard deviation (SDNN), adjacent R-R interval difference root-mean-square value (RMSSD) and radio-frequency component HF and assess human body autonomic nervous system active A NSA, assess autonomic nervous system balance ANSE according to low frequency/high frequency ratio (LF/HFration), its computing formula is:
ANSA=k 1* SDNN+k 2* RMSSD+k 3* HF+k 4, wherein k ifor power layer coefficients, four power layer coefficients are determined by clinical trial, and value is between 0-1, and four power layer coefficients sums are 1.
wherein k ifor power layer coefficients, two power layer coefficients are determined by clinical trial, and value is between 0-1, and two power layer coefficients sums are 1.
(5) analyze the stress state of experimenter, comprise following four indexs: emotional state index, anti-pressure ability index, physical fatigue exponential sum pressure state index; Its computing formula is as follows:
Emotional state index: wherein k ifor power layer coefficients, four power layer coefficients are determined by clinical trial, and value is between 0-1, and four power layer coefficients sums are 1.
Anti-pressure ability index: RPAI=k 1* TP+k 2* RMSSD+k 3* pNN 50+ k 4, wherein k ifor power layer coefficients, four power layer coefficients are determined by clinical trial, and value is between 0-1, and four power layer coefficients sums are 1.
Physical fatigue index: BFI=k 1* VLI+k 2* LF+k 3, wherein k ifor power layer coefficients, three power layer coefficients are determined by clinical trial, and value is between 0-1, and three power layer coefficients sums are 1.
Pressure state index: PSI=k 1* VAI+k 2* HF+k 3, wherein k ifor power layer coefficients, three power layer coefficients are determined by clinical trial, and value is between 0-1, and three power layer coefficients sums are 1.
Pressure state index, be current residing stress state, generally have three kinds of states: pressure is little, without pressure (normally), pressure is large;
Emotional state index, whether the spiritual emotion of the current experimenter referred to anxiety, passiveness or normal.
Anti-pressure ability index, the anti-pressure ability of the experimenter referred to, such as this experimenter's pressure ratio is larger now, if but his anti-pressure ability is stronger, namely health voltage endurance capability is stronger, can not be worn down, otherwise health easily goes wrong in such health short time by pressure;
Physical fatigue index, refers to non-spiritual pressure, refers to the degree of fatigue on health, this fatigue that such as driver's long-duration driving produces.
(6) according to the stress index of correlation obtained, the mental status of measured is assessed.Demarcated the span of various state in emotional state index, anti-pressure ability index, physical fatigue exponential sum pressure state index by controlled clinical trial, provide corresponding suggestion simultaneously.As demarcated the value of anxiety, passiveness and normal three kinds of states by controlled clinical trial.
(7) provide measured according to the pulse frequency measured and heart rate and whether suffer from atrial fibrillation or the premature beat that takes place frequently prompting.

Claims (8)

1. a human body stress state analysis device, comprises sensor assembly, physiological signal collection module, embedded system platform and wireless communication module; It is characterized in that:
Sensor assembly, comprise right electrocardiogram acquisition electrode, left electrocardiogram acquisition electrode and pulse wave sensor, all with physiological signal collection model calling, the photoplethysmographic signal that electrocardiosignal and finger for gathering human body are held, and by Signal transmissions to physiological signal collection module;
Physiological signal collection module, be connected with embedded system platform, comprise ECG detecting circuit and pulse wave testing circuit, the electrocardiogram acquisition Electrode connection in ECG detecting circuit and sensor assembly, pulse wave testing circuit is connected with the pulse wave sensor in sensor assembly; Analog-digital conversion is carried out to the simulation physiological signal that testing circuit produces, and transfers to embedded system platform and process;
Wireless communication module is connected with embedded system platform, and wirelessly between embedded system platform and personal hand-held terminal, transmits data;
Embedded system platform, is connected with wireless communication module, for analyzing the data of physiological signal collection module input, reduction electrocardio and pulse wave signal, carry out stress analysis, complete following operation:
(1) resolve by the data of physiological signal collection module transfer, reduction electrocardiosignal and pulse wave signal, carry out filtering and amplification to signal, obtain human body electrocardio figure wave sequence X (K) and pulse waveform sequence Y (k);
(2) analyze pulse waveform sequence Y (K), utilize each characteristic point on this waveform (as ascending branch starting point, peak point or incisura) to calculate pulse frequency PR;
(3) analyze ecg wave form sequence X (K), identify the R ripple in ecg wave form, obtain the RR interval of heart rate and electrocardiosignal, RR interval is analyzed, obtain heart rate variability relevant time domain index, frequency-domain index and nonlinear analysis index; Wherein:
1. time domain index comprises: R-R interval standard deviation, adjacent R-R interval difference root-mean-square value and adjacent normal R-R interval are more than the percentage ratio of 50ms;
2. frequency-domain index comprises: gross energy TP, low-frequency component LF, radio-frequency component HF and low frequency/high frequency ratio;
3. nonlinear indicator comprises: vector length index VLI and vectorial angle index VAI;
(4) analyze heart rate variability metrics R-R interval standard deviation, adjacent R-R interval difference root-mean-square value and radio-frequency component HF and assess human body autonomic nervous system active A NSA, according to low frequency/high frequency ratio assessment autonomic nervous system balance ANSE;
(5) analyze the stress state of experimenter, comprise following four indexs: emotional state index, anti-pressure ability index, physical fatigue exponential sum pressure state index;
(6) according to the stress index of correlation obtained, the mental status of measured is assessed;
(7) provide measured according to the pulse frequency measured and heart rate and whether suffer from atrial fibrillation or the premature beat that takes place frequently prompting.
2. human body stress state analysis device according to claim 1, is characterized in that: the computing formula of described R-R interval standard deviation (SDNN) is:
S D N N = Σ i = 1 N ( RR i - m e a n R R ) 2 N ,
Wherein N is the sum of normal heartbeat in omnidistance record, RR ibe i-th R-R interval, meanRR is the meansigma methods of the R-R interval of N number of heartbeat.
3. human body stress state analysis device according to claim 1, is characterized in that: the computing formula of described adjacent R-R interval difference root-mean-square value (RMSSD) is:
R M S S D = Σ i = 1 N ( RR i + 1 - RR i ) 2 N ,
Wherein N is the sum of normal heartbeat in omnidistance record, RR i+1, RR ifor the length of adjacent two sinus cardiac cycles.
4. human body stress state analysis device according to claim 1, is characterized in that: described adjacent normal R-R interval is more than the percentage ratio (pNN of 50ms 50) computing formula be:
pNN 50 = NN 50 N N × 100 ,
Wherein NN 50in omnidistance record, adjacent R-R interval difference is greater than the number of 50ms, and NN is R-R interval sum.
5. human body stress state analysis device according to claim 1, is characterized in that: the computational process of described frequency-domain index is:
To RR interval series analysis of spectrum, the Energy distribution situation of quantitative description HRV signal, obtains the power spectral density plot of AR model;
Low-frequency component LF is the integrated value of power spectral density plot in 0.04-0.15Hz frequency band;
Radio-frequency component HF is the integrated value of power spectral density plot in 0.15-0.4Hz frequency band;
Gross energy TP: the integrated value of power spectral density plot in 0.04-0.4Hz frequency band, equals the summation of low-frequency component LF and radio-frequency component HF;
Low frequency/high frequency ratio: the ratio of low-frequency component LF and radio-frequency component HF.
6. human body stress state analysis device according to claim 1, is characterized in that: the computational process of described nonlinear indicator is as follows:
With the previous R-R interval length RR of adjacent two sinus cardiac cycles ifor abscissa, a later R-R interval RR i+1for vertical coordinate is drawn a bit, so continue, in regular hour section, be depicted as by each loose scattergram formed, obtain according to this scattergram:
Vector angle index: wherein N is the number of loose point, and a (i) is the vectorial angle angle value of point loose in scatterplot;
Vector length index: wherein N is the number of loose point, and b (i) and b (j) is the vector length angle value of point loose in scatterplot.
7. human body stress state analysis device according to claim 1, is characterized in that, in described step (4), the computing formula of ANSA and ANSE is:
ANSA=k 1* SDNN+k 2* RMSSD+k 3* HF+k 4, wherein k ifor power layer coefficients;
A N S E = k 1 L F H F + k 2 , Wherein k ifor power layer coefficients;
Each power layer coefficients in each index is determined by clinical trial, value 0-1, and the proprietary rights layer coefficients sum in each index is 1.
8. human body stress state analysis device according to claim 1, it is characterized in that, in described step (5), the computing formula of four indexs is as follows:
Emotional state index: M S I = k 1 * L F + k 2 * H F + k 3 L F H F + k 4 , Wherein k ifor power layer coefficients;
Anti-pressure ability index: RPAI=k 1* TP+k 2* RMSSD+k 3* pNN 50+ k 4, wherein k ifor power layer coefficients;
Physical fatigue index: BFI=k 1* VLI+k 2* LF+k 3, wherein k ifor power layer coefficients;
Pressure state index: PSI=k 1* VAI+k 2* HF+k 3, wherein k ifor power layer coefficients;
Each power layer coefficients in each index is determined by clinical trial, value 0-1, and the proprietary rights layer coefficients sum in each index is 1.
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