KR20120033777A - A method of stress analysis using a biometric reaction information on varying a time - Google Patents

Abstract

PURPOSE: A method for analyzing stress through a time-variable biometric reaction signal is provided to accurately analyze the stress by converting a biometric reaction signal feature of a subject in a frequency domain. CONSTITUTION: A biometric signal of a subject is measured by using an electrocardiogram measuring instrument(S101). The biometric signal of the subject is obtained(S103). The measured biometric signal is changed from a frequency domain to a spectrum quantity(S105). A HRV(Heart Rate Variability) parameter is extracted(S107). An absolute power ratio of a low frequency element to a high frequency element is calculated(S109). Stress is determined by analyzing the signal(S111).

Description

A method of stress analysis using a biometric reaction information on varying a time}

The present invention relates to stress analysis using heart rate variability (HRV), and more particularly, to convert the characteristics of the bioreaction signal of a learner, which is a non-periodic signal that changes with time, into a frequency domain. The present invention relates to a stress analysis method using heart rate variability that enables the identification of stress by interpretation.

Stress causes a change in the body's metabolism, which is manifested by abnormal changes in hormones or the cardiovascular system. Heiko Schoder, Daniel H. Silverman, Roxana Campsi, Harold Karpman, Michael E Phelps, Heinruch R. Schelbert, Johannes Czernin, "Effects of Mental Stress on Myocardial Blood Flow and Vasomotion in Patients with Coronary Artery Disease", The Journal of Nuclear Medicine, vol. 41, no. 1, pp 11-16, 2000. and references Willem J. Kop, John S. Gottdiener, Stephan M Patterson, David S. Krantz, "Relationship between left ventricular mass and hemodynamic response to physical and physical stress ", Journal of Psychosomatic Research, vol. 48, pp79-88, 2000. and references Jean Pierre Fauvel, Catherine Cerutti, Pierre Quelin, Maurice Laville," Mental Stress-Induced Increase in Blood Pressure IS Not Related to Baroreflex Sensitivity in Middle-Aged Healthy Men ", Hypertension, vol. 35, pp887-891, 2000., et al., Showed that the effects on the cardiovascular system such as changes in heart rate, blood pressure, etc. . The human body responds to changes in the autonomic nervous system to stresses such as stress. Heart rate variability (HRV) has been studied to measure such changes in a noninvasive manner.

Conventional techniques for evaluating autonomic nervous system function by heart rate variability analysis are based on the unification of terms related to heart rate interval analysis indicators, suggesting the normal range of Western standard, and the clinical use of neurophysiology for each indicator. The introduction of papers on the field has been rapidly developed.

Heart rate interval refers to the interval between peaks of an electrocardiogram or pulse wave waveform. The graph of change in heart rate interval during a measured time is called RR Interval Variability (RRV) and according to the literature, the cycle length variability (CLV) Length

Variability, and heart rate variability (HRV).

In general, well-known analytical indicators that can be extracted from the time series of heart rate variability include Heart Rate (HR), Standard Deviation of Normal R-Normal R intervals (SDNN), and Low Frequency (LF) Low Frequency (LF). , Absolute power of high frequency region (HF), absolute power ratio of low frequency region to high frequency region (LF / HF), spread of probability distribution (HRV-Index, Heart Rate Variability-Index).

These heart rate variability analysis indicators do not have any significant differences according to gender, but due to aging of autonomic nervous system, the differences by age group tend to be large.

HRV signal is widely used for predicting various cardiovascular diseases, etc., after reconstructing from ECG to HRV signal, applying frequency analysis method, and as shown in Table 1 below, Changes indicate the activity of the sympathetic / parasympathetic nerves of the autonomic nervous system. In order to observe the activity of the autonomic nervous system from the HRV signal, the ratio of the frequency domain is divided into a low frequency (LF), a middle frequency (MF), and a high frequency (HF) region.

References G.Gerra, A.Zaimovic, U.Zambelli, M.Timpano, N.Reali, "Neuroendocrine Responses to Psychological Stress in Adolescents with Anxiety Disorder", Neuropsychophysiology, vol. 42, pp82-92, 2000. and references Rollin McCraty, Mike Atkinson, William A. Tiller, Glen Rein, Alan D. Watkins, "The Effects of Emotions on Short-Term Powear Spectrum Analysis of Heart Rate Variability", The American Journal of Cardiology, vol. 76, pp 1089-1093 , 1995. According to the user, when the user requires mental concentration or stimulation to the external environment, the LF component is increased to increase the LF / HF value, and Rollin et al. By measuring the signal and observing the change, the LF / HF value increases in the bad emotional state and the MF / (LF + HF) value increases in the good emotional state.

Frequency Components and Emotions of HRV Signals Frequency component Frequency domain Mechanism Low Frequency (LF) 0.01 to 0.08 Hz It mainly represents the activity of the sympathetic nerve and increases the amount of activity during mental stress. Mid Frequency (MF) 0.08 to 0.15 Hz Representative activity of both sympathetic / parasympathetic nerves and parasympathetic nerve activity is more prevalent. High Frequency (HF) 0.15 to 0.5 Hz It is mainly caused by parasympathetic nerves and increases in active emotional stability.

However, even if the normal range is presented only for these analytical indicators, this is often based on a specific race (for example, westerner), and there is a problem that Asians such as Koreans are not suitable to apply it.

In addition, although the conventional HRV signal analysis methods can be applied to some extent to the bio signals that do not change, there is a limit in analyzing the bio signals that change dynamically with time.

Accordingly, the present invention has been proposed to solve various problems occurring in the conventional HRV signal analysis method as described above.

The problem to be solved by the present invention, stress analysis using the heart rate variability that can accurately analyze the stress by converting the characteristics of the learner's biological response signal that appears as a non-periodic signal that changes over time to the frequency domain to interpret the signal To provide a way.

"Stress analysis method using heart rate variability" according to the present invention for solving the above problems,

A biosignal obtaining step of obtaining a biometric response signal of the learner, which is a non-periodic signal that changes with time;

And converting the obtained biological response signal into a frequency domain, and analyzing the stress through signal analysis.

In the above stress analysis step,

The bioreaction signal may be converted into spectral quantities in a frequency domain by discrete Fourier transform (DFT).

In the above stress analysis step,

The heart rate variability parameter is extracted from the converted spectral amount in the frequency domain.

The heart rate variability parameter of the stress analysis step,

A spectral density (PSD), a high frequency (HF) component, a medium frequency (MF) component, and a low frequency (LF) component are included.

In the above stress analysis step,

Calculate the absolute power ratio (LF / HF) of the low frequency components to the high frequency components, and the absolute power ratio (MF / (LF + HF) of the medium frequency components to the frequency components that combine the high and low frequency components. It is characterized by determining the stress.

According to the present invention, there is an advantage that the stress can be accurately analyzed by converting the characteristics of the bioreaction signal of the learner, which is a non-periodic signal that changes over time, into a frequency domain to perform signal analysis.

1 is a block diagram of a stress analysis system using a heart rate variability to which the present invention is applied.
2 is a flow chart illustrating a stress analysis method using a heart rate variability according to the present invention.
3A to 3G are graphs showing biosignals and spectral densities, and respective frequency components and signal analysis obtained in the present invention.
Figure 4 is an environmental explanatory diagram for the experiment in the present invention.
5 is an experimental result table according to the present invention.

Hereinafter, described in detail with reference to the accompanying drawings a preferred embodiment of the present invention. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail.

1 is a configuration diagram of a stress analysis apparatus to which the present invention is applied, an electrocardiogram measurer 110 for measuring an electrocardiogram, an oxygen saturation measurer 120 for measuring oxygen saturation, and a microprocessor for analyzing stress from a measured biosignal 130, a display unit 140 for displaying a heart rate variability parameter or an analysis result for analysis, and a storage unit 150 for storing the calculated parameters and result data.

2 is a flowchart illustrating a stress analysis method using a heart rate variability according to the present invention, wherein a biosignal obtaining step (S101 to S103) of obtaining a biometric response signal of a learner, which is a non-periodic signal that changes with time; And a stress analysis step (S105 ˜ S111) of converting the obtained bioreaction signal into a frequency domain and analyzing the stress through signal analysis.

In the stress analysis step (S105 ~ S111), the discrete Fourier transform (DFT) of the biological response signal is converted into a spectral amount in the frequency domain (S105), the heart rate variability parameter from the converted spectral amount in the frequency domain In step S107, the absolute power ratio (LF / HF) of the low frequency component to the high frequency component, and the absolute power ratio (MF / (LF + HF) of the medium frequency component to the frequency component of the sum of the high frequency component and the low frequency component Comprising the step (S109) and the signal analysis to determine the stress (S111).

In the stress analysis method using the heart rate variability according to the present invention configured as described above, an ECG measuring device and an oxygen saturation measuring device are first attached to a subject to measure a biosignal, and the ECG measuring device 110 measures the ECG and the oxygen saturation degree. The measurer 120 measures oxygen saturation and transmits the oxygen saturation to the microprocessor 130 as a biosignal that dynamically changes with time.

Then, the microprocessor 130 performs the stress analysis process as shown in FIG. 2 to analyze the stress.

That is, in steps S101 and S103, a biosignal of a subject (learner) is measured by using an electrocardiogram detector and the like to obtain a biosignal.

In operation S105, the measured biosignal is discrete Fourier transform (DFT) to convert the measured biosignal into a spectral amount in the frequency domain.

In this case, the biosignal dynamically changes with time, and if the biosignal is Fourier transformed, the biosignal may be displayed as a spectral amount in the frequency domain. The Fourier transform represents a non-periodic function as a sum, or integration, of countless frequency sinusoidal components, and expresses a function of the time domain as a continuous exponential over the frequency domain. The spectral density function expressed in this way can know the relative magnitude of each frequency component included in a signal in the time domain and is an essential method for analyzing and understanding the signal in the time domain. Therefore, it is a useful tool to convert the characteristics of the bioreaction signal of the learner, which appears to be a non-periodic signal that changes over time, into the frequency domain for signal interpretation.

In the present invention, a discrete fourier transform of the following formula (1) was used. Where F (u) is the Fourier coefficient and f (x) is the measured biosignal.

Next, in step S107, the heart rate variability parameter is extracted from the transformed spectral density (PSD) of the frequency domain. That is, the high frequency (HF) component, the medium frequency (MF) component, and the low frequency (LF) component, which are heart rate variability parameters, are extracted from the spectral density PSD.

In more detail, assuming that a heart rate variability (HRV) signal as shown in FIG. 3A is obtained, the spectral density of the frequency domain as shown in FIG. 3B is calculated through a discrete Fourier transform. Through this, the frequency distribution of each area can be known.

Thereafter, the low frequency (LF) component as shown in FIG. 3C is extracted from the frequency spectrum distribution component, the middle frequency (MF) component as shown in FIG. 3D is extracted, and the high frequency (HF) component as shown in FIG. 3E is extracted.

Thereafter, by moving to step S109, using the extracted heart rate variability parameter, the absolute power ratio (LF / HF) of the low frequency component to the high frequency component, and the absolute power ratio of the medium frequency component to the frequency component of the sum of the high frequency component and the low frequency component ( 3F shows the absolute power ratio (LF / HF) of the low frequency components to the high frequency components, and FIG. 3G shows the frequency components of the sum of the high frequency components and the low frequency components. Absolute power ratio (MF / (LF + HF)) of the medium frequency components is shown.

Next, the process moves to step S111 to determine the stress through signal analysis based on the information calculated in step S109.

In other words, based on the relationship shown in Table 1, the heart rate variability signal analysis in the frequency domain is performed to analyze the amount of stress that the subject (learner) receives. For example, when the LF / HF value is increased, it is analyzed that there is a lot of stress as a bad emotional state, and when the MF / (LF + HF) value is increased, it is analyzed that it is a relatively low stress as a good emotional state. will be.

The present inventors experimented with the stress analysis method using the heart rate variability of the present invention as described in the following results.

As shown in FIG. 4, the experiment site for 17 children was conducted in an independent space of the day care center and maintained at room temperature of 23 ° C. The biological signal of the subject measured electrocardiogram and oxygen saturation. Bionet's BM3_wide model was used as the data measurement equipment. The sampling rate was set to 1 and the resolution was set to 12 bits. The measured data was transferred to a PC and stored and analyzed.

Before the experiment, the subjects were attached with electrodes for measuring bio signals, and laptops and headphones were prepared on the table. The subject listened to the description of the experiment for 2 minutes, had a ballast, and then watched 11 video contents for 10 minutes and surveyed the video contents. In addition, during the experiment, the outside sound was cut off, and all conversations were prohibited, and his biosignal data was not seen.

The measured electrocardiogram signal was reconstructed at intervals of RR by using QRS detection method by software provided by Bionet, and the heart rate variation was obtained.

3A to 3G are results of one subject obtained by using a frequency analysis method in the HRV signal measured during the experiment, the spectral density of the HRV signal, LF, MF, HF components, LF / HF, MF / ( LF + HF) analysis results.

FIG. 5 shows experimental results of extracting LF / HF and MF / (LF + HF) values, which are stress values, for 83 children.

As the experimental result shows, when the LF / HF value is increased, it can be seen that there is a lot of stress as a bad emotional state, and when the MF / (LF + HF) value is increased, it is relatively low as a good emotional state. It can be seen that.

The present invention is not limited to the above-described specific preferred embodiments, and various modifications can be made by any person having ordinary skill in the art without departing from the gist of the present invention claimed in the claims. Of course, such changes will fall within the scope of the claims.

110 ..... ECG meter
120 ..... Oxygen Saturation Meter
130 ..... Microprocessor
140 ..... Display
150 ..... storage

Claims (5)

In the stress analysis method using heart rate variability (HRV),
Obtaining a biometric signal of a subject (learner), which is an aperiodic signal that changes with time;
And a stress analysis step of converting the obtained biological response signal into a frequency domain and analyzing the stress through signal analysis.
The method of claim 1, wherein the stress analysis step,
Stress analysis method using the heart rate variability, characterized in that the bioreaction signal is converted to a spectral density in the frequency domain by discrete Fourier transform (DFT).
The method of claim 2, wherein the stress analysis step,
And a heart rate variability parameter is extracted from the converted spectral density of the frequency domain.
The heart rate variability parameter of the stress analysis step is
Stress analysis method using a heart rate variability, characterized in that it comprises a spectral density (PSD), a high frequency (HF) component, a medium frequency (MF) component, a low frequency (LF) component.
The method of claim 4, wherein the stress analysis step,
Calculate the absolute power ratio (LF / HF) of the low frequency components to the high frequency components, and the absolute power ratio (MF / (LF + HF) of the medium frequency components to the frequency components of the high and low frequency components. Stress analysis method using the heart rate variability, characterized in that to determine the stress.

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