CN101642369A - Autonomic nervous function biological feedback method and system - Google Patents

Abstract

The autonomic nervous function biofeedback method and system, on the basis of obtaining the heart rate variability physiological parameter value through the test, feedback and output the autonomic nervous function state reflected by the obtained heart rate variability physiological parameter with an easily detectable reminder signal, so that Users have a vivid understanding of their own autonomic nervous system, and then can assist users to learn to regulate the activities of internal organs through consciousness within a certain range, and correct internal organ activities that deviate from the normal range, and it is easy to implement.

Description

Autonomic nervous function biofeedback method and system

technical field

This application document relates to autonomic nervous function biofeedback technology.

Background technique

With the development of science and technology, human beings have created many effective methods to deal with physical diseases. In contrast, the ability to deal with mental diseases pales in comparison, such as anxiety and depression, which have become threats to human health. A lot of practice has proved that to deal with this type of disease, the effect of medicine, surgery and gene therapy alone is not good.

A person's psychological state will not only be reflected in emotion, but also in the activity level of the autonomic nervous system. The autonomic nervous system (ANS, Autonomic Nervous System) refers to the nerves distributed to the heart, lungs, digestive tract and other organs, including sympathetic and parasympathetic nerves. Most organs are controlled by both sympathetic and parasympathetic nerves. Under normal circumstances, the two work in harmony to regulate the daily operation of internal organs and the secretion of glands, so as to maintain a stable internal environment, such as blood pressure, heart rate, and body temperature. When the ANS is out of balance, it will cause many problems. The mild ones will cause some not very serious symptoms, such as gastrointestinal disorders, heart palpitations, dyspnea, etc., and the severe ones will cause various acute and chronic diseases, such as heart disease, high blood pressure, etc., and serious ones It may even cause sudden death.

Contents of the invention

The implementation of the autonomic nervous function biofeedback method includes: obtaining the heart rate variability physiological parameter value according to the bioelectrical signal; and outputting a corresponding reminder signal according to the heart rate variability physiological parameter value.

The embodiment of the autonomic nervous function biofeedback system includes: a test unit, which obtains the heart rate variability physiological parameter value according to the bioelectric signal; a feedback reminder unit, which outputs a corresponding reminder signal according to the heart rate variability physiological parameter value.

Another embodiment of the autonomic nervous function biofeedback system includes: a data acquisition unit, which processes the detected physiological electrical signals, and obtains time-domain heart rate data within a predetermined time interval; a processing device, which converts the time-domain heart rate data Converting to frequency-domain heart rate data, based on spectrum analysis and calculation of the frequency-domain heart rate data, obtaining heart rate variability physiological parameter values, and outputting corresponding reminder signals according to the heart rate variability physiological parameter values; output unit, outputting The reminder signal.

On the basis of obtaining the heart rate variability physiological parameter value through the test, the above-mentioned embodiment outputs an easy-to-perceive reminder signal according to the autonomic nervous function status information reflected by the obtained heart rate variability physiological parameter value, so that the user is aware of his own autonomy. The state of neurological function can be clearly understood, and then it can assist users to learn to regulate the activities of internal organs through consciousness within a certain range, and correct internal organ activities that deviate from the normal range, and it is easy to implement.

Description of drawings

Fig. 1 is the flowchart of the embodiment of autonomic nervous function biofeedback method;

Figure 2 is a schematic diagram of a normal electrocardiogram waveform;

Fig. 3 is a flow chart of a specific embodiment of outputting a corresponding reminder signal according to the heart rate variability physiological parameter value in D2 shown in Fig. 1;

Fig. 4 is a schematic diagram of the physiological parameter value of heart rate variability of S21 and its normal value range shown in Fig. 3;

Fig. 5 (a) is the schematic diagram of the trend chart of the physiological parameter of heart rate variability of the extrovert;

Fig. 5 (b) is a schematic diagram of the trend graph of physiological parameters of heart rate variability of introverted people;

Fig. 6 (a) is a schematic diagram of the trend graph of heart rate variability physiological parameters when normal people are awake and quiet;

Figure 6(b) is a schematic diagram of the trend graph of the physiological parameters of heart rate variability when the mood of a normal person is disturbed;

7 is a schematic diagram of an embodiment of an autonomic nervous function biofeedback system;

Fig. 8 is a schematic diagram of a specific embodiment of the test unit N1 shown in Fig. 7;

FIG. 9 is a schematic diagram of a specific implementation of the data acquisition unit M1 shown in FIG. 8;

Fig. 10 is a schematic diagram of a specific embodiment of the preprocessing unit M1b shown in Fig. 9;

FIG. 11 is a schematic circuit diagram of a specific embodiment of the first amplifying unit 1101 shown in FIG. 10;

FIG. 12 is a schematic circuit diagram of a specific embodiment of the second amplifying unit 1102 shown in FIG. 10;

FIG. 13 is a schematic circuit diagram of a specific embodiment of the filtering and amplifying unit 1103 shown in FIG. 10;

Fig. 14 is a schematic circuit diagram of a specific embodiment of the detection and shaping unit shown in Fig. 9;

Fig. 15 is a schematic diagram of a specific embodiment of the physiological parameter calculation unit shown in Fig. 8;

Fig. 16 is a schematic diagram of a specific embodiment of the feedback reminding unit shown in Fig. 7;

Fig. 17 is a schematic diagram of another embodiment of the autonomic nervous function biofeedback system;

Fig. 18 is a schematic diagram of feedback reminder signals and stimulation signals in the specific embodiment shown in Fig. 17 .

Figure 19 is a schematic diagram of an embodiment of an autonomic nervous function biofeedback system.

Detailed ways

Referring to FIG. 1 , embodiments of the autonomic nervous function biofeedback method include:

Step D1, obtaining the heart rate variability physiological parameter value according to the bioelectrical signal; Step D2, outputting a corresponding reminder signal according to the heart rate variability physiological parameter value.

The physiological parameters of heart rate variability (HRV, Heart Rate Variability) can reflect the information of the autonomic nervous system function, and can provide doctors with the patient's psychological condition, so as to formulate a treatment plan that is more in line with the disease.

The heart rate of the human body is not absolutely regular, and there is a time difference of tens of milliseconds or even more than 100 milliseconds between two heartbeats. Under normal circumstances, the change of the interval between the heartbeats of healthy people is caused by the changes of the sympathetic and parasympathetic nerves with breathing and other factors. Heart rate variability is the difference in time between heartbeats. Heart rate variability is a parameter that reflects the ability of the heart to self-regulate to external or internal stimuli. A higher HRV indicates that the heart can adapt more quickly to external or internal influences, and that there is a good interaction between the sympathetic and parasympathetic nervous systems; while a low HRV indicates poor adaptability of the body. By measuring the size and speed of changes in the normal heartbeat interval, it can reflect that the autonomicity of the sinoatrial node is regulated by the autonomic nervous system.

The total HRV signal is composed of many single frequencies. By analyzing the spectrum of these frequencies, the researchers found that the parameters total frequency power (TP, Total frequency Power), high frequency power (HF, High Frequency Power), low frequency power (LF, Low Frequency Power), very low and very low frequency power (VLF, Very Low Frequency Power), and the ratio of low frequency power to high frequency power (LF/HF) are parameters reflecting autonomic nervous function. Among them, the HF part is synchronized with the respiratory signal of the animal, and HF or TP can reflect the function of the parasympathetic nerve; LF, VLF and LF/HF can reflect the activity of the sympathetic nerve.

Step D1 obtains the heart rate variability physiological parameter value according to the bioelectrical signal, which may include in a specific embodiment:

Step S11, obtaining heart rate data according to the bioelectric signal; specifically, including amplification and filtering of the detected bioelectric signal, QRS complex detection and shaping, analog-to-digital conversion, and calculation of heart rate data.

Physiological signals can generally be divided into two categories, one is electrical signals and signals derived from electrical activity, such as electrocardiographic signals and cardiomagnetic signals, which can be called physiological electrical signals; the other is non-electrical signals, including body temperature, blood pressure, etc. , respiration, heart sounds, muscle contraction, partial pressure of carbon dioxide, partial pressure of oxygen, PH value, etc.

The heart is like the power source in the human body. In each cardiac cycle, the pacemaker, atrium, and ventricle are excited one after another. The conductive tissue and body fluid around the heart conduct and reflect the sum of potential changes of countless cardiomyocytes to the body surface. Among the points distributed on the surface of the human body, some points have the same potential, and some points have potential differences. In one embodiment, the process of detecting the physiological electrical signal may include: measuring the potential difference between non-isoelectric points on the body surface through sensors such as electrodes to record the electrocardiographic signal. In other embodiments, the non-contact SQUIT system can also be used to convert cardiomagnetic signals into electrical signals and record them, so as to obtain the physiological electrical signals for subsequent analysis and processing.

The amplification refers to amplifying the detected bioelectrical signal to distinguish it from other interference signals such as torso signals, providing signal data with a sufficiently large amplitude for analysis and recording, and limiting current flow into the human body.

The filtering refers to filtering the amplified bioelectrical signal to retain signals in a certain frequency range, including high-frequency filtering and low-frequency filtering.

The QRS complex detection and shaping refers to detecting the QRS complex and shaping the obtained QRS waveform to obtain the R wave signal.

The waveforms in the electrocardiogram are named by unified English letters. Referring to Figure 2, a normal electrocardiogram includes P waves, PR segments, QRS complexes, ST segments, and T waves. Among them, the P wave refers to the first positive wave above the reference horizontal line, which is caused by the potential change of the atrial depolarization before the atrial contraction; the PR segment refers to the duration from the beginning of the P wave to the beginning of the QRS wave group. It is the interval time from the beginning of atrial depolarization to the beginning of ventricular depolarization; the QRS wave group is caused by the potential change of the ventricular depolarization before the ventricular contraction; the T wave is the potential change of the ventricular repolarization; the ST segment is the change from the end of the QRS wave group The line segment between the end and the beginning of the T wave, when the ventricles are all in a state of depolarization, and there is no potential difference, so it is normally level with the baseline, called the isoelectric line. In the QRS complex, the Q wave refers to the first negative wave, the R wave refers to the first positive wave, the S wave refers to the first negative wave after the R wave, and the QS wave refers to the only QRS wave. negative wave.

The analog-to-digital conversion refers to converting the obtained R-wave signal and the amplified and filtered physiological electrical signal into digital signals through analog-to-digital conversion.

In the process of calculating the heart rate data, the heart rate data refers to the interval between the corresponding times of two adjacent R wave peaks, that is, the RR interval. The calculation of the heart rate data includes: sampling according to the R wave signal analog-to-digital conversion Frequency, the interval between R-wave apexes is obtained by calculation. Specifically, the RR interval can be obtained by multiplying the time interval between data points by the number of data points between adjacent R wave peaks.

On the basis of the obtained heart rate data, subsequent processing and analysis can be performed to obtain heart rate variability physiological parameter values.

It should be noted that, in order to obtain better results in the subsequent analysis and processing process, there are certain requirements for the heart rate data. In an embodiment, heart rate data should be obtained over a certain time interval. Typically, the time interval can be 15-40 minutes. If it is less than 15 minutes, the amount of heart rate data collected is insufficient; if it is longer than 40 minutes, it is easy to make the user anxious and emotionally affected, thereby affecting the test results.

Step S12, saving the obtained heart rate data; specifically, manual saving or automatic saving can be selected.

In the automatic saving mode, once the time interval reaches an integral multiple of the first period, the heart rate data and the number of heart rate data are automatically saved, and step S13 is entered. Wherein, the first period may be 15 to 40 minutes.

In the manual saving mode, when the time interval reaches or exceeds the first set value, the obtained heart rate data and the number of heart rate data are saved; if the first set value is not reached, the heart rate data and the number of heart rate data are not saved. To record; wherein, the first set value is less than the first period, under normal circumstances, the time for ordinary people to go from mood swings to complete stability is not more than 3 minutes, and the first set value should be slightly longer than the time from mood swings to full stability cycle to ensure that at least one mood swing is recorded. In a specific embodiment, the first set value may be 3 to 8 minutes.

Step S13, grouping the stored heart rate data.

Specifically, it refers to grouping the heart rate data saved in step S12 so that the number of each group of heart rate data is the second set value; if the number of the last group of heart rate data is less than the second set value, the available value is Zero data supplementation. Wherein, the second set value is determined by the first set value, and the corresponding second set value value is determined according to the value of the first set value; specifically, when the first set value is 3 minutes, The second set value is 256; when the first set value is 4 to 8 minutes, the second set value can be any integer between 256 and 540.

The grouping step size can be any integer value between zero and the second set value. For example, for the 3-minute heart rate data saved in step S13, group them with a step size of 128, so that the number of heart rate data in each group is 256, that is, the first to 256th heart rate data are the first group , the 129th to 384th heart rate data is the second group, and so on, when the number of the last group of heart rate data is less than the second set value, it is supplemented with data with a value of zero. The purpose of grouping with a certain step size here is to filter and smooth the trend curves of the physiological parameters in the subsequent physiological parameter trend graphs. When grouping, if the step size is closer to the second set value, the calculation amount is smaller, but the smoothing effect is weaker; when the step size is reduced, the smoothing effect is better, but the calculation amount is relatively larger.

Step S14, calculating the average value of each set of heart rate data obtained.

Specifically, it includes: calculating the sum of each set of heart rate data; dividing the obtained sum of heart rate data by the number of the set of heart rate data, that is, dividing by the second set value.

Step S15, calculating the difference between the value of each heart rate data in each group and the average value of the heart rate data in the group.

In step S16, frequency-domain heart rate data is obtained according to the obtained difference.

In a specific implementation, the frequency-domain heart rate data can be obtained through the following steps: use a window function to truncate the obtained difference to obtain the time-domain data to be analyzed; according to the time-domain data to be analyzed, obtain the corresponding frequency domain data.

Among them, the reason for data truncation is that since it is impossible to measure and calculate an infinitely long signal, a time segment is intercepted from the signal, and then the observed signal time segment is used for period extension processing to obtain a virtual infinitely long On this basis, the relevant analysis and processing of the signal is carried out.

The window function may include a Hamming window, a Hanning window, a Bradman window, a Gaussian window, and the like.

In a specific embodiment, the differences are processed using a Hamming window. The attenuation of the first side lobe of the Hamming window is -42dB. Its spectrum is synthesized from the spectrum of three rectangular time windows, and its weighting coefficient can make the side lobe smaller. The time function expression of the Hamming window used is:

$\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0.54</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.4</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\frac{\mathrm{<span\; class="notranslate">\; \&pi;t</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; )</span>& <span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; <figure-callout\; id="0"\; label="T"\; filenames="A2008100413780028H1.png,A2008100413780028H2.png"\; state="\{\{state\}\}">T</figure-callout></span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}& <span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; T</span>}\end{array}\right.$

Its window spectrum is:

$\mathrm{<span\; class="notranslate">\; W</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1.08</span>}\frac{\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; \&omega;T</span>}}{\mathrm{<span\; class="notranslate">\; \&omega;T</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.46</span>}<span\; class="notranslate">\; [</span>\frac{\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;\; T</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&omega;\; T</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&pi;</span>}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;\; T</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&pi;</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&omega;T</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&pi;</span>}}<span\; class="notranslate">\; ]</span>$

Wherein T is the Hamming window time period, and its length needs to cover all heart rate data in each group, which can be the second set value in a specific embodiment;

On this basis, the conversion of the time-domain data to be analyzed to the frequency-domain data can be realized by way of fast Fourier transform (FFT).

In another specific implementation manner, the obtaining of the frequency-domain heart rate data may include a step of converting the obtained difference into corresponding frequency-domain heart rate data through an autoregressive (AR) algorithm. The adopted AR algorithm is a conventional method, which will not be repeated here.

Step S17, calculating the heart rate variability physiological parameter value according to the obtained frequency domain heart rate data.

The physiological parameters include LFnorm (normalized value of low frequency power), HFnorm (normalized value of high frequency power), LF/HF (low frequency power/high frequency power) and the like. The low-frequency power LF is mainly dominated by the cardiac sympathetic nerve and can be used as a parameter of the cardiac sympathetic efferent activity level; while the high frequency spectrum represents the heart rate fluctuation parameter originating from the vagus nerve (parasympathetic nerve), so the high-frequency power HF can be used as a quantitative Observe parameters of vagal efferent activity. The LF/HF ratio can be used as a measure of sympathetic-parasympathetic balance.

The calculation of the heart rate variability physiological parameter value specifically includes: calculating the frequency interval; calculating the frequency power according to the calculated frequency interval; and calculating the normalized value of the frequency power and the frequency ratio according to the calculated frequency power.

The frequency interval is the frequency interval between the frequency domain data corresponding to each heart rate data in each set of heart rate data. The frequency interval can be obtained by the reciprocal of the product of the mean value of the frequency domain data corresponding to each set of heart rate data and the number of the set of heart rate data, that is, the reciprocal of the product of the mean value of the frequency domain data corresponding to each set of heart rate data and the second set value .

According to the frequency interval, the number of heart rate data included in the frequency range corresponding to each spectrum segment of the heart rate variability can be calculated, and the corresponding frequency power value can be obtained by adding the powers of all frequency domain data corresponding to the heart rate data. Specifically, according to the definition of the HRV spectrum segment, the very low frequency power VLF is the power of frequencies less than 0.04Hz, the low frequency power LF is the power of frequencies in the range of 0.04Hz to 0.15Hz, and the high frequency power HF is the power of frequencies in the range of 0.15Hz to 0.15Hz. Power at frequencies in the 0.4Hz range. Therefore, the calculation of frequency power according to the frequency interval includes calculation of total frequency power TP, calculation of LF, calculation of HF and calculation of VLF; wherein, calculation of TP refers to calculation within a certain frequency range, specifically, it can refer to 0.4 In the Hz range, the sum of the power of all frequency domain data corresponding to the heart rate data is to add the power of each frequency domain data corresponding to the heart rate data to obtain the total frequency power TP; calculating LF can refer to calculating at 0.04Hz to 0.15Hz The sum of the power of all frequency domain data corresponding to the heart rate data in the range; calculating HF can refer to calculating the sum of the power of all frequency domain data corresponding to the heart rate data in the range of 0.15Hz to 0.4Hz; calculating VLF can refer to calculating at 0.04 The sum of the power of the frequency domain data corresponding to the heart rate data in the Hz range.

After the value of the frequency power is obtained, the process of calculating the normalized value of the frequency power may specifically include: calculating LFnorm, HFnorm and LF/HF according to the LF, HF and TP obtained by calculating the frequency power. Calculating LF/HF refers to calculating the ratio of LF and HF. In one embodiment, the process of calculating LFnorm and HFnorm includes: dividing the corresponding LF and HF values by the difference between the total power and VLF, and then multiplying the result by 100 to obtain the low frequency/high frequency power normalization value.

Then go to step D2.

Referring to FIG. 3 , step D2 outputs a corresponding reminder signal according to the heart rate variability physiological parameter value. In a specific embodiment, it may include:

Step S21, comparing the heart rate variability physiological parameter value with its normal value range to obtain the trend of each parameter over time.

In a specific embodiment, the value of LFnorm in the range of 50-58nU, the value of HFnorm in the range of 26-32nU and the value of LF/HF in the range of 1.5-2. Referring to Figure 4, area B in the parameter trend graph is the set normal range; when LFnorm or LF/HF is higher than the normal value, or HFnorm is lower than the normal value, it is considered that the sympathetic nerve is dominant, as shown in the parameter trend graph in Figure 4 Area A of the above is the sympathetic nerve-dominated area; when the LFnorm or LF/HF is lower than the normal value, or the HFnorm is higher than the normal value, it is considered that the parasympathetic nerve is dominant, and the C area in the parameter trend diagram in Figure 4 is the sympathetic nerve. The parasympathetic dominance described above. Generally, when normal people sleep, their LF and LF/HF are lower than the normal value, and the trend graph curves are all in area C, as shown in Figure 4, which is consistent with the fact that the parasympathetic nerve plays a leading role in physiological sleep, and the curve Very stable.

The user's personality characteristics will also affect the trend distribution position of LF, HF, and LF/HF parameters. Referring to Figure 5, Figure (a) is the trend graph of heart rate variability physiological parameters obtained by people with a more extroverted personality, and most of the curves are in area A; with reference to Figure 5, Figure (b) is obtained by people with a more introverted personality The trend graph of heart rate variability physiological parameters, most of the curves are in area C; but in general, the distribution of various physiological parameters of heart rate variability in normal people is close to area B, whether it is biased towards area A or area C, or Floating around area B.

A large number of experiments have found that when normal people are awake and quiet, their LF, HF, and LF/HF trend curves are relatively stable as shown in Figure 6(a); ), the trend curve shows large fluctuations.

Step S22, according to the comparison result and the trend of each parameter over time, the autonomic nervous function state is obtained.

Step S23, outputting a corresponding reminder signal according to the autonomic nervous function state.

For example, when in a resting state, if the user's LF and LF/HF are significantly higher than the normal range, it indicates hyperactivity of the sympathetic nervous system, and the user may be an anxiety patient; When LF increases, or when LF increases, HF decreases, or any one or more of the three situations of LF/HF increase, it can be considered that the user has an anxiety disorder; when HF increases or LF/HF decreases, The user may be considered to have a depressive disorder.

Corresponding to different autonomic nervous function states, the reminder signal can be set to have several levels with the same number as the corresponding output. Wherein, the reminder signal can be output in the form of image or sound or a combination of image and sound, and different pictures, or sounds, or a combination of picture and sound are set corresponding to different levels of reminder signals. For example, corresponding to the three states of anxiety, normal, and depression, the reminder signal is divided into three levels: A, B, and C, and three cartoon face pictures of frowning, smiling and crying are used as the output form of the reminder signal. When the reminder signal is A level, a frowning cartoon face picture is displayed; when the reminder signal is B level, a grinning cartoon face picture is displayed; when the reminder signal is C level, a crying cartoon face picture is displayed.

Another embodiment of the above autonomic nervous function biofeedback method may further include a step of: generating and continuously outputting stimulation signals with a set frequency range.

Specifically, the stimulating signal is used to assist in stabilizing the user's mood, making him feel calm. The set frequency range can be a frequency range that is generally considered in the industry to make the human body feel comfortable and stable, that is, not greater than 2 Hz. In one embodiment, the stimulation signal frequency can be set to 0.25 Hz, or 0.5 Hz, or 0.75 Hz, or 1 Hz, or 2 Hz. After a lot of experiments, it has been shown that under the condition of other experimental conditions being the same, the user will be emotionally stable if he stares at the pendulum with the set frequency for a long time. Moreover, there is no limitation on the effect by which graphic is used as the expression form of the stimulation signal, for example, a moving stick, a moving ball, a metronome, etc. may also be used.

With reference to Fig. 7, the embodiment of autonomic nervous function biofeedback system comprises:

The test unit N1 obtains the heart rate variability physiological parameter value according to the bioelectric signal; the feedback reminder unit N2 outputs different levels of reminder signals according to the heart rate variability physiological parameter value.

Referring to FIG. 8, the test unit N1, in a specific implementation manner, includes:

The data acquisition unit M1 obtains heart rate data according to the bioelectrical signal; the data recording unit M2 saves the obtained heart rate data and the number of heart rate data; the data grouping unit M3 groups the saved collected signals; the mean value unit M4, Calculate the average value of each group of heart rate data obtained; the difference calculation unit M5 calculates the difference between the value of each heart rate data in each group and the obtained average value of the group of heart rate data; the frequency domain data calculation unit M6 , according to the obtained difference, to obtain the frequency-domain heart rate data; the physiological parameter calculation unit M7, according to the obtained frequency-domain heart rate data, calculates the heart rate variability physiological parameter value.

In a specific implementation manner, referring to FIG. 9 , the data acquisition unit M1 may include a preprocessing unit M1b, a detection and shaping unit M1c, an analog-to-digital conversion unit M1d, and a heart rate data calculation unit M1e. The bioelectric signal detected by the bioelectric signal detection unit M1a is processed by the preprocessing unit M1b, the detection and shaping unit M1c, the analog-to-digital conversion unit M1d and the heart rate data calculation unit M1e to obtain heart rate data.

Wherein, the physiological electrical signal detection unit M1a detects the physiological electrical signal of the human body, and in a specific embodiment, it includes electrodes connected to the human body to detect the electrocardiographic signal; in another specific embodiment, it includes electrodes that can connect the human body The cardiomagnetic signal is converted into an electrical signal by the SQUIT system.

The preprocessing unit M1b amplifies and filters the obtained bioelectrical signal. In one embodiment, the preprocessing unit M1b meets the following technical parameters: the amplification factor is not less than 1000; the frequency response is 0.05-100Hz; the input impedance is not less than 3MΩ; common mode rejection ratio not less than 100dB; local noise not greater than 3μV _pp .

Referring to FIG. 10 , the preprocessing unit M1b includes a first amplifying unit 1101 , a second amplifying unit 1102 and a filtering and amplifying unit 1103 . Wherein, the bioelectrical signal input to the preprocessing unit M1b is amplified by the first amplifying unit 1101 and the second amplifying unit 1102 , and then filtered by the filtering and amplifying unit 1103 .

In a specific embodiment, referring to FIG. 11 , the first amplifying unit 1101 may include five operational amplifiers 1201, 1202, 1203, 1204, and 1205 to distinguish physiological electrical signals from other interference signals such as torso signals and provide high input impedance. The role of limiting the flow of current into the human body, where the signals A and A' are the received physiological electrical signals, the signals B and B' are the output amplified signals, and the signal _B1 is the suppression signal, which is fed back to the user to limit the current inflow human body.

Referring to FIG. 12 , the second amplifying circuit 1102 further amplifies the bioelectrical signal for subsequent recording and analysis, and may include an amplifier 1301, which is equivalent to an equivalent circuit of three operational amplifiers, and can adapt to a relatively wide frequency range, wherein, Signals B and B' are received primary amplified signals, and signal C is an output secondary amplified signal.

Referring to FIG. 13 , the filtering and amplifying unit 1103 filters the amplified bioelectrical signal, including low-frequency filtering below 0.05 Hz and high-frequency filtering above 100 Hz. It may include a filter amplification circuit composed of an operational amplifier 1401, wherein the signal C is the received secondary amplified signal, and the signal D is the output filtered signal.

A normal ECG waveform includes P wave, QSR wave group, and T wave. These waves are periodically repeated according to the excitation pulse generated by the sinoatrial node. Compared with other waveforms, the R wave has a higher amplitude, while the T wave Waves, P waves, baseline drift and other frequency bands are outside the bottom of the QRS complex frequency bands. Therefore, the QRS complex can be detected and separated. In the above embodiment, the detection and shaping unit M1c detects the QRS complex and shapes the obtained QRS waveform to obtain a more obvious R wave.

Referring to FIG. 14 , the detection and shaping unit M1c includes a detection unit 1501 and a filtering unit 1502; the detection unit 1501 receives the bioelectrical signal output by the preprocessing unit M1b, and obtains an R wave signal; the filtering unit 1502 removes the obtained R wave signal. Noise, and highlight the R wave. Wherein, the detection unit 1501 includes a differential circuit 111 and a full-wave detection circuit 112: the full-wave detection circuit 212 includes an operational amplifier, diodes D5 and D6, and feedback resistors R19, R20, R21, R22, R23, R24, and R25. ; The filtering unit 1502 may include a second-order low-pass filter. The bioelectrical signal output by the preprocessing unit M1b, that is, the signal D, is rectified by the differential circuit 111 and the full-wave detection circuit 112 to obtain a signal whose waveform is a unidirectional multi-peak pulse waveform, and then low-pass filtered by the filtering unit 1502. The waveform is smoothed to highlight the shape waveform of the R wave peak position, that is, the R wave signal.

The analog-to-digital conversion unit M1d receives the R-wave signal and the amplified and filtered physiological electrical signal, performs analog-to-digital conversion, and obtains a corresponding digital signal. Specifically, it can be realized through an analog-to-digital conversion circuit.

The heart rate data calculation unit M1e performs calculations based on the digital signal to obtain the RR interval, that is, the heart rate data. In one embodiment, its specific working process may include: receiving the digital signal provided by the analog-to-digital conversion unit M1d, obtaining a digital signal corresponding to the peak position of the R wave, and according to the digital signal and the sampling frequency during analog-to-digital conversion, The number of data points between adjacent RRs is obtained, and the number of data points is multiplied by the time interval to obtain the RR interval.

The data recording unit M2, in one embodiment, includes a storage mode selection unit and a storage unit. Wherein, the saving method includes automatic saving or manual saving, and the storage unit can store the time interval, heart rate data and the number of heart rate data.

In one embodiment, specifically, in the automatic saving mode, when the time interval reaches an integer multiple of the first period, the heart rate data and the number of heart rate data are automatically saved to the storage unit. In a specific embodiment, the first period may be 15 to 40 minutes. In the manual saving mode, when the time interval reaches the first set value, the heart rate data and the number of heart rate data are respectively saved to the storage unit; if the first set value is not reached, the heart rate data and heart rate data in the time interval Quantities are not recorded; wherein, the first set value is less than the first period. Under normal circumstances, the time for ordinary people to go from mood swings to complete stabilization is no more than 3 minutes, and the first setting value should be slightly longer than the time period from mood swings to complete stabilization to ensure that at least one mood swing is recorded. In a specific embodiment, the first set value may be 3 to 8 minutes.

The working process of the data grouping unit M3 may specifically include: grouping the stored heart rate data so that the number of each group of heart rate data is the second set value; if the number of the last group of heart rate data is less than the second set value, A data complement with a value of zero is available. Wherein, the second set value is determined by the first set value, and the corresponding second set value value is determined according to the value of the first set value; specifically, when the first set value is 3 minutes, The second set value is 256; when the first set value is 4 to 8 minutes, the second set value can be any integer between 256 and 540.

The mean value unit M4 is used to calculate the mean value of each set of heart rate data obtained. The working process may specifically include: calculating the sum of each group of heart rate data; dividing the obtained sum of heart rate data by the number of corresponding heart rate data of the group, that is, dividing by the second set value;

The difference calculation unit M5 calculates the difference between the value of each heart rate data in each group and the obtained average value of the group of heart rate data.

The frequency domain data calculation unit M6 obtains the frequency domain heart rate data according to the difference. The working process may specifically include: generating a window function, performing data truncation on the difference to obtain time-domain data; implementing FFT, converting the time-domain data into corresponding frequency-domain data.

Referring to FIG. 15 , in one embodiment, the physiological parameter calculation unit M7 includes a frequency interval calculation unit M7a, a frequency power calculation unit M7b, and a normalized value calculation unit M7c.

Wherein, the frequency interval calculation unit M7a calculates the reciprocal of the product of the mean value of the frequency-domain heart rate data and the number of heart rate data in the group to obtain the frequency interval.

The frequency power calculation unit M7b receives the frequency interval, calculates the number of heart rate data contained in the frequency range corresponding to each frequency spectrum segment of the heart rate variability, and adds the power of all frequency domain data corresponding to these heart rate data to obtain Corresponding frequency power value. Its working process can specifically include: calculating the sum of the power of the frequency domain data corresponding to the heart rate data in the range of 0.4Hz to obtain the total frequency power; calculating the sum of the power of the frequency domain data corresponding to each heart rate data in the range of 0.04Hz to 0.15Hz , to obtain the low-frequency power; calculate the sum of the power of the frequency-domain data corresponding to each heart rate data within the range of 0.15Hz to 0.4Hz to obtain the high-frequency power; calculate the sum of the power of the frequency-domain data corresponding to each heart rate data within the range of 0.04Hz , to obtain very low frequency power.

The normalized value calculation unit M7c calculates the high frequency power normalized value HFnorm, the low frequency power normalized value LFnorm and the ratio of high and low frequency power LF/HF according to the total frequency power, high frequency power, low frequency power and extremely low frequency power.

Referring to FIG. 16, in a specific implementation manner, the feedback reminding unit N2 includes:

The state classification unit M8 classifies the indicated autonomic nervous function states according to the relationship between each heart rate variability physiological parameter value and the normal value and the trend of each physiological parameter over time. The working process may specifically include: judging whether the value of the heart rate variability physiological parameter is within the set normal value range; judging the trend of the heart rate variability physiological parameter value over time; according to the judgment result, obtaining the autonomic nervous function state category .

The feedback output unit M9 maps it to a corresponding reminder signal according to the state category of the autonomic nerve function, and feeds back and outputs the reminder signal in the form of an image, or sound, or a combination of image and sound, to remind the user of its autonomic nervous function. Status; in one embodiment, may include a display, or a speaker, or a combination of a display and a speaker.

Referring to FIG. 17 , in another embodiment of the biofeedback system for autonomic nervous function, it further includes a stimulation signal generation unit, which generates and continuously outputs stimulation signals with a set frequency range.

In one embodiment, the stimulation signal generating unit may comprise a signal generating device. The signal generating device generates a signal with a set frequency, so that the pendulum or the moving straight rod oscillates at the set frequency, wherein the set frequency can be a medically recognized frequency range that can make the human body feel calm, that is, Not greater than 2 Hz, specifically, the frequency of the stimulation signal can be set to 0.25 Hz, or 0.5 Hz, or 0.75 Hz, or 1 Hz, or 2 Hz.

In another embodiment, the stimulation signal generation unit may also include an image display device, which displays the pendulum or the moving straight rod in the form of an image instead of the physical form, and still makes the pendulum or the straight rod in the form of the image Oscillate according to the set frequency. The form of expression of the stimulation signal is not limited to physical objects or virtual images, nor is it limited to image forms. The object moving at a set frequency can be a metronome or a moving ball.

Referring to FIG. 18 , in a specific embodiment, the reminder signal is displayed in the form of a smiling face, and the stimulation signal is displayed in the form of a pendulum image. The simple pendulum is in a motion state with a fixed frequency, and the reminder signal reminds the user of its current autonomic nervous function status, and the user keeps watching the screen during the implementation process. When the user is disturbed, the smiling face of the reminder signal changes to a sad face, indicating that the user is disturbed. After seeing the prompt, the user keeps looking at the pendulum and makes self-adjustment under the guidance of the doctor, so that the mental state can be improved.

Referring to Figure 19, embodiments of the autonomic nervous function biofeedback system include:

The data acquisition unit E1 processes the detected physiological electrical signals to obtain time-domain heart rate data within a predetermined time interval.

In one embodiment, the predetermined time interval may be 15 to 40 minutes.

The processing device E2 converts the time-domain heart rate data into frequency-domain heart rate data, obtains the heart rate variability physiological parameter value based on the frequency spectrum analysis and calculation of the frequency-domain heart rate data, and obtains the heart rate variability physiological parameter value according to the heart rate variability physiological parameter value , output the corresponding reminder signal.

In a specific implementation, the processing device E2 may be various electronic devices with data processing capabilities, such as computers, servers, single-chip microcomputers or microcontrollers. A memory may be included to store the time-domain heart rate data, the frequency-domain heart rate data and various intermediate data.

In one embodiment, the processing device E2 further includes a stimulation signal generation unit, which generates and continuously outputs stimulation signals in a set frequency range. For the specific implementation of the stimulation signal generation unit, reference may be made to the specific descriptions of the foregoing embodiments, which will not be repeated here.

The output unit E3 outputs the reminder signal.

During specific implementation, the output unit E3 may choose to output the reminder signal in any form of image, or sound, or a combination of image and sound. In one embodiment, the output unit E3 may include a display or a speaker.

For the specific implementation of the data acquisition unit E1, reference may be made to the descriptions of the foregoing embodiments, which will not be repeated here.

The above-mentioned embodiment can also be realized in the following way: the steps include saving the heart rate data, grouping the saved heart rate data, calculating the average value of each group of heart rate data, calculating the average value of each group in each group respectively The difference between the value of the heart rate data and the average value of the group of heart rate data, obtaining the frequency domain heart rate data according to the difference, performing spectrum analysis and calculation on the frequency domain heart rate data to obtain the heart rate variability physiological parameter value, based on the heart rate variability The autonomic nervous function state reflected by the physiological parameter value outputs a corresponding reminder signal, which is described in executable program code, and the storage medium storing the above-mentioned executable program code is directly or indirectly provided to the system or device, and the system or device The computer or central processing unit (CPU) in the computer reads out and executes the above-mentioned program codes.

At this time, as long as the system or device has the function of executing the program, the embodiment is not limited to the program, and the program may also be in any form, for example, an object program, a program executed by an interpreter, or a script program provided to the operating system wait.

The above-mentioned machine-readable storage media include, but are not limited to: various memories and storage units, semiconductor devices, magnetic disk units such as optical, magnetic and magneto-optical disks, and other media suitable for storing information, and the like. In addition, the above process can also be realized by connecting a client computer to a corresponding website on the Internet, and downloading and installing computer program codes into the computer and then executing the program.

In the above embodiment, on the basis of obtaining the heart rate variability physiological parameters, the autonomic nervous function status reflected by the heart rate variability physiological parameters is fed back and output as an easy-to-perceive reminder signal, so that the user has a vivid image of his own autonomic nervous function status. understanding, so as to assist users to adjust themselves according to the feedback reminder information under guidance, and it is easy to implement.

Claims (23)

1. A biofeedback method for autonomic nervous function, comprising: Obtain the heart rate variability physiological parameter value according to the bioelectrical signal; According to the heart rate variability physiological parameter value, a corresponding reminder signal is output. 2. The biofeedback method according to claim 1, wherein, according to the heart rate variability physiological parameter value, outputting a corresponding reminder signal comprises: Comparing the heart rate variability physiological parameter value with its normal value range to obtain the trend of each parameter over time; Obtain the autonomic nervous function state according to the comparison result and the trend of each parameter; According to the autonomic nervous function state, a corresponding reminder signal is output. 3. The biofeedback method according to claim 2, wherein the reminder signal has the same number of grades as the state of the autonomic nervous function. 4. The biofeedback method according to claim 1, further comprising continuously outputting stimulation signals with a set frequency range. 5. The biofeedback method as claimed in claim 1, wherein, according to the bioelectrical signal, obtaining the heart rate variability physiological parameter value comprises: Obtain heart rate data based on bioelectrical signals; Processing the heart rate data to obtain frequency domain heart rate data; According to the frequency-domain heart rate data, a heart rate variability physiological parameter value is obtained. 6. The biofeedback method as claimed in claim 5, wherein said obtaining heart rate data according to the bioelectrical signal comprises: Amplify and filter bioelectrical signals; Detect the QRS wave group in the bioelectrical signal, and perform waveform shaping on the obtained QRS wave group to obtain the R wave signal; Convert the amplified and filtered bioelectrical signal, as well as the R-wave signal that has been detected and shaped by the QRS complex, into digital signals. 7. biofeedback method as claimed in claim 5, wherein, described heart rate data is processed, and obtaining frequency domain heart rate data comprises: grouping the heart rate data; Calculate the average value of each group of heart rate data; calculating the difference between the value of each heart rate data in each group and the average value of the group of heart rate data; Frequency-domain heart rate data is obtained according to the difference. 8. The biofeedback method according to claim 7, wherein grouping the heart rate data comprises: making the number of each group of heart rate data a second set value; if the number of heart rate data is less than the second set value When , it is supplemented with data with a value of zero. 9. biofeedback method as claimed in claim 5, wherein, described according to frequency domain heart rate data, obtain heart rate variability physiological parameter value, comprising: Calculate the frequency interval between frequency domain heart rate data; According to the frequency interval, calculate the frequency power of the spectrum segment where each physiological parameter value of the heart rate variability is located; According to the frequency power, a frequency power normalized value and a frequency power ratio are calculated. 10. The biofeedback method according to claim 9, wherein the frequency power of the spectrum segment where each physiological parameter value of the heart rate variability is located includes the values of total frequency power, high frequency power, low frequency power and extremely low frequency power. 11. A biofeedback system for autonomic nervous function, comprising: The test unit obtains the heart rate variability physiological parameter value according to the bioelectrical signal; The feedback reminder unit outputs a corresponding reminder signal according to the heart rate variability physiological parameter value. 12. The biofeedback system as claimed in claim 11, wherein the feedback reminder unit comprises: The state classification unit classifies the indicated autonomic nervous function state according to the heart rate variability physiological parameter value; The feedback output unit, according to the state category, maps it to a corresponding reminder signal, and feedbacks and outputs the reminder signal. 13. The biofeedback system as claimed in claim 12, wherein the state classification unit judges whether the value of the heart rate variability physiological parameter value is in the set normal value range; judges that the heart rate variability physiological parameter value changes with time According to the judgment result, the category of autonomic nervous function status is obtained. 14. The biofeedback system according to claim 11, further comprising: a stimulation signal generation unit, which generates and continuously outputs a stimulation signal with a set frequency range. 15. The biofeedback system as claimed in claim 11, wherein said testing unit comprises: The data acquisition unit acquires heart rate data according to the bioelectrical signal; a processing unit, processing the heart rate data to obtain frequency domain heart rate data; The physiological parameter calculation unit calculates the heart rate variability physiological parameter value according to the frequency domain heart rate data. 16. The biofeedback system as claimed in claim 15, wherein said data acquisition unit comprises: a preprocessing unit, amplifying and filtering the bioelectric signal; A detection and shaping unit, detecting the QRS wave group in the bioelectric signal, and shaping the QRS wave group to obtain an R wave signal; The analog-to-digital conversion unit performs analog-to-digital conversion on the R wave signal and the amplified and filtered bioelectrical signal to obtain a digital signal; The heart rate data calculation unit performs calculation based on the digital signal to obtain heart rate data. 17. The feedback system according to claim 16, wherein the pre-processing unit comprises at least one amplification unit and a filtering unit. 18. The feedback system according to claim 16, wherein the detection and shaping unit comprises a detection unit and a filtering unit. 19. The feedback system according to claim 18, wherein the detection unit comprises a differential circuit and a full-wave detection circuit. 20. The feedback system as claimed in claim 15, wherein said processing unit comprises: a data grouping unit, for grouping the heart rate data; A mean value unit, which calculates the mean value of each set of heart rate data obtained; A difference calculation unit, which calculates the difference between the value of each heart rate data in each group and the obtained average value of the heart rate data of the group; The frequency domain data calculation unit obtains the frequency domain heart rate data according to the obtained difference. 21. The feedback system as claimed in claim 15, wherein the physiological parameter calculation unit comprises: A frequency interval calculation unit, which calculates the frequency interval between the heart rate data in the frequency domain; The frequency power calculation unit calculates the frequency power of the spectrum segment where each physiological parameter value of the heart rate variability is located according to the frequency interval; The normalized value calculation unit calculates a frequency power normalized value and a frequency power ratio according to the frequency power. 22. A biofeedback system for autonomic nervous function comprising: The data acquisition unit processes the detected bioelectrical signal to obtain time-domain heart rate data within a predetermined time interval; A processing device that converts the time-domain heart rate data into frequency-domain heart rate data, obtains heart rate variability physiological parameter values based on spectrum analysis and calculation of the frequency-domain heart rate data, and according to the heart rate variability physiological parameter values, Output the corresponding reminder signal; an output unit, for outputting the reminder signal. 23. The biofeedback system according to claim 22, wherein the processing device further comprises a stimulation signal generating unit, which generates and continuously outputs a stimulation signal in a set frequency range.

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