CN115998259A - Biofeedback training system based on HRV - Google Patents
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Abstract
The invention relates to a biological feedback training system based on HRV, which comprises a data acquisition, data processing and feedback training module. The data acquisition module acquires electrocardio and respiratory signals, and the data processing module calculates corresponding physiological indexes. The feedback training module comprises a resting feedback training module and a pressure feedback training module, and the resting feedback training can guide the trainee to adjust the mood and breath, so as to help the trainee to master the self-adjusting skills. The pressure feedback training module combines the voice hearing to simulate a pressure scene, and compared with feedback training in a rest scene of the main stream on the market, the pressure feedback training module can more effectively help a trainee to improve the multi-task performance in the pressure scene so as to obtain better results. The two training modes complement each other, and have important significance for relieving pressure, regulating emotion and improving life quality.
Description
Technical Field
The invention belongs to the field of physiological biofeedback systems, and particularly relates to a biofeedback training system under multiple scenes.
Background
The busy and fast rhythm of the modern society makes the incidence of mental health problems increasingly higher and the mental health problems increasingly prominent. The traditional consultation psychological doctor or drug therapy has various inconveniences, such as questionnaires or consultations adopted during pressure evaluation, excessively depends on subjectivity of patients, and can lead the patients to face the problem of privacy disclosure; in addition, drug therapy is often associated with side effects in addition to being expensive. Compared with the conventional treatment methods, the biofeedback treatment method is not only low in cost but also has no side effect on the patient, and thus, in many countries, particularly developed countries, biofeedback has been introduced into clinical treatment and its effectiveness has been confirmed in various aspects.
Biofeedback is described as a "psychophysiological mirror" that allows a patient to monitor and learn physiological signals produced by the body. In some mental disorders, such as anxiety, depression, and schizophrenia, etc., its efficacy has been demonstrated. In general, biofeedback is to train a user to correctly adjust or control his body functions by using feedback information, and the user is recovered through continuous training.
The heart is an important organ of the human body and the heart rate has a significant impact on the health of the individual. The heart rate of an individual is adapted to the physical needs of oxygen and may be contributed by the individual's movements, rest, and other activity levels. Furthermore, stressors also cause fluctuations in heart rate, caused by release of epinephrine and cortisol, and accompanied by increased heart rate are redirection of blood flow to the muscular system, release of fat into the blood stream for use as energy, an increase in respiratory rate, muscle tone and an increase in blood clotting capacity. In some scenarios, these reactions are beneficial, such as combat or escape, but for pressures imposed in the individual's daily life, these reactions are negative and even severely affect the individual's health.
The biofeedback training method based on electrocardiosignals is to study bad emotion and mental stress according to the enhancement of sympathetic nerves and the weakening of vagus nerves of autonomic nerves of a human body. The change of the autonomic nerve function of the human body is mainly reflected by HRV, and the height of the HRV is in direct proportion to the activity of the vagus sensory nerve. Autonomic nerves regulate the heart and lung system, digestive system, and urinary system of people. The state of the autonomic nervous system can be inferred from the operating states of the three systems. In addition, the cardiopulmonary system has unique operation rules, the operation rhythms of the cardiopulmonary system are easy to measure, and the breathing rhythms of the cardiopulmonary system are easy to control. Therefore, we can monitor the running state of the cardiopulmonary system to infer the autonomic regulation state, and then guide the regulation rhythm of the autonomic nerve to a regular direction through the regulation of respiration.
Studies have shown that individuals with higher resting HRV tend to exhibit better sustained attention, working memory, and cognitive flexibility in different tasks. In addition, individuals with higher resting HRV tend to have more aggressive emotional arousal and better regulation and response when dealing with stress scenarios. At present, most of researches focus on the effect of resting HRV on cognitive control and emotion regulation, but neglect the effect of HRV in pressure scenes, and in some complex scenes, particularly in pressure sources needing to be participated in, the research of the pressure scenes HRV is the first choice.
Disclosure of Invention
The invention provides a biofeedback training system based on HRV, which aims to solve the problems of high economic cost, inaccurate pressure evaluation and the like of the traditional medical treatment on psychological disease treatment, and also considers and optimizes the current situation that the current biofeedback training based on HRV is mainly concentrated on resting HRV, thereby supplementing a biofeedback training scheme under a pressure scene.
In order to achieve the purpose, the invention provides a biological feedback training system based on HRV, which comprises a data acquisition module, a data processing module and a biological feedback training module, wherein the biological feedback training module comprises two modes of resting feedback training and pressure feedback training. The acquisition module acquires electrocardiosignals and respiratory signals of a user simultaneously, and the data processing module extracts corresponding physiological indexes such as IBI, HRV, biofeedback components and the like from the acquired signals. And the user autonomously selects a resting feedback training mode or a pressure feedback training mode to perform feedback training.
Preferably, the data acquisition module adopts an ADS1292 acquisition chip, and three leads are used for simultaneously acquiring two physiological electric signals of electrocardio and respiration, so that the data acquisition module has the characteristics of high precision and low power consumption.
Preferably, the data processing module processes the collected electrocardio signals and respiratory signals in real time, and the electrocardio signals calculate the physiological indexes of IBI and HRV in real time; biofeedback scores are calculated from respiratory signals in real time.
Preferably, the feedback training module includes feedback training in a resting scene and feedback training in a pressure scene, and before the feedback training in the pressure scene starts, the trainee should perform feedback training in the resting scene first, and through respiratory training, the trainee is enabled to master the autonomous adjustment skill.
Preferably, the resting feedback training module comprises a separate program module, the main interface comprises an IBI real-time curve, an HRV real-time curve and a circle animation, the circle in the animation can be contracted and expanded, the frequency is equal to the breathing frequency to be trained, and the user can directly set the breathing frequency on the interface.
The rest feedback training step comprises the following steps:
the user sets the breathing frequency, adjusts the breathing rhythm according to the circular animation on the interface, and breathes when the circle in the animation contracts, and breathes when the circle in the animation expands;
simultaneously, the respiratory training is carried out, and real-time IBI and HRV waveforms on an interface are observed;
different respiratory frequencies are set, training is repeated, the IBI waveform is more regular, the respiratory frequency of the rising HRV curve is more close to the training target, training is repeated for multiple times, and the ideal HRV of a user is found.
Preferably, the pressure feedback training module comprises a complete training scheme, and is characterized in that the trainee is guided to perform English hearing test, the hearing is provided with a difficulty gradient, the difficulty gradient is divided into 3 grades, and the performance of the trainee is quantified according to the total score of the answered questions. The physiological signals of the user are obtained in real time while hearing is achieved, and biofeedback scores are calculated; noise is added into the hearing earphone of the trainee, the size of the noise is adjusted in real time by the feedback score, when the hearing difficulty is increased, the psychological pressure of the trainee is increased, the feedback score is reduced, the noise in the hearing earphone can be increased, and the trainee needs to adjust autonomously, such as deep breathing, relieving pressure and reducing the noise in the hearing in order to obtain better performance.
Preferably, the biofeedback score represents the extent to which the trainee's current physiological state matches the target physiological state, the calculation of which depends primarily on the power spectrum of the respiratory signal. For the characteristics of the respiratory signal, the frequency domain characteristic of the respiratory signal can be obtained by short-time Fourier transform (short-time Fourier transform, STFT), and the core idea is that the computing formula of the respiratory signal is as follows by multiplying a window function h (t) before Fourier transform:
the respiratory signal can also be regarded as a noisy periodic signal, so that an autocorrelation analysis processing method can also be adopted, and the formula is as follows:
wherein x is i For signal time series, x i+τ For a time series of shifted τ units, μ is the mean, σ 2 Is the variance.
The whole processing is to maintain a dynamic window for the respiration signals in the time domain, process the data in the window at regular intervals to obtain the power spectrum, integrate the power spectrum curve, integrate the area outside the target respiration frequency (4-12 per minute) and the area in the target respiration frequency respectively, calculate the ratio, and normalize the ratio to the range of 0 to 1 as the feedback input parameter.
Preferably, the data acquisition module and the data processing module adopt wired serial port communication or wireless Bluetooth communication and a custom communication protocol, and the upper computer can send instructions to change the embedded hardware setting, so that the safety and convenience of the system are ensured.
The HRV-based biofeedback training system has the following advantages:
the HRV-based biofeedback training system provided by the invention is a digital medical product, and has various advantages compared with the traditional treatment method. In the traditional treatment process, patients often interfere with the judgment of doctors due to privacy or subjective unconscious self-beautifying expression, and the biofeedback training system effectively relieves the problems through the pressure evaluation of the physiological indexes; while traditional drug treatment is often accompanied by side effects, the biofeedback training system of the invention is safe to the mind and body; in addition, the biofeedback training system reduces the medical cost and is more economical and more civilian.
In addition, compared with a general biofeedback training system, the biofeedback system comprises the biofeedback training under a pressure scene. In the aspect of psychological diagnosis, most researchers focus on biofeedback training in a resting scene, and little research is done on biofeedback training in a stress scene. In fact, for emotion adjustment and stress relief, biofeedback training in stress scenes is the first choice, and can help trainees to relieve anxiety and improve performance more effectively.
Drawings
FIG. 1 is a diagram of an HRV-based biofeedback training system architecture.
Fig. 2 is a collector connection diagram.
Fig. 3 is an electrocardiographic wave group diagram.
Detailed Description
The invention will now be described in detail with reference to the accompanying drawings, it being pointed out that the examples described are only intended to facilitate an understanding of the invention and do not serve as a limitation.
As shown in fig. 1, the HRV-based biofeedback training system includes a data acquisition module, a data processing module, and a biofeedback training module, which is further divided into a rest feedback training module and a pressure feedback training module. By using three bioelectrodes, the electrocardiograph can be connected with the left chest, the right chest and the right abdomen of a human body, and the electrocardiograph and the respiratory signals can be acquired in real time as shown in figure 2. The data acquisition module and the data processing module are in data transmission through USB serial port communication or wireless Bluetooth communication, and the data processing module processes the acquired physiological electric signals to obtain parameters such as real-time IBI, HRV, biofeedback scores and the like. IBI and HRV are presented by waveform diagrams showing the interface, and biofeedback scores are presented by the amount of noise in the earpiece during stress training.
And a data acquisition module: the hardware collector selects an ADS1292 chip as a collecting chip, ARM as a main control chip, and upper computer software can send instructions to the collector through a serial port communication protocol to perform necessary hardware settings on the collector, such as sampling rate setting, gain setting and lead falling detection.
And a data processing module: and respectively processing the electrocardiosignals and the respiratory signals obtained by the collector to obtain the required physiological parameter indexes. HRV, heart rate variability, refers to the change in instantaneous heart rate over time. The electrocardiographic signal often contains a set of complexes, as shown in fig. 3, mainly consisting of QRS complex, P wave, T wave, and in some cases a U wave appears after T wave. When the instantaneous heart rate is obtained by the electrocardiosignal, R waves are extracted from the electrocardiosignal, and the time interval between two adjacent R waves is called RR interval, namely parameter IBI, and the inverse of the RR interval is used for representing the instantaneous heart rate. Thus, the core algorithm for electrocardiosignal processing is an algorithm for R wave detection, and a differential threshold method can be adopted. The differential threshold method is a rapid algorithm suitable for detecting the QRS waves of the electrocardiosignal with higher real-time performance, and the basic principle is as follows: since the QRS wave is the place where the waveform of the electrocardiograph signal changes most severely, the rising slope or falling slope of the waveform is significantly different from that of other waveforms, so that the position of the R wave can be detected by detecting the derivative of the electrocardiograph signal sequence with respect to time, i.e., the change condition of the slope. Generally, the rising edge and the falling edge of the R wave are the regions with the greatest change of the slope of the electrocardiographic waveform, and the first derivative zero crossing and the second derivative extreme point which occur in the regions, namely the R wave position to be detected by us. And carrying out first-order difference or second-order difference on the multi-filtered electrocardiosignals and combining the first-order difference or the second-order difference with a determined threshold value to detect R waves. The result of processing the respiratory signal is mainly a feedback score. The feedback score indicates how well the trainee's current physiological state matches the target physiological state. The processing algorithm is mainly to maintain a window with a certain time length, such as 30 seconds, process data in the window once every fixed time interval, calculate the power spectrum of the breathing signal, integrate the power spectrum curve, integrate the area outside the target breathing frequency (4-12 per minute) and the area in the target breathing frequency respectively, calculate the ratio, and finally normalize the ratio to the range of 0 to 1 as the feedback input parameter.
And a rest feedback training module: mainly comprises training auxiliary software and a training method. The auxiliary software interface displays an HRV real-time curve and an IBI real-time curve. In addition, the device also comprises a training aid animation, namely a circle which can shrink and expand, the shrinking and expanding speed of the training aid animation is synchronous with the breathing rate, and the breathing rate can be manually adjusted on a software interface.
Further, the specific training process of the resting feedback training module is as follows:
step one: allowing the trainee to be in a calm and steady state, and connecting the collector to the trainee;
step two: transmitting instructions from software to a collector, and setting necessary hardware parameters such as sampling rate, gain and the like;
step three: setting a target HRV range in a rest feedback training module of software, and selecting the breathing rate required to be trained;
step four: the trainee performs breathing control training according to the auxiliary training animation, when the circle in the auxiliary training animation contracts, the trainee exhales, when the circle in the auxiliary training animation expands, the user inhales, and the cycle is repeated for a period of time;
step five: changing the respiration rate, repeating step four again, and focusing on the HRV and IBI real-time curves, the higher the respiration rate of the HRV curve is, the closer to our training goal. Repeating for a plurality of times, and searching for the ideal breathing rate of the trainee.
The pressure feedback training module: mainly comprises a set of pressure feedback training methods. A pressure scene is constructed by playing hearing understanding, and a plurality of difficulty gradients can be divided for hearing from the speech speed and the content of hearing, so that the pressure gradient of the pressure scene is constructed. In addition, by setting a certain choice question for the hearing content, the correct rate of the choice question can effectively quantify the performance of the trainee under the pressure scene. Meanwhile, the trainee is monitored in real time for physiological parameters, a real-time feedback score of the trainee is obtained, the feedback score is used as one of input parameters of the hearing, and the size of artificially added noise in the hearing is controlled. When the difficulty of the hearing content is increased, the pressure sensed by the trainee is increased, if the trainee reacts to physiological tension and the like, the real-time feedback score of the trainee is reduced, so that the noise in the hearing earphone is increased, the trainee needs to manually perform self-regulation on the physiological emotion, the pressure emotion is reduced, the feedback score is increased, the noise is reduced, a better hearing environment can be obtained, and the performance under the pressure scene is improved.
Further, the specific feedback training process under the pressure scene is as follows:
step one: attaching the collector to the trainee and taking the hearing device to the trainee;
step two: transmitting instructions from software to a collector, and setting necessary hardware parameters such as sampling rate, gain and the like;
step three: hearing is played, and meanwhile, a trainee can answer questions while self-adjusting;
step four: changing the difficulty of hearing, and repeating the third step;
step five: counting the accuracy of the answer questions under each corresponding difficulty, and quantifying the performance of the trainee;
whether it is biofeedback training in resting or stress situations, long-term treatment should be adhered to and the time per training should not be too short. In addition, before biofeedback training in a pressure scene, the trainee should be subjected to biofeedback training in a rest scene, so that the trainee can grasp some self-adjusting skills in advance, thereby improving the task performance in the pressure scene.
Claims (8)
1. HRV-based biofeedback training system, characterized in that: the system comprises a data acquisition module, a data processing module and a biofeedback training module, wherein the biofeedback training module comprises a resting feedback training mode and a pressure feedback training mode, the acquisition module acquires electrocardiosignals and respiratory signals of a user at the same time, the data processing module extracts corresponding physiological indexes such as IBI (IBI), HRV (high resolution video) and biofeedback score from the acquired signals, and the user autonomously selects the resting feedback training mode or the pressure feedback training mode to perform feedback training.
2. The HRV-based biofeedback training system according to claim 1, wherein the data acquisition module adopts an ADS1292 acquisition chip, and three leads are used for simultaneously acquiring two physiological electrical signals of electrocardio and respiration, so that the system has the characteristics of high precision and low power consumption.
3. The HRV-based biofeedback training system according to claim 1, wherein the system data processing module processes the collected electrocardiograph and respiratory signals in real time, and calculates IBI and HRV physiological indexes from the electrocardiograph signals in real time; biofeedback scores are calculated from respiratory signals in real time.
4. The HRV-based biofeedback training system of claim 1, wherein the feedback training module comprises a feedback training in a resting scene and a feedback training in a stress scene, and the trainee should be allowed to perform the feedback training in the resting scene before the feedback training in the stress scene starts, and the trainee is allowed to grasp the autonomic adjustment skills through the respiratory training.
5. The HRV-based biofeedback training system according to claim 1, wherein the resting feedback training module comprises an independent program module, the main interface comprises an IBI real-time curve, an HRV real-time curve, and a circular animation, the circle in the animation can be contracted and expanded, the frequency is equal to the breathing frequency to be trained, and the user can directly set the breathing frequency on the interface;
the rest feedback training step comprises the following steps:
the user sets the breathing frequency, adjusts the breathing rhythm according to the circular animation on the interface, and breathes when the circle in the animation contracts, and breathes when the circle in the animation expands;
simultaneously, the respiratory training is carried out, and real-time IBI and HRV waveforms on an interface are observed;
different respiratory frequencies are set, training is repeated, the IBI waveform is more regular, the respiratory frequency of the rising HRV curve is more close to the training target, training is repeated for multiple times, and the ideal HRV of a user is found.
6. The HRV-based biofeedback training system according to claim 1, wherein the pressure feedback training module comprises a complete training scheme, and is characterized in that the trainee is guided to perform an english hearing test, the hearing is provided with a difficulty gradient, the difficulty gradient is divided into 3 levels, the performance of the trainee is quantified according to the total score of the answered questions, the physiological signal of the user is obtained in real time while the hearing is achieved, and the biofeedback score is calculated; noise is added into the hearing earphone of the trainee, the size of the noise is adjusted in real time by the feedback score, when the hearing difficulty is increased, the psychological pressure of the trainee is increased, the feedback score is reduced, the noise in the hearing earphone can be increased, and the trainee needs to adjust autonomously, such as deep breathing, relieving pressure and reducing the noise in the hearing in order to obtain better performance.
7. The HRV-based biofeedback training system according to claim 1, wherein the biofeedback score represents the matching degree of the current physiological state and the target physiological state of the trainee, the calculation of which mainly depends on the power spectrum of the respiratory signal, and for the characteristics of the respiratory signal, the short-time fourier transform (short-time Fourier transform, STFT) can be used to obtain the frequency domain characteristics thereof, and the core idea is that the window function h (t) is multiplied before the fourier transform, and the calculation formula is as follows:
the respiratory signal can also be regarded as a noisy periodic signal, so that an autocorrelation analysis processing method can also be adopted, and the formula is as follows:
wherein x is i For signal time series, x i+τ For a time series of shifted τ units, μ is the mean, σ 2 Is the variance;
the whole processing is to maintain a dynamic window for the respiration signals in the time domain, process the data in the window at regular intervals to obtain the power spectrum, integrate the power spectrum curve, integrate the area outside the target respiration frequency (4-12 per minute) and the area in the target respiration frequency respectively, calculate the ratio, and normalize the ratio to the range of 0 to 1 as the feedback input parameter.
8. The HRV-based biofeedback training system according to claim 1, wherein the data acquisition module and the data processing module adopt wired serial communication or wireless Bluetooth communication, a communication protocol is defined, and the upper computer can send instructions to change the embedded hardware setting, so that the safety and convenience of the system are ensured.
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