CN114099893A

CN114099893A - Mental health evaluating and deep respiration conditioning system

Info

Publication number: CN114099893A
Application number: CN202010860135.0A
Authority: CN
Inventors: 吴健康; 王红亮; 于晓菲
Original assignee: Zhongke Digital Health Research Institute Nanjing Co ltd
Current assignee: Zhongke Digital Health Research Institute Nanjing Co ltd
Priority date: 2020-08-25
Filing date: 2020-08-25
Publication date: 2022-03-01

Abstract

The invention relates to a mental health evaluation and deep respiration training system method, which comprises a whole respiration heart rate resonance degree measurement method and a method for guiding a trainee to enter a harmonious state. The mental health evaluating and deep respiration training device provided by the invention can be used for acquiring the heart rate and respiration signals of a trainee in real time, analyzing and calculating the respiration heart rate resonance measurement of the trainee, and guiding the trainee to adjust the mood and respiration and enter a harmonious state. Psychological health evaluation and deep breathing training can effectively adjust the autonomic nerve regulation and control state and reduce the risk coefficient of the heart-lung system; the mental pressure of the sports can be effectively relieved, and the score is improved. Therefore, the invention has important significance for health care and life quality improvement of people.

Description

Mental health evaluating and deep respiration conditioning system

Technical Field

The invention belongs to the technical field of biofeedback rehabilitation, and particularly relates to a wearable autonomic nerve regulation state monitoring and biofeedback rehabilitation system.

Background

The autonomic nervous system has its own rhythms of functioning. It controls the operation and coordination of the activities of the three major systems of the heart and lung system, the digestive system and the urinary system. If the autonomic nervous system becomes dysfunctional, it is liable to cause an imbalance in the function of the associated regulatory system. Chinese traditional sitting meditation, qigong, taiji and the like also want to achieve internal balance and improve the running states of three major systems of a human body through self-regulation. Although this has been proven to be an effective method of health preservation, the intrinsic equilibrium state achieved by meditation, qigong and taiji is not objectively evaluated; each person can make their own efforts, whether their direction is correct or not and whether the method is effective or not, and only can you see intelligence, and "make your way in your own right". The balance of autonomic nerves dominates the physical and mental balance of the human body. Autonomic imbalance seriously affects psychological and physiological problems of the human body.

As the pressure of modern life increases, people with emotional problems tend to become bloated. From the initial depressed mood, anxiety, and further to depression. The survey in 2018 shows that the number of global depression patients reaches 3.5 million people, and the number of suicide due to depression is up to 100 ten thousand every year. This figure is growing faster as time progresses. People feel unprecedented survival pressure in the world spread of epidemic situations. Anxiety is affecting people's life as a psychovirus in combination with new coronaviruses. There have been a number of studies that indicate that the root cause of emotional problems is an imbalance in the autonomic nervous system. Therefore, the scientific and effective assessment of the balance state of the autonomic nervous system and the conditioning through personalized intervention are effective means for diagnosing and treating psychological problems such as depression, insomnia, anxiety and the like.

The heart rate variability biofeedback technology originated in the former Soviet Union in the last 70 th century and was subsequently taught by Lehter in the United states for the 90 th century for further research and popularization in the United states. The research of the biofeedback has both theoretical significance and practical value: the method breaks through the limitation that the organs governed by the vegetative nervous system can not be learned and can not be controlled randomly in the conventional learning theory, and opens up a new field of viscera learning; it integrates many subjects such as psychology, physiology, clinical medicine and modern electronics, and breaks through the limitation of traditional therapeutics. It features strong pertinence and curing the diseases in the level of pathogenic causes. In addition, biofeedback is widely concerned with its characteristics of no damage, no pain, and no side effects of drugs, and provides a new means and method for treating many diseases. HRV biofeedback was created in 1975 by russian vaschllo and was subsequently made more lucrative in the united states and europe by the teaching of Lehrer in the united states. Research on American NIH, Toronto university in Canada, university of Pavia Italy, university of Japan, and Zhongshan university in China and the like proves that the heart rate variability biological feedback has obvious curative effects on asthma, hypertension, heart failure, coronary heart disease, low mood of patients and the like, and has the effects of relieving and improving the performance on competitive psychological pressure.

However, the autonomic nervous system has its complexity and uncontrollable nature. From the viewpoint of system science, the control of the cardiopulmonary system by the autonomic nervous system is a typical closed-loop automatic control system. How to estimate the operating state and rhythm of the autonomic nervous system from the measured parameters of the system, and how to direct the autonomic nervous system from imbalance and ataxia to imbalance and harmony, is a challenging issue.

Chinese patent 200410039589.2 "feedback abdominal respiration training instrument" calculates the quantitative expression value of respiratory sinus arrhythmia from the electrocardiogram and respiratory wave, and displays the value to the trainee in visual or auditory way together with the heart rate, blood pressure and blood oxygen saturation as feedback parameters, so that the trainee can know whether the respiratory mode is correct or not immediately. However, it does not give a quantitative expression of sinus arrhythmia very effective. U.S. Pat. No. 8, 8,442,632 for Method and apparatus for manipulating the autonomic nerve system states that the use of respiration and heart rate can affect autonomic nerve rhythms, but implementation details are not given. The Method and apparatus invented in U.S. Pat. No. 8, 8,066,637 for characterizing physiological coherence and automatic balance, uses a spectral distribution to analyze heart rate variability to determine the rhythmic state of the body's biological system. Us patent 8,428,702 Methods and devices for reliving stress provides a device that is convenient to use, assesses stress in a person by measuring respiratory sinus arrhythmia in real time, and helps to relieve stress.

Admittedly, the above patents all propose methods and devices for assessing and regulating the autonomic rhythm using heart rate variability, but none of them systematically present which measurement parameters to assess the operating state and rhythm of the autonomic nervous system, how to direct the autonomic nervous system from disorder and ataxia to disorder and harmony.

Disclosure of Invention

The mental health evaluating and deep respiration training system applies autonomic nerve evaluating and regulating technology to the field of mental diseases, provides a method for diagnosing mental diseases such as depression, bidirectional affective disorder and the like, further guides the autonomic nervous system to be in disorder and random turning to rule and harmony, and provides a system and equipment for mental health evaluating and deep respiration training.

For the purposes of the present invention, two concepts and terms are now described below: "Heart rate variability" refers to the variation of instantaneous heart rate over time. When the instantaneous heart rate is obtained from the electrocardiosignal, R-waves are often extracted from the electrocardiosignal and expressed by the interval between two adjacent R-waves, i.e. the reciprocal of the RR interval. In many cases we replace the instantaneous heart rate with RR intervals, also known as "NN intervals". The key point of the invention is to provide a whole set of respiratory heart rate resonance values so as to quantitatively measure the degree of the regulation and control state of the autonomic nervous and heart-lung system from ataxia to harmonious. Therefore, we also refer to it as the "respiratory heart rate resonance metric", abbreviated "harmonicity metric". In the following description, different concepts and terms are used in different contexts, which do not affect or limit the content of the invention.

To achieve the above object, the present invention provides a mental health assessment and deep respiration training system method, which comprises: a set of respiratory heart rate resonance metric metrics based on a Heart Rate Variability (HRV) curve and a respiratory signal, comprising: the power spectral density function of the heart rate variability HRV, the respiratory center frequency, the bandwidth, and a respiratory heart rate resonance measure calculated from their combination.

A set of biofeedback exercise training programs comprising: a set of deep breath training programs comprising:

guiding the trainee to smoothly and slowly breathe according to a given frequency by using balloon animation and voice on a display screen, and selecting and playing corresponding language and music according to the current harmony measure of the trainee, so as to guide the relaxation of the body and the mood, control the idea, and reduce the anxiety and the negative idea; displaying HRV waveform and training score on a display screen, and guiding the trainee to move towards the correct direction; searching the optimal breathing rate which is personalized and harmonious by utilizing the training history file of the trainee;

preferably, a set of respiratory heart rate resonance metrics based on Heart Rate Variability (HRV) and respiratory signal is defined as:

wherein psd (k) is a power spectral density function of the heart rate variability HRV. k is a radical of_rIs the respiratory center frequency, k_bIs the bandwidth.

Preferably, the set of deep breathing training programs comprises: for a trainer, searching the optimal breathing rate which is personalized to reach harmony by using the training history file of the trainee, and guiding the trainee to smoothly breathe slowly according to the given frequency by using balloon animation and voice on a display screen; according to the current harmony measure of the trainees, selecting to play corresponding languages and music, guiding to relax the body and emotion, controlling the idea, and reducing anxiety and negative thinking; displaying HRV waveform and training score on a display screen, and guiding the trainee to move towards the correct direction;

preferably, the set of training program for biofeedback exercise further comprises: for each trainee, the optimal respiratory resonance frequency was found: for an initial trainer, making the respiration frequency per minute be 6, namely the respiration period be 10 seconds, carrying out deep respiration training for 20-30 minutes once, and recording the total harmony measure and each harmony measure; fine-tuning the breathing cycle up and down, such as: 11 seconds, 9 seconds and the like, deep breathing training is carried out, and the overall harmony degree measurement and each harmony degree measurement are recorded; analyzing the harmonicity measurement under different respiratory periods to find out the optimal respiratory rate or the optimal trend; and determining the current training parameters according to the historical records and the analysis results.

The invention also provides mental health evaluating and deep breathing training equipment, which comprises: the signal acquisition module is wearable equipment comprising an electrocardio electrode/PPG sensor and a respiratory sensor, acquires electrocardiosignals or PPG signals and respiratory signals, and transmits the signals to a mobile phone APP; and calculating the respiratory heart rate resonance degree measurement by the aid of the mobile phone APP, guiding deep respiration training, and managing a training report.

The signal acquisition module adopts and is microelectronics hardware wearable equipment, and its structure and sensor and signal acquisition and transmission unit that include, according to the demand, have following two kinds of configurations:

two electrocardio-electrodes and corresponding amplifying, filtering and A/D converting circuits, and a respiration sensor. With this configuration, the signal acquisition and transmission unit extracts RR intervals and heart rate from the electrocardiographic signal; the respiration signal is obtained from a respiration sensor of a bioelectrical impedance technology;

using a finger-worn photoplethysmography PPG sensor and corresponding amplification, filtering and a/D conversion circuitry, and a respiration sensor. With this configuration, the signal acquisition and transmission unit extracts the heart rate signal from the PPG signal; the respiration signal is obtained from a respiration sensor of a bioelectrical impedance technology;

preferably, the signal acquisition module comprises: acquiring electrocardiosignals, detecting R waves from the electrocardiosignals, acquiring PPG signals, extracting heart rate signals from the PPG signals, and acquiring respiratory signals;

preferably, the mobile phone APP calculates the RR interval or the heart rate signal from the signal acquisition module and the respiratory signal to obtain the respiratory heart rate resonance degree metric. Further, the trainees are guided to train according to the heart rate resonance degree measurement;

for a trainee, calling out personal data and historical training records, training schemes and execution conditions thereof from the personal files and the historical records, and finding out the individualized optimal breathing rate according to the historical training records; according to the current harmony measurement of the trainee, selecting to play corresponding language and music to guide physical and mental relaxation, and guiding the trainee to smoothly breathe slowly according to a given frequency by using balloon animation and voice on a display screen;

for each training, all information is recorded, analyzed, stored and uploaded in real time, including: respiratory rate, RR interval and respiratory signal, HRV waveform, respiratory heart rate resonance measure;

and summarizing each training, generating a training report and presenting the training report to the trainee.

According to the mental health evaluation and deep respiration training system method and device, the wearable device and the handheld terminal are in wireless Bluetooth communication, the product is convenient to use and high in flexibility, and the adaptive scene is wide.

The invention relates to a mental health evaluation and deep respiration training system method and equipment, which are characterized in that:

1) electrocardiosignals or PPG signals and respiratory signals are monitored in real time, and a whole set of respiratory heart rate resonance degree measurement is extracted from heart rate variation signals and respiratory signals.

2) The trainee is guided to adjust the mood and the respiration in multimedia modes such as animation, graph, language, music and the like, and the respiration heart rate resonance measurement is fed back to the trainee in a graph and digital mode to guide the trainee to adjust and control towards the positive direction.

3) Storing and analyzing the heart rate variability power spectrum density, the bandwidth and the respiratory center frequency of the trainee to obtain the respiratory heart rate resonance degree measurement, and searching an individualized autonomic nerve harmony regulation and control optimal individualized training method by a system intelligent method.

The mental health evaluating and deep breathing training system comprises: the wearable signal acquisition module synchronously acquires electrocardiosignals or PPG signals and respiratory signals in real time through the electrocardioelectrode/PPG sensor and the respiratory sensor, and transmits the electrocardiosignals or the PPG signals and the respiratory signals to the mobile phone APP through the signal acquisition and transmission unit. The mobile phone APP obtains the respiratory heart rate resonance measurement through an algorithm, the optimal respiratory frequency is optimized according to the measurement and training files of a trainer, and the optimal respiratory frequency is fed back to the deep respiration training guide unit. The deep respiration training guiding unit guides the trainee to smoothly and slowly breathe by a balloon animation graph, plays corresponding guiding language and relaxing music according to the harmonious level divided by the respiration heart rate resonance measurement, adjusts the mood and the respiration mode of the trainee and guides the trainee to reach or approach the harmonious state. The training report management unit manages trainee data, maintains trainee files, assists trainees to insist on training, keeps a good autonomic nerve regulation state and has healthy life quality.

Drawings

FIG. 1 is a block diagram of a mental health assessment and deep breath training system according to the present invention;

Detailed Description

The present invention will be described in detail below with reference to the accompanying drawings, and it should be noted that the described embodiments are only intended to facilitate understanding of the present invention, and do not limit the present invention in any way.

Referring to fig. 1, the present invention is a wearing real-time physiological monitoring, analyzing and feedback system hardware and software, as shown in the block diagram of the mental health assessment and deep breathing training system of the present invention. The whole mental health evaluating and deep respiration training system is composed of a wearable signal acquisition module 100 and a mobile phone APP 200. The signal acquisition module is composed of an electrocardio-electrode or PPG sensor 111 and a respiration sensor 112. When the electrocardio-electrode is worn, the electrocardio-electrode is worn near the heart and keeps a certain distance so as to ensure the strength of electrocardiosignals and the accuracy of R wave detection later; the PPG sensor is worn at the finger tip. The signal acquisition and transmission module 110 amplifies, filters and converts the super signals, packs all acquired signals according to a data transmission protocol, and transmits the signals to the mobile phone APP200 through bluetooth.

The cell phone APP200 is composed of a respiratory heart rate resonance measurement unit 210, a deep breathing training guidance unit 220, and a training report management unit 230. The respiration heart rate resonance measurement 210 calculates the RR interval or heart rate signal from the signal acquisition module and the respiration signal, obtains the respiration heart rate resonance measurement, and sends the respiration heart rate resonance measurement to the deep respiration training guidance unit. The deep respiration training guiding unit 220 is a human-computer interaction module, and on one hand, it presents the current respiration heart rate resonance measurement to the trainee in the form of graph and digital measurement to complete a feedback path; on the other hand, it infers the harmonious rhythm of the trainee and guides the respiratory rhythm of the trainee by using animation pictures such as a balloon and the like; meanwhile, the trainees are prompted to relax the mood by voice and music, and then slowly breathe deeply to enter a harmonious state regulated by an autonomic nervous, heart and lung system. The training report management unit 230 records various parameters throughout the biofeedback process, generates reports, and saves the reports to a personal profile and history.

The following detailed description of embodiments of the invention:

signal acquisition module 100

As shown in fig. 1, the signal acquisition module 100 is a wearable microelectronic hardware component, driven by a firmware program, and transmits acquired signals to the signal processing and analyzing unit 200 through bluetooth, and interacts with the signal processing and analyzing unit 200 to complete authentication and management tasks.

The respiration signal is acquired by a sensor of the respiration signal, such as, but not limited to, bioelectrical impedance technology (EIP). It uses the principle that the electric characteristics (impedance, admittance, dielectric constant, etc.) of biological tissue and organ and their change are related to respiration, and utilizes the electrocardio-electrode to feed small alternating current into two points of chest to detect correspondent electric impedance and its change so as to obtain respiration signal.

One implementation scheme of the electrocardiosignal acquisition 111 is to acquire a single-lead electrocardiosignal by using two electrode plates, and the other scheme is to use a photoplethysmogram PPG sensor to be worn on a finger to acquire a pulse wave signal and a respiratory signal.

(II) Mobile phone APP200

The mobile phone APP operates at the mobile phone end. The respiratory heart rate resonance measurement unit is composed of a respiratory heart rate resonance measurement unit 210, a deep respiration training guide unit 220 and a training report management unit 230.

When the trainee begins deep breathing training, he wears signal acquisition module, opens cell-phone APP200, utilizes display screen and sound system on the equipment, sits up and begins the training.

The respiration heart rate resonance measurement unit 210 obtains the electrocardiographic or PPG signal and the respiratory signal from the signal acquisition module, and performs algorithm processing on the signals. The heart rate analysis core algorithm is a high-performance R wave detection algorithm. To reduce false positives, it identifies and detects QRS structures (QRS Complex). There are well established algorithms for this, such as: huabin Zheng, Jiankang Wu, A Real-Time QRS Detector Based on characterization Wavelet Transform and current Spline Interpolation, Journal of Telemedicine and e-Health, Vol.14, ISS.8,2008, pp.809-815. The method of obtaining the heart rate from the PPG signal is similar to the electrocardiogram signal analysis method. For the processing of the respiration signal, if the respiration signal is obtained from electrocardiogram, acceleration, Photoplethysmogram (PPG). The frequency characteristic of the respiratory signal is different from other signals, and the respiratory signal is extracted by using methods such as band-pass filtering and the like. If the acceleration signal is used, the three-axis accelerometer is worn in front of the chest, the chest expands and contracts due to respiration, the accelerometer detects the forward acceleration perpendicular to the chest, and the forward acceleration signal is subjected to band-pass filtering to obtain the respiration signal.

Since the heart rate information is a non-equally sampled interval time series signal provided in the form of a sequence of RR wave intervals, the respiration signal is an equally sampled interval time series signal. In order to unify them into discrete time series with the same sampling rate, it is necessary to interpolate the RR interval series and resample the two signals to the same time interval. Then, using the RR intervals and the respiratory signals, a set of respiratory heart rate resonance values is calculated, including: standard deviation of RR intervals (SDNN), standard deviation of adjacent RR intervals (SDSD), HRV mid-frequency power spectrum index (Coh), HRV curve to respiratory wave synchronization index (Synch), and overall harmonicity metric integrated from them.

For a trainee, the deep breathing training guidance module 220 calls the personal data and the historical training record of the trainee, the training scheme and the execution condition thereof from the personal file and the historical record 230, sets parameters such as the current training time and the breathing rate, and starts training.

For each training, the store, report, upload module 230 records all information in real time: respiratory frequency and other parameters, RR interval and respiratory signal, respiratory heart rate resonance measurement, harmony measurement for short. Storing and reporting content includes: RR intervals and respiratory signals at the beginning of training, and a measure of harmony; a time point to reach harmony, and RR intervals and respiratory signals, harmony measures; a harmonious retention time; a curve of the measure of harmonicity over time; RR intervals and respiratory signals, harmonicity measures at optimal harmonious states at different respiratory rates.

For the collective training scenario, the storage, reporting, upload module 230 uploads the RR intervals and respiratory signals of the trainee, the harmonicity metric, in real-time. The central server stores a personal profile and a history of each trainee. The training master coach/rehabilitee/doctor selects to display and watch the real-time training condition of each trainee at the client. For a specific trainee, calling out the historical record, analyzing the training trend, finding out the training rule and making a training scheme.

The personalized HRV deep breathing training scheme comprises: the training course is usually 2-4 weeks; training times are usually 1-2 times per day; the training time is usually 20-30 minutes for each time; the respiratory rate in training is usually 6 times per minute; the ratio of exhalation to inhalation times, etc.

(III) measurement of respiratory heart rate resonance

In the harmonious state, the mood is relaxed, the HRV resonates with the respiration, and shows a typical respiratory Sinus Arrhythmia, and the instantaneous heart rate curve forms a smooth curve similar to a sine wave on the waveform display, which is called Rhythm Sine Arrhythmia (RSA). In the RSA state, the HRV value generally reaches the individual maximum value, and the HRV appears to be concentrated in the frequency domain, forming a peak around 0.1 Hz.

Experimental research shows that the respiratory rhythm is controlled at about 6 times per minute, and the harmonious state can be achieved most. To assess the regulation status of the cardiopulmonary system, we move a sliding window of 10-30 seconds in width, i.e., one to three respiratory cycles, over the heart rate signal and the respiratory signal, and calculate the following metric within the window:

a respiratory heart rate Resonance measurement (respitation-Rhythm Resonance RRR). When breathing at a certain breathing rate, the coincidence of the breathing rate with the heart rate, or the value of the resonance level. The degree of concentration of the power spectrum of the heart rate variability HRV around the respiratory frequency was evaluated:

wherein psd (k) is the power spectral density function of HRV. k is a radical of_rIs the respiratory center frequency, k_bFor bandwidth, k may be taken_b＝0.4k_r. E.g. respiratory centre frequency k_r0.1Hz, then k_bThe HRV power spectra of the denominator sum between 0.06Hz and 0.14Hz at 0.04 Hz. The above numerical values are one of the examples, and are only intended to facilitate understanding of the present invention, and do not have any limiting effect thereon.

(V) HRV deep respiration training method

The deep breathing training is carried out by adopting the following measures:

1. the mode of language guidance and music playing is adopted to guide and relax the body and emotion, control the mind and reduce anxiety and negative ideas;

2. for the selected respiratory frequency, a balloon animation is used on a screen to guide smooth slow breathing;

3. displaying HRV waveform, harmony measure and training score on a screen, and guiding the trainee to move towards the correct direction;

4. for each person, a personal training profile is created and the optimum resonant frequency is found.

When a trainee is registered and training is started, the training process is as follows:

A) inquiring the training file, and respectively processing the following three conditions:

a) for the initial trainee with an empty training profile, the number of breaths per minute is 6, i.e. the period is 10 seconds.

b) For trainees who have training files but have training times less than 5 times, the breathing cycle is finely adjusted upwards and downwards according to the past breathing cycle, such as: 10.5 seconds, 9 seconds, 11 seconds, 8 seconds, …, etc., to select a training respiratory cycle.

c) For trainees with training times more than 5 times, harmonious degree measurement curves under different respiratory cycles are drawn. And if the maximum value of the harmonic measure appears in the curve, taking the corresponding breathing cycle as the training breathing cycle. If the curve does not have the maximum value of the harmonious metric, the optimal trend is found, and the respiratory cycle of the training is determined by using a prediction method.

B) Training according to the selected respiratory cycle, calculating, displaying and storing the total harmonious degree metric and other harmonious metrics for each respiratory cycle, namely: standard deviation of RR intervals (SDNN), standard deviation of adjacent RR interval differences (SDSD), HRV mid-frequency power spectrum index (Coh), and HRV wave to respiratory wave synchronization index (Synch).

C) The training states are classified into different classes according to the harmony measures. For example three levels: harmonious, quasi-harmonious and random, and different balloon display colors, guidance prompt commentary and music are configured for different harmonious grades. And playing corresponding guide commentary and music according to the current harmonious level, and using the graph with corresponding color.

D) And when the training time is reached, prompting the end of training. And summarizing the training, comparing the training with the training of the previous time, and generating a report. The report gives RR interval and harmonic measurement curves in the training in the form of numbers and graphs; calculating the maximum harmonious measurement of the harmonious time, the harmonious state holding time and the holding time of more than 5 minutes, wherein the maximum harmonious measurement is used for evaluating the parameters of the training; the three parameters were plotted using different breathing cycles over the course of the training.

Claims

1. A mental health assessment and deep breath conditioning system, comprising:

a measure of respiratory heart rate resonance for assessing a mental health level defined as:

A set of deep breath training programs comprising:

searching the individualized optimal breathing rate which reaches harmony by utilizing the training history file of the trainee;

guiding the trainee to smoothly breathe slowly according to a given frequency by balloon animation and voice on a display screen;

according to the current harmony measure of the trainees, selecting to play corresponding languages and music, guiding to relax the body and emotion, controlling the idea, and reducing anxiety and negative ideas;

displaying HRV waveform and training score on a display screen, and guiding the trainee to move towards the correct direction;

a mental health assessment and deep breath training apparatus comprising: signal acquisition module and cell-phone APP as figure 1.

2. The mental health assessment and deep breath training system of claim 1, wherein said set of deep breath training programs comprises:

for a trainer, selecting a breathing frequency according to a training file of the trainer, and guiding the trainer to smoothly breathe slowly by using a balloon animation graph on a display screen;

dividing the respiratory heart rate resonance measurement into 2-5 grades by using the harmony measurement, designing corresponding guide voice and music for each grade, selecting to play the corresponding guide voice and music according to the current harmony grade, and guiding the trainee to relax the body and the mood, control the idea, and reduce the anxiety and the negative idea;

HRV waveform, harmonious degree measurement and training score are displayed on a screen, and the trainee is guided to travel to the right direction.

3. The mental health assessment and deep breathing training system of claim 1, wherein said set of deep breathing training programs further comprises, for each trainee, finding an optimal breathing resonance frequency:

for an initial trainer, making the respiration frequency per minute be 6, namely the respiration period be 10 seconds, carrying out deep respiration training for 20-30 minutes once, and recording the total harmony measure and each harmony measure;

finely adjusting the breathing cycle upwards and downwards, carrying out deep breathing training, and recording the total harmony degree measurement and each harmony degree measurement;

analyzing the harmonicity measurement under different respiratory periods to find out the optimal respiratory frequency;

and determining the current training parameters according to the historical records and the analysis results.

4. The mental health assessment and deep breathing training system according to claim 1, wherein said mental health assessment and deep breathing training apparatus comprises:

the signal acquisition module is wearable equipment comprising an electrocardio electrode/PPG sensor and a respiratory sensor, acquires electrocardiosignals or PPG signals and respiratory signals, and transmits the signals to a mobile phone APP; and calculating the respiratory heart rate resonance degree measurement by the aid of the mobile phone APP, guiding deep respiration training, and managing a training report.

5. The mental health assessment and deep respiration training device according to claim 4, wherein said signal acquisition module is a microelectronic hardware wearable device, and comprises a sensor and a signal acquisition and transmission unit, and optionally has the following two configurations:

a finger-worn photoplethysmography PPG sensor and corresponding amplification, filtering and a/D conversion circuitry, as well as a respiration sensor, are used. With this configuration, the signal acquisition and transmission unit extracts the heart rate signal from the PPG signal; the respiration signal is obtained from a respiration sensor of bioelectrical impedance technology.

6. The mental health assessment and deep breath training device according to claim 4, wherein said signal acquisition module comprises:

acquiring electrocardiosignals, detecting R waves from the electrocardiosignals, acquiring PPG signals, extracting heart rate signals from the PPG signals, and acquiring respiratory signals.

7. The mental health assessment and deep respiration training device according to claim 4, wherein the mobile phone APP calculates RR interval or heart rate signal from the signal acquisition module and respiration signal to obtain respiration heart rate resonance degree measurement, and then guides the trainee to train according to the heart rate resonance degree measurement;