A pelvic floor training device and training method based on heart rate variability biofeedback training

Abstract

The present invention discloses a pelvic floor training device and training method based on heart rate variability biofeedback training. The pelvic floor training device based on heart rate variability biofeedback training provided by the present invention includes a host control module, an electrocardiogram signal acquisition module, an electrocardiogram signal analysis module, a biofeedback electrical stimulation module, and a breathing relaxation module; heart rate variability HRV is associated with pelvic floor training, and a training scheme is corresponding to the time domain analysis parameters and / or frequency domain analysis parameters of the electrocardiogram signal to provide users with a more accurate pelvic floor training effect. The pelvic floor training is achieved through the biofeedback electrical stimulation module, the breathing relaxation module, the intracavitary electrode, and the body surface electrode.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical devices, and in particular relates to a pelvic floor training device and a training method. Background Art

[0002] Pelvic floor dysfunction mainly refers to functional disorders caused by weakness or damage to the components of the female or male pelvic floor, such as muscles, fascia, and ligaments.

[0003] For this disorder, electrical stimulation and biofeedback are common physical training methods. Electrical stimulation mainly promotes the recovery of muscle function through low-frequency pulse current, and biofeedback strengthens the brain's control over the pelvic floor muscles through active training methods. However, current pelvic floor training mainly focuses on the training of the pelvic floor muscles, but ignores the fact that the occurrence and development of many pelvic floor dysfunctions are closely related to the user's psychological condition. Heart rate variability (HRV) refers to the changes in the differences between successive heartbeat cycles. The difference in heartbeat intervals is determined by the impulses sent by the sinoatrial node, and the impulses sent by the sinoatrial node are jointly determined by the activity of the sympathetic nerves and the activity of the parasympathetic nervous system. Therefore, HRV is an indicator that reflects the activity and balance of the autonomic nervous system.

[0004] Many domestic and foreign literatures have reported the correlation between heart rate variability (HRV) and pelvic floor dysfunction, especially for pelvic floor pain, constipation, irritable bowel syndrome, and sexual dysfunction in men or women. However, the current application of heart rate variability (HRV) is mostly concentrated in psychiatry and psychology to assess the user's mental state, and there is no existing technology that combines heart rate variability with pelvic floor training to assist in training users' pelvic floor dysfunction from multiple dimensions. Summary of the invention

[0005] Purpose of the invention: In view of the above shortcomings, the present invention provides a pelvic floor training device based on heart rate variability biofeedback training, the purpose of which is to combine heart rate variability with pelvic floor training to provide users with more diverse judgment factors for pelvic floor training.

[0006] The present invention also provides a biofeedback training method based on heart rate variability, which also achieves the above purpose.

[0007] Technical solution: To solve the above problems, the pelvic floor training device based on heart rate variability biofeedback training of the present invention can adopt the following technical solutions:

[0008] A pelvic floor training device based on heart rate variability biofeedback training, comprising: a host control module, an electrocardiogram signal acquisition module, an electrocardiogram signal analysis module, a biofeedback electrical stimulation module, a breathing relaxation module, an intracavity electrode, and a body surface electrode;

[0009] The ECG signal acquisition module includes a biosignal sensor and a digital signal processing unit;

[0010] The ECG signal acquisition module is used to collect raw electromyographic data;

[0011] The ECG signal analysis module is used to perform time domain analysis and frequency domain analysis on the collected ECG signal to obtain time domain analysis parameters and / or frequency domain analysis parameters;

[0012] The host control module is used to obtain analysis results according to the time domain analysis parameters and / or the frequency domain analysis parameters;

[0013] The biofeedback electrical stimulation module, the breathing relaxation module, the intracavity electrodes, and the body surface electrodes are used to implement the training program.

[0014] Furthermore, the ECG signal acquisition module includes: a biosignal sensor for acquiring original ECG biosignals; a digital signal processing unit for converting the original ECG biosignals into digital signals; and a wireless communication unit for transmitting the digital signals to the ECG signal analysis module.

[0015] Furthermore, the analysis indicators of the time domain analysis include: standard deviation during normal heartbeats, standard deviation during average normal heartbeats, square root of mean square error during continuous normal heartbeats; the analysis indicators of the frequency domain analysis include: high frequency, frequency range: 0.15-4hz; low frequency, frequency range: 0.04-0.15hz; extremely low frequency, frequency range: 0.005-0.04hz; total power; ratio of low frequency to high frequency.

[0016] Furthermore, the host control module sends instructions to the biofeedback electrical stimulation module and performs relaxation training on the user through the intracavitary electrodes and the body surface electrodes; the breathing relaxation module is used to monitor the user's breathing.

[0017] Furthermore, it also includes an electromyographic bio-amplifier and a filter; the electromyographic bio-amplifier amplifies the electromyographic signal collected by the intracavitary electrode, and the filter filters the amplified signal and transmits the signal to the host control module.

[0018] Furthermore, the ECG data analyzed in the ECG signal analysis module is transmitted to the host control module via the wireless Bluetooth transmission module.

[0019] Furthermore, the ECG signal acquisition module includes at least one of a bracelet, an electrocardiogram (ECG) sensor, and a photoplethysmographic (PPG) sensor; and the acquisition methods include electrocardiogram and pulse wave.

[0020] Furthermore, the breathing relaxation module includes head-mounted VR glasses, a signal processing module, and a wireless transmission module. The wireless transmission module is connected to the host control module, and the control module transmits instructions to the signal processing module to control the display screen of the head-mounted VR glasses.

[0021] Beneficial effects:

[0022] The pelvic floor training device based on heart rate variability biofeedback training provided by the present invention associates heart rate variability HRV with pelvic floor training, and provides users with more accurate pelvic floor training effects by corresponding training schemes to time domain analysis parameters and / or frequency domain analysis parameters of electrocardiogram signals. The pelvic floor training is implemented through a biofeedback electrical stimulation module, a breathing relaxation module, intracavitary electrodes, and body surface electrodes.

[0023] The pelvic floor training method based on heart rate variability biofeedback training provided by the present invention can adopt the following technical solutions:

[0024] A pelvic floor training method based on heart rate variability biofeedback training comprises the following steps:

[0025] (1) Collecting the user's original ECG bio-signal, converting the original ECG bio-signal into a digital signal, and then performing digital filtering and denoising on the noise in the digital signal;

[0026] (2) calculating heart rate variability data based on the original ECG data to obtain time domain analysis parameters and / or frequency domain analysis parameters;

[0027] (3) Pushing training plans to users based on time domain analysis parameters and / or frequency domain analysis parameters;

[0028] (4) Training the user through a breathing relaxation module and / or a biostimulation feedback module; the biostimulation feedback module includes intracavitary electrodes and body surface electrodes.

[0029] Furthermore, the pelvic floor training method, in step (3), comprises the following sub-steps:

[0030] (3.1) Compare the range of the user's heart rate variability data with the normal value and push a solution;

[0031] (3.2) Choose to use frequency domain indicators or time domain indicators to recommend training plans;

[0032] (3.3) Regardless of whether it is a frequency domain indicator or a time domain indicator, if it is within the normal range, a bioelectric stimulation module is used for training; if it is within the abnormal range, a breathing relaxation module is used for training. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1The structure block diagram of the pelvic floor training device for heart rate variability biofeedback training provided by an embodiment of the present invention.

[0034] Figure 2 This is a flowchart of a pelvic floor training device for heart rate variability biofeedback training provided by an embodiment of the present invention.

[0035] Figure 3 A flowchart of the application of the training module provided in an embodiment of the present invention.

[0036] Figure 4 This is a structural diagram of the breathing relaxation module. DETAILED DESCRIPTION

[0037] The specific implementation of the present invention is described below with reference to the accompanying drawings.

[0038] Embodiment 1

[0039] This embodiment provides a pelvic floor training device based on heart rate variability.

[0040] Figure 1 The preferred structure of the pelvic floor training device based on heart rate variability provided by this embodiment is shown. Figure 2 The work flow chart of the pelvic floor training device for heart rate variability provided in this embodiment is shown. As shown in the figure, the device includes: a host control module 100, an ECG signal acquisition module 101, an ECG signal analysis module 102, an electromyographic bioamplifier 103, a filter 104, a biostimulation feedback module 105, a breathing relaxation module 106, an intracavity electrode 107, a body surface electrode 108, an ECG signal acquisition module 101 and a mobile terminal 200. Among them, the ECG signal acquisition module 101 can be a bracelet or other structure that can be worn on the user, such as a portable ECG machine, a light sensing device, so as to collect the user's original ECG data. The collection means include but are not limited to ECG collection and pulse wave collection. The ECG can directly collect the user's heartbeat data, and the light sensing device can detect the skin surface light intensity change information caused by the change of blood volume in the user's blood vessels after irradiating the skin surface with light, and further calculate the pulse wave signal based on the light intensity change information. The ECG signal analysis module 102 processes the original ECG data to obtain heart rate variability data, and analyzes the time domain parameters and / or frequency domain parameters of the heart rate variability data to obtain time domain analysis parameters and / or frequency domain analysis parameters.

[0041] The ECG signal acquisition module 101 includes: a biosignal sensor 201 and a digital signal processing unit 202. The biosignal sensor 201 is used to sense and collect the user's original ECG data, and transmit the original ECG data to the digital signal processing unit 202. The digital signal processing unit 202 receives the original ECG data and performs A / D conversion on it, thereby converting the original ECG biosignal into a digital signal, and performs digital filtering and denoising on the noise in the digital signal, thereby obtaining ECG data with obvious R waves and low noise. The signal is then transmitted to the ECG signal analysis module 102 for analysis.

[0042] It should be noted that the process of ECG signal collection is affected by many external factors, such as the time of collection, the state and action of the user during collection, the posture of collection, etc. In order to avoid the influence of the above factors and ensure the uniformity of the indicators, a preferred implementation method makes a unified standard for the collection time and collection posture, that is, the user is required to collect ECG signals for 5 minutes in a lying position in a quiet state. In order to ensure the accuracy of the data, the ECG data of the last 3 minutes are selected for analysis.

[0043] The ECG signal analysis module 102 may include: a heart rate variability data time domain analysis unit 301 and a heart rate variability data frequency domain analysis unit 302. The ECG signal analysis module 102 receives the original ECG data after noise reduction processing by the ECG signal acquisition module 101, calculates the time lengths between adjacent R waves of the original ECG data one by one, and forms an RR interval sequence, which lays a foundation for the heart rate variability data time domain analysis unit 301 and the heart rate variability data frequency domain analysis unit 302 to perform data analysis on the heart rate variability data. The heart rate variability data time domain analysis unit 301 performs statistical analysis on the values ​​of the RR interval sequence, thereby obtaining a series of time domain analysis parameters. Specifically, the time domain parameters include: SDNN, SDANN, RMSSD; the heart rate variability data frequency domain analysis unit 302 applies fast Fourier transform to the RR interval sequence, converts the RR interval sequence that changes with time into a spectrum, calculates the power spectrum density, and then divides it into different frequency intervals, and calculates the power value corresponding to each frequency band, thereby obtaining the frequency domain analysis parameters. Specifically, the frequency domain parameters include: HF (high frequency, frequency range: 0.15-4hz); LF (low frequency, frequency range: 0.04-0.15hz); VLF (very low frequency, frequency range: 0.005-0.04hz); TP (total power).

[0044] The data analyzed by the ECG signal analysis module 102 through the time domain analysis unit 301 and the heart rate variability data frequency domain analysis unit 302 is transmitted to the host control module 1 through the wireless Bluetooth module 203. The host control module 1 determines the user's current mental state by analyzing the frequency domain data and the time domain data. Specifically, the host control module 1 contains the heart rate variability frequency domain index and time domain index range of the normal population, and can automatically compare and analyze the heart rate variability data of the transmitted user. Table 1 and Table 2 are the parameters of the commonly used frequency domain index and time domain index.

[0045] Table 1

[0046]

[0047] Table 2

[0048]

[0049] The host control module 100 trains the user through the biostimulation feedback module 105 and the breathing relaxation module 106. Specifically, the host control module 100 can choose to push the scheme through the frequency domain index or the time domain index. The frequency domain index scheme is recommended as follows: LF / HF is less than 0.5, push through the biostimulation feedback module 105 for training; LF / HF is greater than 2, push through the breathing relaxation module 106 for training; LF / HF is between 0.5-2, the system pushes the biostimulation feedback module 105 and the breathing relaxation module 106 for training. The time domain index scheme is recommended as follows: any one of the three indicators SDNN, SDANN, and RMSSD can be selected as the benchmark value, for example, RMSSD is selected, and the calculation is as follows: RMSSD of normal people-RMSSD of users / RMSSD of users>30%, then push through the breathing relaxation module 106 for training; RMSSD of normal people-RMSSD of users / RMSSD of users<30%, then push the biostimulation feedback module 105 for training.

[0050] The biostimulation feedback module 105 trains the user through the intracavitary electrodes 107 and the surface electrodes 108. The training modes include muscle coordination training, endurance training, relaxation training, etc. During the training, the intracavitary electrodes 107 and the surface electrodes 108 monitor the user's surface electromyographic signals in real time, and transmit them to the host control module 1 through the electromyographic bioamplifier 103 and the filter 104. The host control module 1 automatically adjusts the difficulty of the training mode according to the user's training effect.

[0051] The biostimulation feedback module 105 mainly performs muscle strength training through the intracavitary electrode 107, including kegel training, electrical stimulation and other modes. Electrical stimulation promotes muscle contraction and blood circulation, and kegel training strengthens the brain's control over muscles. The biostimulation feedback module 105 mainly performs animation or music relaxation training through the surface electrode 108. The surface electrode is attached to the user's shoulder and neck, thigh adductor muscle, and gluteus maximus. During the training, the above muscle groups are kept relaxed as much as possible. At the same time, the surface electrode 108 will also detect the electromyographic signal of the above muscle group in real time. When the electromyographic signal is less than the threshold, the animation and music will continue to play; when the electromyographic signal is greater than the threshold, the animation and music will stop, prompting the user that the muscles are tense at this time and need to be further relaxed.

[0052] The breathing relaxation module 106 includes the device including head-mounted VR glasses 401, a signal processing module 402, a wireless transmission module 403, and a breathing monitoring module 404. The wireless transmission module 403 is connected to the host control module 100. The control module 100 can transmit instructions to the signal processing module 402, and further control 401 to display pictures of different difficulties and goals. The breathing monitoring module 404 has a built-in breathing rate sensor, which can monitor the patient's breathing rate, that is, the time of each exhalation and the time of inhalation, and give the user prompts during training.

[0053] Breathing is the only part in the human body that connects the sympathetic nerves and the parasympathetic nerves. It can be used when the sympathetic nerves are controlled, and it can also be used when the parasympathetic nerves are controlled.

[0054] When breathing at the respiratory resonance frequency, the heart rate variability is the largest. According to the resonance model, cardiovascular resonance (i.e., high cardiac coherence) requires slow-paced breathing, about 6 breathing cycles per minute (i.e., 0.1Hz), which represents the body's resonance frequency. The user can adjust his breathing according to the guidance video animation displayed by the head-mounted VR glasses 401 to achieve the stress relief breathing training function. Specifically, the video animation can be selected, including natural scenery: night starry sky, beach sea, mountain stream, etc. During the user's training, the head-mounted VR glasses 401 are used to completely immerse the user in the training screen, feel the relaxation brought by the screen and sound, and adjust their breathing frequency at the same time. The ECG signal acquisition module 101 will monitor the user's ECG data in real time, and the ECG signal analysis module 102 will calculate the user's heart rate variability data, and generate a relaxation index at the same time. The relaxation index is closely related to the frequency domain index and the time domain index. The larger the relaxation index, the more relaxed the user is; the smaller the relaxation index, the more nervous the user is. The user can adjust his / her breathing frequency according to the relaxation index. When the relaxation index reaches a normal level, the breathing monitoring module 404 will prompt the user to maintain training according to this breathing frequency, which is the breathing resonance frequency.

[0055] Embodiment 2

[0056] This embodiment provides a pelvic floor training method based on heart rate variability.

[0057] See also Figure 2 , Figure 2 This is a flow chart of the method for analyzing physical and mental conditions provided in this embodiment. As shown in the figure, the method includes the following steps:

[0058] Step S110, obtaining original ECG data.

[0059] Specifically, the user's original ECG bio-signal is first collected, and then A / D conversion is performed on it to convert the original ECG bio-signal into a digital signal. The noise in the digital signal is then digitally filtered and denoised to obtain ECG data with obvious R waves and lower noise.

[0060] Step S120, calculating heart rate variability data based on the original ECG data to obtain time domain analysis parameters and / or frequency domain analysis parameters.

[0061] Step S130: Push a training plan to the user based on the time domain analysis parameters and / or frequency domain analysis parameters

[0062] Step S140, training is performed by using different modules, and the training parameters can be monitored in real time during training to adjust the training parameters.

[0063] See also Figure 3 , Figure 3 This is a flow chart of step S130 in the pelvic floor training method based on heart rate variability provided in this embodiment. As shown in the figure, this step includes:

[0064] In sub-step S210, the host control module automatically determines the range of the user's heart rate variability data, compares it with the normal value, and pushes a solution

[0065] Sub-step S220: Select frequency domain indicators or time domain indicators to recommend training plans

[0066] Sub-step S230, whether it is a frequency domain indicator or a time domain indicator, if it is within the normal range, a certain block is used for training using bioelectric stimulation; if it is within the abnormal range, a breathing relaxation module is used for training.

[0067] There are many methods and approaches to implement the technical solution of the present invention, and the above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A pelvic floor training device based on heart rate variability biofeedback training, It is characterized in that include: A host control module (100), an electrocardiogram signal acquisition module (101), an electrocardiogram signal analysis module (102), a biofeedback electrical stimulation module (105), a breathing relaxation module (106), an intracavity electrode (107), and a body surface electrode (108); The electrocardiogram signal acquisition module includes a biological signal sensor (201) and a digital signal processing unit (202); The ECG signal acquisition module is used to collect raw ECG data; The ECG signal analysis module is used to perform time domain analysis and frequency domain analysis on the collected ECG signal to obtain time domain analysis parameters and / or frequency domain analysis parameters; The host control module (100) is used to obtain analysis results based on time domain analysis parameters and / or frequency domain analysis parameters and control training through the biofeedback electrical stimulation module (105), the breathing relaxation module (106), the intracavitary electrode (107), and the body surface electrode (108); when LF / HF is greater than 2, the training is pushed through the breathing relaxation module; when LF / HF is between 0.5 and 2, the system pushes the biofeedback electrical stimulation module to perform training in combination with the breathing relaxation module; LF / HF indicates the degree of sympathetic and parasympathetic nerve dominance; HF represents a high frequency domain parameter, with a frequency range of 0.15-4 Hz; LF represents a low frequency domain parameter, with a frequency range of 0.04-0.15 Hz; The biofeedback electrical stimulation module (105), the breathing relaxation module (106), the intracavitary electrode (107), and the body surface electrode (108) are used to implement the training program.

2. The pelvic floor training device according to claim 1, It is characterized in that The electrocardiogram signal acquisition module (101) comprises: A biosignal sensor (201), used for collecting original electrocardiogram biosignals; A digital signal processing unit (202), used for converting the original electrocardiographic biosignal into a digital signal; The wireless communication unit is used to transmit the digital signal to the ECG signal analysis module.

3. The pelvic floor training device according to claim 1 or 2, It is characterized in that The analysis indicators of the time domain analysis include: standard deviation during normal heartbeats, standard deviation during average normal heartbeats, square root of mean square error during continuous normal heartbeats; the analysis indicators of the frequency domain analysis include: high frequency, frequency range: 0.15-4hz; low frequency, frequency range: 0.04-0.15hz; extremely low frequency, frequency range: 0.005-0.04hz; total power; ratio of low frequency to high frequency.

4. The pelvic floor training device according to claim 3, It is characterized in that The host control module (100) sends instructions to the biofeedback electrical stimulation module (105) and performs relaxation training on the user through the intracavitary electrodes (107) and the body surface electrodes (108); the breathing relaxation module (106) is used to monitor the user's breathing.

5. The pelvic floor training device according to claim 1, It is characterized in that It also includes an electromyographic bio-amplifier (103) and a filter (104); the electromyographic bio-amplifier (103) amplifies the electromyographic signal collected by the intracavitary electrode (107), and the filter (104) filters the amplified signal and transmits the signal to the host control module (100).

6. The pelvic floor training device according to claim 1, It is characterized in that The ECG data analyzed in the ECG signal analysis module (102) is transmitted to the host control module (100) via the wireless Bluetooth transmission module.

7. The pelvic floor training device according to claim 6, It is characterized in that The ECG signal acquisition module includes at least one of a bracelet, an ECG sensor, and a photoplethysmographic (PPG) sensor; and the acquisition methods include an ECG and a pulse wave.

8. The pelvic floor training device according to claim 4, It is characterized in that The breathing relaxation module (106) includes head-mounted VR glasses (301), a signal processing module (302), and a wireless transmission module (303). The wireless transmission module (303) is connected to the host control module (100). The control module (100) transmits instructions to the signal processing module (302) to control the head-mounted VR glasses (301) to display an image.

Images