Rehabilitation training evaluation method based on combination of dream guidance and myoelectricity monitoring

Abstract

The invention discloses a rehabilitation training evaluation method based on dream guidance in combination with myoelectricity monitoring. The method comprises the following steps: collecting and preprocessing electroencephalogram data; analyzing the sleep state and judging whether the sleep state is a deep sleep state; performing stimulation guidance of a deep sleep state; repairing the deep sleep dream; performing real-time myoelectricity monitoring by using myoelectricity monitoring equipment after the patient enters the deep sleep state, and performing motor imagery marking in combination with the electroencephalogram signals; and performing rehabilitation training effect evaluation in combination with the myoelectricity data and the electroencephalogram data. According to the invention, a patient is guided to perform nerve rehabilitation treatment in a mode of monitoring dream by electroencephalogram, so that the sleep time is used for training to improve the training duration, and environmental interference factors can be reduced through deep sleep; the muscle activity effect of the dream guiding part can be judged in an auxiliary mode through the myoelectricity monitoring means, and whether the patient is correctly guided or not is judged in combination with monitoring of the brain electricity on the muscle part.

Description

Technical Field

[0001] The present invention relates to the field of electroencephalogram (EEG) data processing, and in particular to a rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring. Background Art

[0002] At present, EEG technology has been involved in many fields, among which motor imagery has a wide range of applications. However, the results of this method are affected in many ways, such as requiring patients to actively imagine the corresponding movements. In addition, spontaneous EEG signal monitoring such as motor imagery will have many subjective factors and external interference, resulting in poor accuracy in assessing rehabilitation.

[0003] Using EEG to monitor sleep status is a very mature state, because there are obvious characteristic marks in different sleep cycles. In addition, people will have dreams during the rapid eye movement period (deep sleep period), and the brain will be as active as usual. If it does not affect the positive feedback of sleep to the body, using the guidance during the dream period can drive the dream to develop in the corresponding direction, thereby affecting the outcome of the dream. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring.

[0005] To achieve the purpose of the present invention, the technical solution adopted by the present invention is:

[0006] A rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring comprises the following steps:

[0007] Step 1, collect and preprocess EEG data;

[0008] Step 2, analyzing the sleep state and determining whether it is a deep sleep state;

[0009] Step 3, conduct stimulation guidance for deep sleep state;

[0010] Step 4: Perform dream repair in deep sleep;

[0011] Step 5, using electromyography monitoring equipment to perform real-time electromyography monitoring after the patient enters a deep sleep state, and combining electroencephalographic signals to perform motor imagery marking;

[0012] Step 6: Combine the electromyography data and electroencephalogram data to evaluate the effect of rehabilitation training.

[0013] Furthermore, step 1, EEG data preprocessing is specifically to calculate the frequency domain characteristics of EEG waves in different frequency bands through Fourier transform or wavelet transform.

[0014] Furthermore, in step 2, the frequency domain characteristics of the real-time brain waves obtained in step 1 are compared with the characteristics of different sleep stages to determine the sleep state.

[0015] Furthermore, in step 3, the stimulation guidance in the deep sleep state is electrical stimulation guidance. When entering the deep sleep state, the electrical stimulation device is turned on to output current to the set guidance part according to the set rehabilitation part.

[0016] Furthermore, the initial current output is experimentally adjusted to a frequency and amplitude acceptable to the patient when the patient is awake, and the frequency and amplitude of the output current are dynamically changed during sleep.

[0017] Furthermore, in step 4, dream repair is performed after the patient's sleep state is disturbed in the guidance mode. The dream repair uses an electrical stimulation device with the same stimulation source, and a plurality of current outputs with different amplitudes and frequencies are set in advance as electrical stimulation options for dream repair. After the dream repair mode starts, different types of random switching are performed.

[0018] Furthermore, in step 5, the electromyography is monitored in real time. If there is a jump in amplitude, it means that the current muscle may be activated, and the electromyography data is recorded;

[0019] Perform fast Fourier transform on the current EEG signal to obtain the energy of different frequencies, and judge the energy change trend of the electrodes in the motor brain areas C3 and C4. If the electrode energy of the relevant brain areas C3 and C4 is in a continuous high-energy state after being guided, it means that the patient is successfully guided, and the EEG data at this time is recorded.

[0020] Further, in step 6, the numerical statistics of the day are performed through the electromyographic data recording information in step 5, and the electromyographic maximum value Vmax and variance S are calculated, and whether Vmax and S are improved through stage training is observed;

[0021] If the improvement is obvious, it means that the patient's mental state is good and the training effect is good; if the improvement is slow or there is a decline, it means that the patient's mental state is average at the current stage, and the training guidance mode should be adjusted or the patient should be given more rest.

[0022] The beneficial effect of the present invention is that, compared with the prior art, the present invention reasonably stimulates the affected part in the deep sleep stage by means of dream guidance, and performs a cycle transition from stimulation to monitoring by switching the input and output of electrical stimulation, thereby realizing a rehabilitation training of the affected part and a rehabilitation assessment function of the affected part. In addition, the dream stage is in the deep sleep stage, and the interference reflection to the environment is greatly reduced at this time, thereby reducing the rehabilitation training effect problems caused by a series of interference factors and data quality problems.

[0023] The present invention guides patients to undergo rehabilitation neurological treatment by monitoring dreams through electroencephalogram (EEG), thereby utilizing sleep time for training to increase the training duration, and can also reduce environmental interference factors through deep sleep; using an electrical stimulation device as a stimulation source for dream guidance can achieve strong adaptability through the flexible and changeable nature of its output source, and achieve stronger rehabilitation effects for different patients with different effects; electromyography monitoring can assist in judging the muscle activity effect of the dream guidance site, and combined with EEG monitoring of the muscle site, it can be judged whether the patient is correctly guided. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of the rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring according to the present invention;

[0025] Figure 2 This is a diagram of sleep stages. DETAILED DESCRIPTION

[0026] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of this application.

[0027] like Figure 1 As shown, the rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring described in the present invention collects and processes EEG data; analyzes the sleep state and determines whether it is a deep sleep state, and distinguishes the sleep state. If it is a non-deep sleep state, the sleep aid system is turned on to make the patient fall asleep quickly; if it is a deep sleep state, the stimulation source is turned on for stimulation guidance; the mode can be switched between dream guidance mode and dream repair mode based on the dream state and sleep quality induced by the stimulation; at the same time, the electromyography monitoring device is used to perform real-time electromyography monitoring after the patient enters a deep sleep state, and after the training is completed, the rehabilitation training effect is evaluated based on the collected electromyography data and EEG data.

[0028] The rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring described in the present invention has the following specific steps:

[0029] Step 1, collect and preprocess EEG data;

[0030] Set the sleep start time, sleep duration, rehabilitation site (guided site) in the sleep monitoring system, and use the electrical stimulation device as the stimulation source. Wear the EEG acquisition device before training, attach the electrodes of the electrical stimulation device to the rehabilitation site to complete the preparation, turn on the EEG acquisition device when the time reaches the preset sleep start time, and perform real-time EEG data acquisition, and then perform real-time EEG data preprocessing to determine the sleep state. Through Fourier transform or wavelet transform, calculate the frequency domain characteristics of brain waves in different frequency bands to determine the sleep state.

[0031] like Figure 2 As shown, the sleep stages are mainly divided into four stages: non-rapid eye movement stage 1 with alpha waves of 8 to 13 Hz as the main frequency; non-rapid eye movement stage 2 with spindle waves and K complex waves of 11 to 16 Hz; non-rapid eye movement stage 3 with delta waves of 0.5 to 2 Hz as the main frequency; and rapid eye movement stage with theta waves of 3 to 7 Hz as the main frequency.

[0032] Step 2, analyzing the sleep state and determining whether it is a deep sleep state;

[0033] The sleep state can be determined by comparing the frequency domain characteristics of the real-time EEG waves obtained after EEG data collection and preprocessing in step 1 and combining the characteristics of different sleep stages. If it is a non-deep sleep state, the sleep aid system is turned on to help the patient fall asleep quickly; if it is a deep sleep state, the stimulation source is turned on for stimulation guidance.

[0034] The entire sleep process is a cycle of alternating non-rapid eye movement and eye movement stages. The EEG characteristics can be used to determine whether a person has entered a deep sleep state, thereby switching modes and switching on and off stimulation sources to avoid affecting sleep.

[0035] Step 3, conduct stimulation guidance for deep sleep state;

[0036] Through the monitoring in step 2, when the EEG characteristics are detected to enter deep sleep, the sleep stage at this time is the rapid eye movement period, and the sleep monitoring system enters the dream guidance mode. At this time, the corresponding part is stimulated and guided according to the rehabilitation part set by the patient.

[0037] The main guidance method is electrical stimulation guidance. When entering a deep sleep state, the electrical stimulation device is turned on and the current is output to the set guidance part according to the set rehabilitation part. The initial current output can be adjusted to the frequency and amplitude acceptable to the patient when the patient is awake. This output can ensure that it will not affect the deep sleep state. The frequency and amplitude of the output current can also be dynamically changed during sleep.

[0038] When sleep begins, monitor whether the patient enters the rapid eye movement period. At this time, the default electrical stimulation mode is the sleep-aid mode. In the sleep-aid mode, the electrical stimulation will stimulate with a massage current change to assist the patient in sleeping. The massage area is selected by the patient himself. When the rapid eye movement period is detected, the electrical stimulation mode is switched to the guidance mode, which can provide electrical stimulation guidance for the patient's rehabilitation area.

[0039] Step 4: Perform dream repair in deep sleep;

[0040] According to step 3, the interaction of stimulation and rest is performed in deep sleep. If the patient enters non-deep sleep from deep sleep during the interaction, it is necessary to consider whether the patient's sleep is affected by the current output and whether the patient is transitioning a sleep cycle. This can be judged by the stage of dream guidance. If it is the guidance stage, the probability that the reduction in sleep quality is related to stimulation is greater at this time. In order to ensure that the patient's sleep is not affected, the dream repair mode is switched.

[0041] Dream repair can use electrical stimulation equipment with the same stimulation source, with a variety of pre-set current outputs with different amplitudes and frequencies as electrical stimulation options for dream repair. After the dream repair mode starts, different types of random switching can be performed.

[0042] Dream repair mainly has a sleep-inducing effect. It is a remedial measure taken after the patient's sleep state is disturbed due to the size and frequency of the electric current in the guidance mode.

[0043] Step 5, using electromyography monitoring equipment to perform real-time electromyography monitoring after the patient enters a deep sleep state, and combining electroencephalographic signals to perform motor imagery marking;

[0044] After the dream guidance mode in step 3 is turned on, the electromyography monitoring device will be turned on and the electromyography data will be recorded in real time. Since the guidance process uses the electric stimulation device as the stimulation source, it will stimulate the muscles through electric current for tactile guidance and interfere with the electromyography monitoring, so the current output of the current frequency of the electric stimulation needs to be filtered through a filter.

[0045] After the EMG signal preprocessing is completed, the EMG is monitored in real time. If there is a jump in amplitude, it means that the current muscle may be activated, and the EMG value is recorded.

[0046] At this time, perform fast Fourier transform FFT on the current EEG signal to obtain the energy size of different frequencies, judge the trend of electrode energy change in the motor brain areas C3 and C4, and determine whether the EEG changes are caused by motor imagination. If the energy of the relevant brain areas C3 and C4 is in a continuous high-energy state after being guided, it means that the patient has been successfully guided and can be recorded at this time.

[0047] The current data point is marked in combination with the judgment of the electromyographic signal, that is, the marked data point is when the electromyographic amplitude jumps and the electrode energy changes of the motor brain areas C3 and C4 are in a continuous high-energy state, and the electromyographic data and electroencephalographic data at this time are recorded.

[0048] Step 6, combining electromyography data and electroencephalogram data to evaluate the effect of rehabilitation training;

[0049] The EMG data recorded in step 5 is used to perform numerical statistics for the day, and the maximum EMG value Vmax and variance S are calculated. By observing whether Vmax and S are improved through stage training, it represents the effectiveness of dream-guided training.

[0050] In addition, the effectiveness of motor imagery rehabilitation training can be judged based on the muscle strength recovery at each stage. The most obvious stage of the improvement curve indicates that the motor imagery data has a high probability of high quality and the patient's mental state is good. On the contrary, if the improvement is slow or declines at a certain stage, it can be judged that the patient's mental state is average at the current stage, and it is necessary to adjust the training guidance mode and current stimulation mode or increase the patient's rest.

[0051] The beneficial effect of the present invention is that, compared with the prior art, the present invention reasonably stimulates the affected part in the deep sleep stage by means of dream guidance, and performs a cycle transition from stimulation to monitoring by switching the input and output of electrical stimulation, thereby realizing a rehabilitation training of the affected part and a rehabilitation assessment function of the affected part. In addition, the dream stage is in the deep sleep stage, and the interference reflection to the environment is greatly reduced at this time, thereby reducing the rehabilitation training effect problems caused by a series of interference factors and data quality problems.

[0052] The present invention guides patients to undergo rehabilitation neurological treatment by monitoring dreams through electroencephalogram (EEG), thereby utilizing sleep time for training to increase the training duration, and can also reduce environmental interference factors through deep sleep; using an electrical stimulation device as a stimulation source for dream guidance can achieve strong adaptability through the flexible and changeable nature of its output source, and achieve stronger rehabilitation effects for different patients with different effects; electromyography monitoring can assist in judging the muscle activity effect of the dream guidance site, and combined with EEG monitoring of the muscle site, it can be judged whether the patient is correctly guided.

[0053] The applicant of the present invention has made a detailed explanation and description of the implementation examples of the present invention in conjunction with the drawings in the specification. However, those skilled in the art should understand that the above implementation examples are only preferred implementation schemes of the present invention, and the detailed description is only to help readers better understand the spirit of the present invention, but not to limit the scope of protection of the present invention. On the contrary, any improvements or modifications based on the inventive spirit of the present invention should fall within the scope of protection of the present invention.

Claims

1. A rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring, characterized in that: Includes steps: Step 1, collect and preprocess EEG data; Step 2, analyzing the sleep state and determining whether it is a deep sleep state; Step 3, conduct stimulation guidance for deep sleep state; Step 4: Perform dream repair in deep sleep; Step 5, using electromyography monitoring equipment to perform real-time electromyography monitoring after the patient enters a deep sleep state, and combining electroencephalographic signals to perform motor imagery marking; Step 6: Combine the electromyography data and electroencephalogram data to evaluate the effect of rehabilitation training.

2. The rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring according to claim 1 is characterized in that: Step 1: EEG data preprocessing is to calculate the frequency domain characteristics of EEG waves in different frequency bands through Fourier transform or wavelet transform.

3. The rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring according to claim 1 is characterized in that: Step 2: Based on the frequency domain characteristics of the real-time brain waves obtained in step 1 and combined with the characteristics of different sleep stages, a comparison is performed to determine the sleep state.

4. The rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring according to claim 1 is characterized in that: Step 3, the stimulation guidance in the deep sleep state is electrical stimulation guidance. When entering the deep sleep state, turn on the electrical stimulation device and output the current to the set guidance part according to the set rehabilitation part.

5. The rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring according to claim 4 is characterized in that: The initial current output is experimentally adjusted to a frequency and amplitude acceptable to the patient when the patient is awake, and the frequency and amplitude of the output current are dynamically changed during sleep.

6. The rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring according to claim 1 is characterized in that: Step 4, when the patient's sleep state is disturbed in the guidance mode, dream repair is performed. The dream repair uses an electrical stimulation device with the same stimulation source, and a variety of current outputs with different amplitudes and frequencies that are set in advance are used as electrical stimulation options for dream repair. After the dream repair mode starts, different types of random switching are performed.

7. The rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring according to claim 1 is characterized in that: Step 5: Monitor the electromyography in real time. If there is a jump in amplitude, it means that the current muscle may be activated, and record the electromyography data; Perform fast Fourier transform on the current EEG signal to obtain the energy of different frequencies, and judge the energy change trend of the electrodes in the motor brain areas C3 and C4. If the electrode energy of the relevant brain areas C3 and C4 is in a continuous high-energy state after being guided, it means that the patient is successfully guided, and the EEG data at this time is recorded.

8. The rehabilitation training evaluation method based on dream guidance combined with electromyography monitoring according to claim 1 is characterized in that: Step 6, perform numerical statistics of the day through the EMG data recorded in step 5, calculate the maximum EMG value Vmax and variance S, and observe whether Vmax and S are improved through stage training; If the improvement is obvious, it means that the patient's mental state is good and the training effect is good; if the improvement is slow or there is a decline, it means that the patient's mental state is average at the current stage, and the training guidance mode should be adjusted or the patient should be given more rest.

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