CN113975633B - Electrical stimulation rehabilitation training system based on multi-source information coupling feedback - Google Patents
Electrical stimulation rehabilitation training system based on multi-source information coupling feedback Download PDFInfo
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- A61N1/18—Applying electric currents by contact electrodes
- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
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- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
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- A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
- A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
- A61N1/36014—External stimulators, e.g. with patch electrodes
- A61N1/3603—Control systems
- A61N1/36031—Control systems using physiological parameters for adjustment
Abstract
The invention discloses an electrical stimulation rehabilitation training system based on multi-source information coupling feedback, which comprises a virtual reality module, an information acquisition module, an information processing and analyzing module, a task evaluation module, an electrical stimulation switch module, a feedback module, an electrical stimulation control module and an electrical stimulation module. The invention combines the neural information detection means and the response mechanism thereof, strengthens the coupling effect of each feedback link, and improves the matching efficiency of brain-muscle-limb multi-source information feedback and electrical stimulation assisted rehabilitation training, thereby realizing the optimal exercise rehabilitation feedback training effect.
Description
Technical Field
The invention relates to the field of limb movement rehabilitation training, in particular to an electrical stimulation rehabilitation training system based on multi-source information coupling feedback.
Background
Functional electrical stimulation belongs to the physical technology of neuromuscular electrical stimulation, and is characterized in that a pre-designed low-frequency sequence pulse current stimulates a specific muscle group with a specific waveform, strength and repetition frequency according to a set program to induce the muscle to simulate normal autonomous movement or complete specific actions according to a treatment scheme, so that the neural plasticity transformation process of a stroke patient can be accelerated, and the limb movement function can be gradually recovered. Currently, functional electrical stimulation is mainly used for passive training in clinical rehabilitation, stimulation is performed at a fixed frequency, time and the like, feedback and real-time adjustment of multi-source information such as brain, muscle, body and the like are lacked, and maximum behavior gain of functional electrical stimulation is difficult to fully play. The motor dysfunction after stroke is caused by neuromuscular pathway damage and muscle group coordination dysfunction caused by cerebral vascular diseases, and is often accompanied by multi-level information interaction characteristic changes such as oscillation between different brain regions of the brain, information transmission between the brain and limb muscles, synergy between limb muscles, functional coupling between nerve vessels and the like, the rehabilitation process needs the joint participation and coordination of multi-level units such as the brain, the muscles, the limbs, physiological information and the like, and the interaction relationship under different motions also shows certain time-varying, bidirectional and nonlinear coupling characteristics. Although the multi-modal motor feedback training study is effective in overcoming the above limitations to some extent, the synchronization and cooperativity between feedback modes still need to be enhanced.
Therefore, the invention provides an electrical stimulation rehabilitation training system based on multi-source information coupling feedback. The neural information detection means and the response mechanism thereof are combined, the coupling effect of each feedback link is strengthened, and the matching efficiency of brain-muscle-limb multi-source information feedback and electrical stimulation assisted rehabilitation training is improved, so that the optimal exercise rehabilitation feedback training effect is realized.
Disclosure of Invention
In order to realize the purpose of the invention, the following technical scheme is adopted for realizing the purpose:
the utility model provides an electro photoluminescence rehabilitation training system based on multisource information coupling feedback, includes virtual reality module, information acquisition module, information processing analysis module, task evaluation module, electro photoluminescence switch module, feedback module, electro photoluminescence control module and electro photoluminescence module, wherein: the virtual reality module is used for providing a rehabilitation training task for the patient according to the clinical evaluation result of the patient; the information acquisition module is used for acquiring brain function, electrophysiological and motion image information in the rehabilitation training process of the patient; the information processing and analyzing module is used for processing and analyzing the brain function, the electrophysiology and the motion image information synchronously acquired from the acquisition module; the task evaluation module is used for evaluating the task completion condition of the patient under the virtual reality task according to the information obtained by the information processing and analyzing module; the electrical stimulation switch module is used for starting or closing electrical stimulation according to the task completion evaluation condition of the patient; the feedback module is used for feeding back the coupling condition of the brain function, the electrocardio and the myoelectricity in the electrical stimulation process; the electrical stimulation control module is used for adjusting electrical stimulation control parameters according to the coupling information fed back by the feedback module; the electrical stimulation module is used for being started or closed according to the starting or closing instruction output by the electrical stimulation switch module and adjusting parameters according to the specific parameter instruction output by the electrical stimulation control module.
The electrical stimulation rehabilitation training system based on multi-source information coupling feedback is characterized in that: the information acquisition module includes: the near-infrared brain function equipment is used for collecting brain oxygen near-infrared light nerve signals of a patient in the rehabilitation training process; the electromyographic information acquisition instrument is used for acquiring electromyographic signals of a patient in the rehabilitation training process; the electrocardio acquisition instrument is used for acquiring electrocardiosignals of a patient in the rehabilitation training process; and the depth camera is used for acquiring limb motion image information of the patient in the rehabilitation training process.
The electrical stimulation rehabilitation training system based on multi-source information coupling feedback is characterized in that the information processing and analyzing module is used for preprocessing the signals acquired by the information acquisition module: the information processing and analyzing module is used for preprocessing the signals acquired by the information acquisition module, namely: filtering the brain oxygen near-infrared light neural signals to remove long-distance baseline drift and interference noise; processing the electromyographic signals by using a Gaussian filter to remove power frequency noise of the electromyographic signals; processing the electrocardiosignals by using a band-pass filter to remove artifacts of the electrocardiosignals; and performing image preprocessing on the limb moving image information.
The electrical stimulation rehabilitation training system based on multi-source information coupling feedback is characterized in that: and the task evaluation module establishes a patient task completion degree evaluation model according to the brain function connectivity, the concentration degree and the task score of the patient in the rehabilitation training process.
The electrical stimulation rehabilitation training system based on multi-source information coupling feedback is characterized in that: the task evaluation module establishes a patient task completion evaluation model as follows:
the brain oxygen near-infrared light nerve information of the pretreated patients in the rehabilitation training processPerforming complex wavelet transform and wavelet phase coherence calculation, calculating Pearson correlation coefficient and significance level between optical nerve signals of each channel, if the optical nerve signals of two channels are significantly correlated, defining the existence of functional connection, thereby calculating the number of functional connection channels in the brain region of healthy side and affected side, and obtaining the functional connection index L of the brain of affected side based on the functional connection index calculation rule of affected side i ：
Wherein CI is the number of functional connections existing in the healthy lateral brain region, TI is the total channel number of the healthy lateral brain region, CC is the number of channels of the functional connections existing in the affected lateral brain region, TC is the total channel number of the affected lateral brain region, and L jk Is the shortest path between two channels and is used for representing the efficiency and the speed of information transmission, L jk J and k in (a) represent two different brain region channels;
extracting heart rate variability indexes of the preprocessed electrocardiosignals of the patient in the rehabilitation training process, carrying out time domain and frequency domain analysis on the heart rate variability indexes, and establishing an electrocardiogram-based concentration index Z a ：
Wherein LF and HF are low frequency power and high frequency power of heart rate variability index, P i (e jw ) Is the average power spectrum, delta (e), of the heart rate variability signal over a certain sampling period jw ) For a pulse function power spectrum, SDNN is a time domain characteristic standard deviation of heart rate variability, PNN is the percentage of the number of RR intervals of a heart rate variability signal which is more than 50 milliseconds to the total number of RR numbers, alpha and beta are weight coefficients, e is a natural constant, w is the central frequency of the heart rate variability signal, and j is the imaginary part of a complex number;
the motion trail of the patient and the motion trail of the virtual reality actual task are carried out on the motion image signal and the electromyographic signal of the preprocessed patient in the rehabilitation training processTrace contrast analysis, establishing task scoring index P in virtual reality environment c ：
Wherein Q is a motion track integrity coefficient, Rc is a contrast score given by the system and a virtual reality task track, and X i The electromyographic signals are preprocessed, N is the length of a time window corresponding to the electromyographic signals, N is the number of sampling points of the electromyographic signals, theta,
The maximum angle of joint movement of the shoulder joint and the elbow joint in the image signal of the depth camera.
The electrical stimulation rehabilitation training system based on multi-source information coupling feedback is characterized in that: the task evaluation module establishes a patient rehabilitation training task completion index F from three aspects of a brain function connection index, a concentration index and a task scoring index based on a data-driven weight analysis mechanism:
F＝C 1 *L i +C 2 *Z a +C 3 *P c
wherein, C 1 、C 2 、C 3 A weight coefficient.
The data-driven weight analysis mechanism is as follows:
wherein, C j Weight coefficient, p, representing the jth feature j Representing the fluctuation degree of the jth feature in the collected sample database about the patient rehabilitation training task completion degree, namely the standard deviation of the jth feature, and setting the n sample values of the jth feature as x j (1)、x j (2)...x j (n),
w j Represents a deviation coefficient, and can be obtained by the following formula:
wherein, w j Denotes a deviation coefficient, and λ is a threshold value.
The electrical stimulation rehabilitation training system based on multi-source information coupling feedback is characterized in that the feedback module represents the coupling strength of different signals by calculating the coherence of the different signals according to a power spectrum calculation method:
brain-brain coupling strength:
wherein, C nn The brain region coupling strength of different channels, m is the number of channels connected with the affected side function after electrical stimulation, CP ij (w) Power spectra, CP, of brain oxygen signals of different channels ii (w) self-Power Spectrum, CP, of brain oxygen signals for channel i jj (w) is the self-power spectrum of the brain oxygen signal of channel j;
heart-brain coupling strength:
wherein, C nx Coupling strength of cerebral blood oxygen signals of different channels and heart rate variability, m is the number of channels which are functionally connected on the affected side after electrical stimulation, and CP ix (w) Power spectra, CP, of brain oxygen signals and time series of Heart Rate variability of different channels ii (w) self-Power Spectrum, CP, of brain oxygen signals for channel i xx (w) an auto-power spectrum of a time series of heart rate variability;
brain-muscle coupling strength:
wherein, C nz Coupling strength of cerebral blood oxygen signals and myoelectricity of different channels, m is the number of channels for functional connection of the affected side after electrical stimulation, and CP iz (w) Power spectra, CP, of time series of brain oxygen and myoelectrical signals of different channels ii (w) self-Power Spectrum, CP, of brain oxygen signals for channel i zz (w) is the self-power spectrum of the electromyographic signals.
The electrical stimulation rehabilitation training system based on multi-source information coupling feedback is characterized in that: when a patient uses the electrical stimulation module for the first time, the electrical stimulation control module presets the frequency, the pulse width and the amplitude of the electrical stimulation module, judges whether the brain-brain coupling strength index exceeds a threshold value M1, and if not, increases the frequency of the electrical stimulation module; if the normal threshold value is exceeded, the frequency of the electrical stimulation module is adjusted to be low; judging whether the heart-brain coupling strength index exceeds a threshold value M2, if not, increasing the pulse width of the electrical stimulation module; if the pulse width exceeds the normal threshold, the pulse width of the electrical stimulation module is adjusted to be low; judging whether the brain-muscle coupling strength index exceeds a threshold value M3, if not, increasing the amplitude of the electrical stimulation module; if the normal threshold is exceeded, the amplitude of the electrical stimulation module is adjusted down.
The electrical stimulation rehabilitation training system based on multi-source information coupling feedback is characterized in that: the electrical stimulation control module continuously collects data indexes such as basic information of a patient, brain-brain coupling intensity indexes, heart-brain coupling intensity indexes, brain-muscle coupling intensity and corresponding optimal electrical stimulation parameters and the like to establish an electrical stimulation parameter adjustment database, and intelligently outputs specific numerical values of the electrical stimulation parameters by utilizing an artificial intelligence algorithm, so that the rehabilitation training efficiency is improved; the electrical stimulation control module adjusts a database based on electrical stimulation parameters, and establishes a long-time and short-time memory neural network model:
[W,M,F,T]＝G LSTM (B,P,C nn ,C nx ,C nz )
wherein W, M, F, T represents frequency, pulse width, amplitude and time parameters of the output of the electrical stimulation module, G LSTM For training long-term and short-term memory neural network model, B is basic information of patient, P, C nn 、C nx 、C nz The threshold values of the indexes of the clinical evaluation results, the brain-brain coupling strength, the heart-brain coupling strength and the brain-muscle coupling strength of the patients in different rehabilitation stages are shown.
Drawings
FIG. 1 is a schematic diagram of an electrical stimulation rehabilitation training system based on multi-source information coupling feedback according to the present invention;
FIG. 2 is a flow chart of an electrical stimulation rehabilitation training method based on multi-source information coupling feedback according to the present invention;
FIG. 3 is a schematic diagram of adjusting electrical stimulation parameters according to the present invention.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings 1-3.
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
As shown in fig. 1, the electrical stimulation rehabilitation training system based on multi-source information coupling feedback of the present invention includes: the system comprises a virtual reality module, an information acquisition module, an information processing and analyzing module, a task evaluation module, an electrical stimulation switch module, a feedback module, an electrical stimulation control module and an electrical stimulation module. Wherein:
the virtual reality module is used for providing a rehabilitation training task for the patient according to the clinical evaluation result of the patient; the information acquisition module is used for acquiring brain function, electrophysiological and motion image information in the rehabilitation training process of a patient; the information processing and analyzing module is used for processing and analyzing the brain function, the electrophysiology and the motion image information synchronously acquired from the acquisition module; the task evaluation module is used for evaluating the task completion condition of the patient under the virtual reality task according to the information obtained by the information processing and analyzing module; the electrical stimulation switch module is used for starting or closing the electrical stimulation module according to the task completion evaluation condition of the patient; the feedback module is used for feeding back the coupling condition of the brain function, the electrocardio and the myoelectricity in the electrical stimulation process; the electrical stimulation control module is used for outputting an electrical stimulation control parameter instruction according to the coupling information fed back by the feedback module; the electrical stimulation module is used for being started or closed according to the starting or closing instruction output by the electrical stimulation switch module, and parameter adjustment is carried out according to the specific parameter instruction output by the electrical stimulation control module.
The virtual reality module is used for providing rehabilitation training tasks of the patient according to the clinical scale (exercise scale) evaluation result of the patient. Preferably, the difficulty level of the rehabilitation training task should be adjusted according to the evaluation condition of the patient, and the rehabilitation training task patients in different stages can complete more than 50% of the rehabilitation training tasks.
The information acquisition module includes: the near-infrared brain function equipment is used for collecting brain oxygen near-infrared light nerve signals of a patient in the rehabilitation training process; the electromyographic information acquisition instrument is used for acquiring electromyographic signals of a patient in the rehabilitation training process; the electrocardio acquisition instrument is used for acquiring electrocardiosignals of a patient in the rehabilitation training process; and the depth camera is used for acquiring limb motion image information of the patient in the rehabilitation training process.
The information processing and analyzing module is used for preprocessing the signals acquired by the information acquisition module, namely: filtering the brain oxygen near-infrared light neural signals to remove long-distance baseline drift and interference noise; the information processing and analyzing module is used for processing the electromyographic signals by using a Gaussian filter to remove power frequency noise of the electromyographic signals; the information processing and analyzing module processes the electrocardiosignals by using a band-pass filter to remove artifacts of the electrocardiosignals; the information processing and analyzing module performs median filtering on the limb moving image information to remove isolated point noise.
And the task evaluation module establishes a patient task completion degree evaluation model according to the brain function connectivity, the concentration degree and the task score of the patient in the rehabilitation training process.
The task evaluation module establishes a patient task completion evaluation model as follows:
step 1, carrying out near infrared light on brain oxygen of the pretreated patient in the rehabilitation training processAnd performing complex wavelet transformation and wavelet phase coherence calculation on the neural signals, calculating a Pearson correlation coefficient and a significance level between optical neural signals of each channel, and defining that functional connection exists if the optical neural signals of the two channels are significantly correlated. Thereby calculating the number of functional connection channels in the healthy lateral brain region and the affected lateral brain region, and obtaining the affected lateral brain functional connection index L based on the affected lateral functional connection index calculation rule i ：
Wherein CI is the number of functional connections existing in a healthy lateral brain region, TI is the total number of channels in the healthy lateral brain region, CC is the number of channels of functional connections existing in an affected lateral brain region, TC is the total number of channels in the affected lateral brain region, and L jk Is the shortest path between two channels and is used for representing the efficiency and the speed of information transmission, L jk J, k in (a) represent two brain region channels that are not identical. L is a radical of an alcohol i The values range from 0 to 1, where 0 represents the network connection of the patient's cerebellar function and 1 represents the symmetry of the connections between the affected and healthy brain functions.
Step 2, extracting heart rate variability indexes of the preprocessed electrocardiosignals of the patient in the rehabilitation training process, carrying out time domain and frequency domain analysis on the heart rate variability indexes, and establishing an electrocardiogram-based concentration index Z a ：
Wherein LF and HF are low-frequency power and high-frequency power of heart rate variability index, respectively, P i (e jw ) Is the average power spectrum, delta (e), of the heart rate variability signal over a certain sampling period jw ) For pulse function power spectrum, SDNN is the time domain characteristic standard deviation of heart rate variability, PNN is the percentage of the number of RR intervals of the heart rate variability signal larger than 50 ms to the total number of RR numbers, α, β are weight coefficients, e denotes a natural constant, w is the center frequency of the heart rate variability signal, and j is the imaginary part of a complex number. Z a The value range is between 0 and 1Wherein (0-0.5) indicates distraction and insufficient concentration, [0.5-0.8 ] indicates concentration, relaxation and high concentration, [0.8-1 ]]Indicating that the attention is highly concentrated, tense and extremely high in concentration.
Step 3, carrying out comparative analysis on the motion trail of the patient and the motion trail of the virtual reality actual task on the motion picture signal and the electromyographic signal of the preprocessed patient in the rehabilitation training process, and establishing a task scoring index P in the virtual reality environment c ：
Wherein Q is a motion track integrity coefficient, Rc is a contrast score given by the system and a virtual reality task track, and X i The method is to preprocess the electromyographic signals, N is the length of a time window for sampling the electromyographic signals, N is the number of sampling points of the electromyographic signals, theta,
The maximum angle of joint movement of the shoulder joint and the elbow joint obtained from the image signal of the depth camera. P c The value range is between 0 and 1, P c The smaller the value, the lower the score indicating task completion; p c A larger value indicates a higher score for task completion.
And 4, establishing a patient rehabilitation training task completion index F from three aspects of a brain function connection index, a concentration index and a task scoring index based on a data-driven weight analysis mechanism.
F＝C 1 *L i +C 2 *Z a +C 3 *P c
Wherein, C 1 、C 2 、C 3 A weight coefficient.
The data-driven weight analysis mechanism is as follows:
wherein the content of the first and second substances,C j weight coefficient, p, representing the jth feature j The standard deviation of the jth feature, which represents the fluctuation degree of the jth feature in the collected sample database about the patient rehabilitation training task completion degree, is as follows: the n sample values of the jth feature are x j (1)、x j (2)...x j (n),
w j Representing the deviation coefficient, which can be obtained by:
wherein, w j Denotes a deviation coefficient, and λ is a threshold value.
The electrical stimulation switch module is used for judging whether the electrical stimulation module needs to be started according to the task completion degree evaluation condition in the rehabilitation training process of the patient.
When the rehabilitation training task completion index F of the patient is lower than 50%, the electrical stimulation switch module starts the electrical stimulation module, and the electrical stimulation module carries out electrical stimulation treatment on the patient according to preset electrical stimulation parameters and stimulation time. For example: the time for turning on the electrical stimulation module for the first time in the whole rehabilitation training is 5 minutes, and the subsequent adjustment can be carried out according to specific conditions.
The feedback module is used for receiving the signal preprocessed by the information processing and analyzing module, calculating the brain-brain coupling strength, the heart-brain coupling strength and the brain-muscle coupling strength index of the patient in the electrical stimulation rehabilitation training process, and outputting the index to the electrical stimulation control module.
The brain-brain coupling intensity index of the patient is used for judging the functional connection condition of the affected side brain area during electrical stimulation; the heart-brain coupling strength index is used for judging the control of the brain on the concentration degree during the electrical stimulation; and the brain-muscle coupling index is used for judging the cooperative regulation and control of the muscle and the brain during electric stimulation.
The feedback module represents the coupling strength of different signals by calculating the coherence of the different signals according to a power spectrum calculation method, which is specifically as follows:
brain-brain coupling strength:
wherein, C nn The brain region coupling strength of different channels, m is the number of channels connected with the affected side function after electrical stimulation, CP ij (w) Power spectra, CP, of brain oxygen signals of different channels ii (w) self-Power Spectrum, CP, of brain oxygen signals for channel i jj (w) is the self-power spectrum of the brain oxygen signal of channel j.
Heart-brain coupling strength:
wherein, C nx Coupling strength of cerebral blood oxygen signals of different channels and heart rate variability, m is the number of channels which are functionally connected on the affected side after electrical stimulation, and CP ix (w) Power Spectrum, CP, of brain oxygen signals and time series of Heart Rate variability of different channels ii (w) self-Power Spectrum, CP, of brain oxygen signals for channel i xx (w) is the self-power spectrum of the time series of heart rate variability.
Brain-muscle coupling strength:
wherein, C nz Coupling strength of cerebral blood oxygen signals and myoelectricity of different channels, m is the number of channels for functional connection of the affected side after electrical stimulation, and CP iz (w) Power spectra, CP, of time series of brain oxygen and myoelectrical signals of different channels ii (w) self-Power Spectrum, CP, of brain oxygen signals for channel i zz (w) is the self-power spectrum of the electromyographic signals.
The electrical stimulation control module is used for outputting key parameter control instructions such as frequency, pulse width, amplitude and the like to the electrical stimulation module according to the brain-brain coupling strength, the heart-brain coupling strength and the brain-muscle coupling strength indexes fed back by the feedback module.
The electrical stimulation module adjusts specific parameters according to key parameter control instructions such as frequency, pulse width and amplitude of electrical stimulation output by the electrical stimulation control module.
Research shows that when the pulse width and amplitude of the electrical stimulation are unchanged, the electrical stimulation with different frequencies has positive correlation influence on the connection of brain functions of a stroke patient; when the frequency and the amplitude of the electrical stimulation are unchanged, the electrical stimulation with different pulse widths has positive correlation influence on the control of concentration by the brain; when the frequency and the pulse width of the electric stimulation are not changed, the electric stimulation with different amplitudes has positive correlation influence on the cooperative control of the muscles by the brain. That is, when the brain-brain coupling strength is not beyond a certain threshold range, increasing the electrical stimulation frequency can enhance the brain function connection of the patient, when the heart-brain coupling strength is not beyond a certain threshold range, increasing the electrical stimulation pulse width can enhance the concentration of the patient, and when the brain-muscle coupling strength is not beyond a certain threshold range, increasing the electrical stimulation amplitude can enhance the brain muscle coordination control of the patient.
Preferably, the electrical stimulation module adaptively adjusts the frequency, pulse width and amplitude of the electrical stimulation module through the brain-brain coupling strength, heart-brain coupling strength and brain-muscle coupling strength indexes fed back by the feedback module, so that the electrical stimulation auxiliary rehabilitation training can exert the maximum gain effect, and the electrical stimulation rehabilitation training efficiency and effect of the patient are improved.
As shown in fig. 3, the specific adjustments are as follows: when a patient uses the electrical stimulation module for the first time, the electrical stimulation control module presets the frequency, the pulse width and the amplitude of the electrical stimulation module, judges whether the brain-brain coupling intensity index exceeds a threshold value M1 or not, and increases the frequency of the electrical stimulation module if the brain-brain coupling intensity index does not exceed the threshold value M1; if the normal threshold value is exceeded, the frequency of the electrical stimulation module is adjusted to be low; judging whether the heart-brain coupling strength index exceeds a threshold value M2, if not, increasing the pulse width of the electrical stimulation module; if the pulse width exceeds the normal threshold value, the pulse width of the electrical stimulation module is adjusted to be low; judging whether the brain-muscle coupling strength index exceeds a threshold value M3, if not, increasing the amplitude of the electrical stimulation module; if the normal threshold is exceeded, the amplitude of the electrical stimulation module is adjusted down.
The first preset electrical stimulation time in the rehabilitation training task is 5 minutes, then the patient has a rest for 1 minute, and then rehabilitation training is carried out, when rehabilitation training is carried out again, if the task completion degree of the patient can reach more than 60%, the electrical stimulation effect is obvious, and if the task completion degree of the patient is still less than 50%, the next electrical stimulation time is increased, for example, each time is increased by 2 minutes.
And recording and storing parameters such as the electrical stimulation frequency, the pulse width, the amplitude, the time and the like with the highest task completion degree of the patient after the electrical stimulation module is executed in the rehabilitation training process, and using the recorded parameters as initial values of the electrical stimulation parameters in the next rehabilitation training.
The brain-brain coupling strength index, the heart-brain coupling strength index and the brain-muscle coupling strength index threshold values M1, M2 and M3 are mainly determined by the maximum frequency, pulse width and amplitude which can be borne by resting electrical stimulation to a patient before rehabilitation training each time.
In addition, the electrical stimulation control module continuously collects data indexes such as basic information of a patient, brain-brain coupling intensity indexes, heart-brain coupling intensity indexes, brain-muscle coupling intensity and corresponding optimal electrical stimulation parameters, establishes an electrical stimulation parameter adjustment database, intelligently outputs specific numerical values of the electrical stimulation parameters by using an artificial intelligence algorithm, and improves the efficiency of rehabilitation training.
Preferably, based on the electrical stimulation parameter adjustment database, a long-term memory neural network model is established, training tests are continuously carried out, parameters of the neural network model are optimized, an intelligent recommendation model of the electrical stimulation parameters is formed, personalized adaptive electrical stimulation parameters can be recommended, fine adjustment is carried out according to real-time rehabilitation training conditions of patients, and the times of manual parameter adjustment are reduced.
[W,M,F,T]＝G LSTM (B,P,C nn ,C nx ,C nz )
Wherein W, M, F, T is the frequency, pulse width, amplitude and time parameters output by the electrical stimulation module, G LSTM For training good personThe neural network model is memorized for a short time, and B is basic information of the patient, such as: hemiplegia, age, etc., P, C nn 、C nx 、C nz The threshold values of the indexes of the clinical evaluation results, the brain-brain coupling strength, the heart-brain coupling strength and the brain-muscle coupling strength of the patients in different rehabilitation stages are shown.
The rehabilitation training system further comprises a display device, such as a display screen, for presenting the feedback process of the feedback module in real time.
Compared with the prior art, the invention has the beneficial effects that:
(1) the patient rehabilitation training task completion degree index is established through three aspects of the brain function connection index, the concentration degree index and the task scoring index, the cooperative control effect of multi-source information such as brain, physiological information and muscle in the patient rehabilitation training process is fully considered, and the limb functions of the patient can be evaluated in real time.
(2) The multi-level information interaction characteristic changes of coupling characteristic indexes such as brain-brain coupling, heart-brain coupling, brain-muscle coupling and the like of a patient are fully utilized, the electrical stimulation parameters in the rehabilitation training of the patient are adjusted in real time, and the cooperative optimization and real-time feedback of brain limb and electrical stimulation data are promoted.
(3) By utilizing the system, a personalized self-adaptive electrical stimulation parameter adjusting scheme can be provided for the patient, so that the electrical stimulation assisted rehabilitation training can exert the maximum gain effect, and the efficiency and the effect of the electrical stimulation rehabilitation training of the patient are improved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (3)
1. The utility model provides an electro photoluminescence rehabilitation training system based on multisource information coupling feedback, includes virtual reality module, information acquisition module, information processing analysis module, task evaluation module, electro photoluminescence switch module, feedback module, electro photoluminescence control module and electro photoluminescence module, its characterized in that: the virtual reality module is used for providing a rehabilitation training task for the patient according to the clinical evaluation result of the patient; the information acquisition module is used for acquiring brain function, electrophysiological and motion image information in the rehabilitation training process of a patient; the information processing and analyzing module is used for processing and analyzing the brain function, the electrophysiology and the motion image information synchronously acquired from the acquisition module; the task evaluation module is used for evaluating the task completion condition of the patient under the virtual reality task according to the information obtained by the information processing and analyzing module, and the task evaluation module establishes a patient task completion evaluation model according to the brain function connectivity, the concentration and the task score of the patient in the rehabilitation training process; the electrical stimulation switch module is used for starting or stopping electrical stimulation according to the task completion evaluation condition of the patient; the feedback module is used for feeding back the coupling condition of the brain function, the electrocardio and the myoelectricity in the electrical stimulation process; the electrical stimulation control module is used for adjusting electrical stimulation control parameters according to the coupling information fed back by the feedback module; the electrical stimulation module is used for being started or closed according to the starting or closing instruction output by the electrical stimulation switch module and carrying out parameter adjustment according to the specific parameter instruction output by the electrical stimulation control module;
the task evaluation module establishes a patient task completion evaluation model as follows:
performing complex wavelet transform and wavelet phase coherence calculation on the cerebral oxygen near-infrared light nerve signals of the preprocessed patient in the rehabilitation training process, calculating the Pearson correlation coefficient and significance level between the light nerve signals of all channels, and if the light nerve signals of the two channels are significantly correlated, defining that functional connection exists, thereby calculating the functional connection of the healthy side brain region and the affected side brain regionThe number of channels is calculated based on the affected side functional connection index to obtain the affected side brain functional connection index L i ：
Wherein CI is the number of functional connections existing in the healthy lateral brain region, TI is the total channel number of the healthy lateral brain region, CC is the number of channels of the functional connections existing in the affected lateral brain region, TC is the total channel number of the affected lateral brain region, and L gh Is the shortest path between two channels and is used for representing the efficiency and the speed of information transmission, L gh G and h in (1) represent two different brain area channels;
extracting heart rate variability indexes of the preprocessed electrocardiosignals of the patient in the rehabilitation training process, carrying out time domain and frequency domain analysis on the heart rate variability indexes, and establishing an electrocardiogram-based concentration index Z a ：
Wherein LF and HF are low-frequency power and high-frequency power of heart rate variability index, respectively, P i (e jw ) Is the average power spectrum, delta (e), of the heart rate variability signal over a certain sampling period jw ) For a pulse function power spectrum, SDNN is a time domain characteristic standard deviation of heart rate variability, PNN is the percentage of the number of RR intervals of a heart rate variability signal which is more than 50 milliseconds to the total number of RR numbers, alpha and beta are weight coefficients, e is a natural constant, w is the central frequency of the heart rate variability signal, and j is the imaginary part of a complex number;
carrying out comparative analysis on the motion trail of the patient and the motion trail of the virtual reality actual task on the motion picture signal and the electromyographic signal of the preprocessed patient in the rehabilitation training process, and establishing a task scoring index P in the virtual reality environment c ：
Wherein Q is a motion track integrity coefficient, Rc is a contrast score given by the system and a virtual reality task track, and X i The electromyographic signals are preprocessed, N is the length of a time window corresponding to the electromyographic signals, N is the number of sampling points of the electromyographic signals, theta,
The maximum angle of joint movement of the shoulder joint and the elbow joint in the image signal of the depth camera is obtained;
the task evaluation module establishes a patient rehabilitation training task completion index F from three aspects of a brain function connection index, a concentration index and a task scoring index based on a data-driven weight analysis mechanism:
F＝C 1 *L i +C 2 *Z a +C 3 *P c
wherein, C 1 、C 2 、C 3 A weight coefficient;
the data-driven weight analysis mechanism is as follows:
wherein, C j Weight coefficient, p, representing the jth feature j Representing the fluctuation degree of the jth feature in the collected sample database about the patient rehabilitation training task completion degree, namely the standard deviation of the jth feature, and setting the n sample values of the jth feature as x j (1)、x j (2)...x j (n),
w j Representing the deviation coefficient, which can be obtained by:
wherein, w j Denotes a deviation coefficient, and λ is a threshold value.
2. The electrical stimulation rehabilitation training system based on multi-source information coupling feedback of claim 1, characterized in that: the information acquisition module includes: the near-infrared brain function equipment is used for collecting brain oxygen near-infrared light nerve signals of a patient in the rehabilitation training process; the electromyographic information acquisition instrument is used for acquiring electromyographic signals of a patient in the rehabilitation training process; the electrocardio acquisition instrument is used for acquiring electrocardiosignals of a patient in the rehabilitation training process; and the depth camera is used for acquiring limb motion image information of the patient in the rehabilitation training process.
3. The electrical stimulation rehabilitation training system based on multi-source information coupling feedback as claimed in claim 1, wherein the information processing and analyzing module is used for preprocessing the signals acquired by the information acquisition module: the information processing and analyzing module is used for preprocessing the signals acquired by the information acquisition module, namely: filtering the cerebral oxygen near-infrared light neural signals to remove long-distance baseline drift and interference noise; processing the electromyographic signals by using a Gaussian filter to remove power frequency noise of the electromyographic signals; processing the electrocardiosignals by using a band-pass filter to remove artifacts of the electrocardiosignals; and carrying out image preprocessing on the limb moving image information.
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