CN119971308A - Rehabilitation training method and system based on transcranial time-domain interferometric electrical stimulation - Google Patents
Rehabilitation training method and system based on transcranial time-domain interferometric electrical stimulation Download PDFInfo
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Abstract
The application discloses a rehabilitation training method and system based on transcranial time domain interference electrical stimulation. The system comprises a physiological signal acquisition module, a software processing module and an electrical stimulation module, wherein the physiological signal acquisition module is used for acquiring multi-mode physiological signals of a user, the software processing module is used for determining a target stimulation target point of the user based on the multi-mode physiological signals and generating an electrical stimulation parameter scheme of the target stimulation target point, and the electrical stimulation module is used for conducting transcranial time domain interference electrical stimulation on the user based on the electrical stimulation parameter scheme. According to the rehabilitation training method and system provided by the application, the characteristics of deep nucleolus can be accurately regulated and controlled by utilizing the transcranial time domain interference electric stimulation technology, so that the deep focus can be accurately stimulated. At the same time, through the guidance of the multi-mode physiological signals, a more effective personalized stimulation scheme is provided for the user. The system can provide a noninvasive, convenient and efficient solution in the motion disorder rehabilitation applications of hospitals, communities and families.
Description
Technical Field
The application relates to the technical field of medical equipment, in particular to a rehabilitation training method and system based on transcranial time domain interferometry electrical stimulation.
Background
Diseases such as parkinsonism, essential tremor, and apoplexy sequelae cause dyskinesia, not only affect physical activity of patients, but also may significantly reduce quality of life of the patients, so that daily activities are severely impaired.
Studies have shown that transcranial electrical stimulation techniques can effectively alleviate symptoms of dyskinesia. However, the conventional transcranial electrical stimulation method, such as transcranial direct current stimulation, has a limited action area and insufficient intervention depth, and can only stimulate the superficial cortex, such as the motor cortex and the like. And the traditional stimulation method ignores the characteristics of the multi-mode physiological signals with user specificity, so that the symptom relief effect is not ideal, unstable and durable.
Disclosure of Invention
The embodiment of the application provides a rehabilitation training method and system based on transcranial time domain interference electric stimulation, which at least solve the technical problems that the transcranial electric stimulation intervention depth is insufficient and a personalized stimulation scheme is difficult to provide in the related technology.
According to an aspect of an embodiment of the present application, there is provided a rehabilitation training system based on transcranial time domain interferential electrical stimulation, comprising:
The physiological signal acquisition module is used for acquiring multi-mode physiological signals of a user;
The software processing module is used for determining a target stimulation target point of a user based on the multi-mode physiological signals and generating an electric stimulation parameter scheme of the target stimulation target point;
And the electric stimulation module is used for conducting transcranial time domain interference electric stimulation on the user based on the electric stimulation parameter scheme.
In one embodiment, the multi-modal physiological signal includes an electroencephalogram signal, an electromyographic signal, and an actional acceleration signal.
In one embodiment, the software processing module includes:
The target stimulation target point determining unit is used for preprocessing the multi-mode physiological signals, extracting time-frequency characteristics and airspace characteristics of the preprocessed electroencephalogram signals to obtain the time-frequency characteristics and airspace characteristics of the electroencephalogram signals;
Positioning and marking abnormal electroencephalogram data fragments and non-abnormal electroencephalogram data fragments based on the preprocessed electromyogram signals and the motion acceleration signals;
and tracing and positioning an abnormal brain region based on marked abnormal and non-abnormal brain electrical data fragments, time-frequency characteristics and airspace characteristics of brain electrical signals, and determining the target stimulation target point based on a positioning result.
In one embodiment, the software processing module includes:
the personalized digital head model generating unit is used for carrying out brain tissue segmentation on the MRI structure image of the user and reconstructing the digital head model of the user based on the segmentation result;
simulating the digital head model by using a finite element method, simulating electric field distribution of transcranial time domain interference electric stimulation, and calculating a guide field of transcranial time domain interference electric stimulation based on the simulation method;
and generating optimal electrical stimulation parameters by adopting an iterative optimization algorithm based on the guide field.
In one embodiment, the software processing module further comprises:
The real-time regulation and control unit is used for collecting multi-mode physiological signals in the rehabilitation training process of the user according to a preset period;
Inputting the multi-mode physiological signals into a pre-trained classification prediction model to obtain the activity state of a user, wherein the activity state comprises an abnormal activity state and a normal activity state;
And when the user is in an abnormal activity state, dynamically adjusting the electrical stimulation parameters based on the real-time multi-mode physiological signals.
In one embodiment, further comprising:
And the evaluation module is used for collecting and evaluating the movement disorder symptom level before and after the rehabilitation training of the user.
In one embodiment, the software processing module further comprises:
The optimizing unit is used for carrying out regression analysis on the movement disorder symptom level and the multi-mode physiological signals which meet the preset difference before and after the rehabilitation training, and obtaining the multi-mode physiological signals related to the movement disorder symptom level which meet the preset difference;
based on the multi-mode physiological signals obtained by regression analysis, the electric stimulation parameter scheme is optimized.
According to still another aspect of the embodiment of the present application, there is provided a rehabilitation training method based on transcranial time domain interferential electrical stimulation, including:
Collecting multi-mode physiological signals of a user;
determining a target stimulation target point of a user based on the multi-mode physiological signals, and generating an electrical stimulation parameter scheme of the target stimulation target point;
based on the electrical stimulation parameter scheme, transcranial time domain interferometry electrical stimulation is performed on the user.
In one embodiment, determining a target stimulation target for a user based on the multi-modal physiological signal comprises:
Performing time-frequency feature extraction and airspace feature extraction on the preprocessed electroencephalogram signals to obtain time-frequency features and airspace features of the electroencephalogram signals;
Positioning and marking abnormal electroencephalogram data fragments and non-abnormal electroencephalogram data fragments based on the preprocessed electromyogram signals and the motion acceleration signals;
and tracing and positioning an abnormal brain region based on marked abnormal and non-abnormal brain electrical data fragments, time-frequency characteristics and airspace characteristics of brain electrical signals, and determining the target stimulation target point based on a positioning result.
In one embodiment, generating an electrical stimulation parameter profile of the target stimulation target comprises:
Performing brain tissue segmentation on the MRI structure image of the user, and reconstructing a digital head model of the user based on the segmentation result;
simulating the digital head model by using a finite element method, simulating electric field distribution of transcranial time domain interference electric stimulation, and calculating a guide field of transcranial time domain interference electric stimulation based on the simulation method;
and generating optimal electrical stimulation parameters by adopting an iterative optimization algorithm based on the guide field.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
The application can precisely regulate and control the characteristics of deep nucleolus by utilizing transcranial time domain interference electric stimulation technology, and guide the electric stimulation scheme by combining the characteristics of multi-mode physiological signals, thereby realizing more effective alleviation of the symptoms of dyskinesia. The method solves the limitation that the traditional transcranial electric stimulation technology cannot accurately stimulate deep lesions (such as thalamus, globus pallidus, hippocampus and the like), simultaneously provides a more effective individualized stimulation scheme for users through the guidance of multi-mode physiological signals, generates the individualized stimulation scheme more suitable for the users, and improves the training effect. The system can be applied in hospitals, communities or home environments, and a noninvasive, convenient and efficient solution is realized.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a rehabilitation training scheme based on transcranial time domain interference electrical stimulation according to an embodiment of the present application;
FIG. 2 is a flow chart of another transcranial time domain interference electrical stimulation-based rehabilitation training protocol according to an embodiment of the present application;
fig. 3 is a schematic diagram of a rehabilitation training system based on transcranial time-domain interferometry electrical stimulation according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
A rehabilitation training system based on transcranial time domain interferometry electrical stimulation according to an embodiment of the present application is described in detail below with reference to fig. 3. It should be noted that the following application scenarios are only shown for facilitating understanding of the spirit and principles of the present application, and embodiments of the present application are not limited in this respect. Rather, embodiments of the application may be applied to any scenario where applicable.
As shown in fig. 3, the system includes a physiological signal acquisition module for acquiring a multi-modal physiological signal of a user.
In an alternative embodiment, the multimodal physiological signal includes an electroencephalogram signal, an electromyographic signal, an actional acceleration signal, and the like. Wherein the brain electrical signals are used to assess the endogenous ganglion rhythm activity levels of the user, such as sensorimotor rhythms and the like. The electromyographic signals are used for evaluating the muscle activity level of the affected limb of the user, and the action acceleration signals are used for evaluating the movement and control capacity of the affected limb of the user, such as tremor degree and the like.
In one embodiment, the physiological signal acquisition module is a physiological signal acquisition device comprising a plurality of signal acquisition electrodes that, in use, ensure that the signal acquisition electrodes are properly placed on the user. This may include electroencephalogram electrodes, electromyogram electrodes, motion acceleration sensors, etc., which need to be placed in the proper position according to specific physiological signal acquisition requirements.
The embodiment of the application further comprises a software processing module which is used for determining a target stimulation target point of the user based on the multi-mode physiological signals and generating an electric stimulation parameter scheme of the target stimulation target point. The software processing module may be a computer device, and is configured to implement data processing.
In one embodiment, the software processing module comprises a target stimulus target determination unit for determining a target stimulus target of the user.
The method comprises the steps of preprocessing a multi-mode physiological signal, and extracting time-frequency characteristics and airspace characteristics of the preprocessed electroencephalogram signal to obtain the time-frequency characteristics and airspace characteristics of the electroencephalogram signal.
The multi-mode physiological signal data before rehabilitation training can be subjected to data fusion, firstly, the physiological signal data of different modes are subjected to downsampling to enable sampling rates to be consistent, and secondly, the physiological signal data of different modes are subjected to data splicing in a lead dimension to be fused into a two-dimensional matrix of lead X time. The multi-mode physiological signal data comprise brain electrical signals, electromyographic signals, action acceleration signals and the like before rehabilitation training of the user.
Further, preprocessing is carried out on a data matrix fused with a plurality of signals of the brain electrical signal, the electromyographic signal and the action acceleration signal, including low-pass filtering, power frequency notch, baseline drift removal and the like, so as to obtain preprocessed data.
Further, the preprocessed electroencephalogram data is subjected to time-frequency feature extraction, for example, time-frequency features are extracted by adopting methods such as filtering, fast Fourier Transform (FFT), wavelet transform and the like. Obtaining the time-frequency characteristic of the brain electrical signal.
And extracting airspace characteristics of the preprocessed electroencephalogram data, for example, extracting airspace characteristics by adopting a co-space mode (CSP), principal Component Analysis (PCA), independent Component Analysis (ICA), cluster statistical analysis (Maximum cluster-LEVEL MASS) and other methods to obtain airspace characteristics of the electroencephalogram data.
Further, based on the preprocessed electromyographic signals and the action acceleration signals, abnormal electroencephalogram data fragments and non-abnormal electroencephalogram data fragments are positioned and marked.
It will be appreciated that by analysing the pre-processed electromyographic signals and motion acceleration signals, abnormal electromyographic signals and abnormal motion acceleration signals can be obtained, for example, abnormal limb movements, abnormal muscle movements of the user at a certain moment, etc. By means of the abnormal action acceleration signal and the electromyographic signal, an abnormal electroencephalogram data fragment corresponding to the abnormal moment can be positioned.
Through the step, based on whether the electromyographic signals and the action acceleration signals are abnormal, abnormal electroencephalogram data fragments are positioned and marked, and labeling of electroencephalogram data is achieved.
Further, based on marked abnormal and non-abnormal electroencephalogram data fragments, time-frequency characteristics and airspace characteristics of electroencephalogram signals, tracing to locate abnormal brain areas, and determining target stimulation targets based on locating results.
In one embodiment, based on the existing target positioning navigation analysis model software, the marked abnormal and non-abnormal electroencephalogram data fragments, the time-frequency characteristics and the airspace characteristics of the electroencephalogram signals are input, and the positioned abnormal brain regions for generating the abnormal electroencephalogram data fragments are output. And determining a target stimulation target point based on the positioning result.
And inputting preprocessed electroencephalogram data, wherein the data comprises electroencephalogram fragments marked as abnormal and non-abnormal by utilizing target positioning navigation analysis model software. At the same time, time-frequency and spatial features of the signals are input, which may include power spectral density, coherence, phase lock values, etc. extracted from the electroencephalogram signals, and spatial features derived from Independent Component Analysis (ICA) or Principal Component Analysis (PCA). Based on the input data, an abnormal brain region that produces an abnormal electroencephalogram data fragment is located. And carrying out statistical test on the positioned abnormal brain regions, and clustering the regions with the most obvious statistical results as target stimulation targets.
In one embodiment, the software processing module further comprises a personalized digital head model generation unit. For generating electrical stimulation parameters during the training process.
Specifically, brain tissue segmentation is performed on an MRI structural image of a user, and a digital head model of the user is reconstructed based on the segmentation result.
The brain tissue segmentation is performed on the personalized MRI structure image of the user by software, and the process can adopt FSL-FAST or SPM and other tools, which can automatically segment the 3D image of the brain into different tissue types, including gray matter, white matter, cerebrospinal fluid and the like. This step is the basis of constructing an individualized digital head model, ensuring the accurate positioning of the subsequent electrical stimulation. Based on the segmentation result, an individualized digital head model is reconstructed.
Further, the digital head model is simulated by using a finite element method, electric field distribution of transcranial time domain interference electric stimulation is simulated, and a guide field of transcranial time domain interference electric stimulation is calculated based on the simulation method.
The digital head model is simulated by using a Finite Element Method (FEM) to calculate the electric field distribution in the transcranial time domain interferometry electrical stimulation (tTIS). The finite element method provides great freedom in the choice of dispersion, and smaller cells can be used in areas where the electric field gradient is large to improve the accuracy of the simulation. The electric field distribution obtained by calculation through a finite element method can determine the guiding field of the electric stimulus, namely the propagation path and distribution condition of the electric stimulus in brain tissue.
Based on the guiding field, an iterative optimization algorithm is adopted to generate optimal electrical stimulation parameters.
In the generated guide field, an iterative optimization algorithm such as a gradient descent method, an evolutionary algorithm, a genetic algorithm and the like is used for reversely deducing an optimal electric stimulation parameter scheme of a target stimulation target point.
Reverse derivation these algorithms optimize electrical stimulation parameters (e.g., amperage, stimulation duration, electrode position, etc.) through an iterative process to achieve optimal stimulation effects on the target brain region.
The method can provide a personalized interferential electric stimulation rehabilitation training scheme for a user by combining the personalized MRI image, the finite element method and the iterative optimization algorithm so as to achieve the optimal effect. By precisely controlling the electrical stimulation parameters, the pertinence and effectiveness of the regimen can be improved while reducing possible side effects.
In one embodiment, the software processing module further comprises a real-time regulation and control unit.
The real-time regulation and control unit is used for collecting multi-mode physiological signals in the rehabilitation training process of the user according to a preset period, inputting the multi-mode physiological signals into a pre-trained classification prediction model to obtain the activity state of the user, wherein the activity state comprises an abnormal activity state and a normal activity state, and when the user is in the abnormal activity state, the electric stimulation parameters are dynamically regulated based on the real-time multi-mode physiological signals.
In one embodiment, a classification prediction model is trained in advance. And acquiring various physiological signal data, and labeling the physiological signal data, wherein the physiological signal data are divided into physiological signal data corresponding to a normal activity state and physiological signal data corresponding to an abnormal activity state. And obtaining a training set based on the marked data. Based on the training set training classification prediction model, the classification prediction model can be a model structure in the forms of a convolutional neural network, a multi-layer perceptron and the like, and the application is not particularly limited.
Further, in the process that the user starts the rehabilitation training device, the multi-mode physiological signals of the user are collected at intervals of a preset period, and specifically, the interval period can be set according to actual requirements. The method comprises the steps of inputting the multi-mode physiological signals into a pre-trained classification prediction model to obtain the activity state of the identified user, and predicting that the current user is in an abnormal activity state based on the real-time multi-mode physiological signals, wherein the current user is required to be given with electric stimulation or in a normal activity state, and the current user is not required to be given with electric stimulation.
Further, when the user is in an abnormal active state, the electrical stimulation parameters are dynamically adjusted based on the real-time multi-mode physiological signals. Realizing self-adaptive electric stimulation rehabilitation training.
In an alternative embodiment, when the user is in an abnormal activity state, the real-time multi-mode physiological signals can be acquired through the signal acquisition equipment, and based on the real-time multi-mode physiological signals, the electric stimulation parameters are adjusted in real time through an iterative optimization algorithm by adopting an electric stimulation parameter generation unit in the software processing module. For example, the current magnitude is adjusted.
The application also comprises an electric stimulation module which is used for conducting transcranial time domain interference electric stimulation on the user based on the electric stimulation parameter scheme. Wherein the electrical stimulation module comprises stimulation electrodes for transcranial time domain interferential electrical stimulation. Is required to be worn on the head of the user.
In one implementation scenario, a user correctly wears signal acquisition electrodes of a multi-mode physiological signal acquisition device, correctly wears stimulation electrodes of transcranial time domain interferometry electrical stimulation, and performs electrical stimulation on the user based on an initially generated electrical stimulation parameter scheme.
In the process of starting the equipment to perform rehabilitation training, the multi-mode physiological signals of the user are collected at intervals in a preset period, and specifically, the interval period can be set according to actual requirements. The method comprises the steps of inputting the multi-mode physiological signals into a pre-trained classification prediction model to obtain the activity state of the identified user, and predicting that the current user is in an abnormal activity state based on the real-time multi-mode physiological signals, wherein the current user is required to be given with electric stimulation or in a normal activity state, and the current user is not required to be given with electric stimulation. When the user is in an abnormal active state, the electrical stimulation parameters are dynamically adjusted based on the real-time multi-mode physiological signals. Realizing self-adaptive electric stimulation rehabilitation training.
The whole rehabilitation training process is characterized by self-adaptability and real-time property. By monitoring and analyzing the physiological signals of the user in real time, the system can dynamically adjust the electrical stimulation parameters so as to adapt to the physiological state and rehabilitation requirements of the user which are constantly changed. This approach not only improves the therapeutic effect, but may also reduce unwanted side effects, as it provides electrical stimulation only when needed by the user.
In one embodiment, the system further comprises an evaluation module. Is used for collecting and evaluating the movement disorder symptom level before and after the rehabilitation training of the user.
Specifically, the movement disorder symptom levels before and after rehabilitation training of the user are respectively acquired and evaluated by using movement disorder clinical evaluation scales, wherein the clinical evaluation scales comprise a movement function evaluation scale, a movement ability evaluation scale, a tremor evaluation scale, a clinical tremor evaluation scale and the like. The present application is not particularly limited.
In an alternative embodiment, the movement disorder symptom level before and after rehabilitation training of the user can be further estimated according to the acquired multi-modal physiological signals.
In one embodiment, after the rehabilitation training is completed, an optimizing unit of the software processing module is further adopted and is used for carrying out regression analysis on the movement disorder symptom level and the multi-mode physiological signal which meet the preset difference before and after the rehabilitation training, obtaining the multi-mode physiological signal related to the movement disorder symptom level which meet the preset difference, and optimizing the electric stimulation parameter scheme based on the multi-mode physiological signal obtained by the regression analysis.
Specifically, by statistical test, the clinical evaluation results before and after rehabilitation training are compared with the multi-modal physiological signal characteristics, and the part with significant difference is identified. This step is an important step in assessing the efficacy of the treatment and identifying key physiological changes. Statistical tests can help determine which changes are significant, providing a basis for subsequent analysis.
Regression analysis is performed on clinical assessment results and multimodal physiological signal features identified in the statistical test as having significant differences. Regression analysis is a statistical method used to evaluate the strength and direction of the relationship between two or more variables. Through regression analysis, multimodal physiological signal characteristics associated with clinical assessment results with significant variation can be obtained.
And taking the multi-mode physiological signal characteristics obtained by regression analysis as model characteristics of a transcranial time domain interference electric stimulation on-line analysis algorithm guided by the multi-mode physiological signals in the next course of treatment. Thereby guiding the adjustment of the electrical stimulation parameters.
The application can precisely regulate and control the characteristics of deep nucleolus by utilizing transcranial time domain interference electric stimulation technology, and guide and optimize the stimulation scheme by combining clinical evaluation and multi-mode physiological signal characteristics, thereby realizing more effective, more stable and more durable alleviation of symptoms of dyskinesia. The method solves the limitation that the traditional transcranial electric stimulation technology cannot accurately stimulate deep lesions (such as thalamus, globus pallidus, hippocampus and the like), and simultaneously provides a more effective personalized stimulation scheme for users through the guidance of multi-mode physiological signals. The method can provide a noninvasive, convenient and efficient solution in the rehabilitation application of dyskinesia in hospitals, communities and families by combining the multi-mode physiological signal acquisition equipment and the transcranial time domain interference electrical stimulation software and hardware equipment.
According to another aspect of the embodiment of the application, a rehabilitation training method based on transcranial time domain interferometry electrical stimulation is also provided. As shown in fig. 1, the method includes:
S101, collecting multi-mode physiological signals of a user;
S102, determining a target stimulation target point of a user based on a multi-mode physiological signal, and generating an electric stimulation parameter scheme of the target stimulation target point;
s103, conducting transcranial time domain interferometry electric stimulation on the user based on the electric stimulation parameter scheme.
In one embodiment, determining a target stimulation target for a user based on a multimodal physiological signal comprises:
The time-frequency characteristic extraction and the airspace characteristic extraction are carried out on the preprocessed electroencephalogram signals to obtain the time-frequency characteristic and the airspace characteristic of the electroencephalogram signals;
Positioning and marking abnormal electroencephalogram data fragments and non-abnormal electroencephalogram data fragments based on the preprocessed electromyogram signals and the motion acceleration signals;
And tracing and positioning an abnormal brain region based on marked abnormal and non-abnormal brain electrical data fragments, time-frequency characteristics and airspace characteristics of brain electrical signals, and determining a target stimulation target point based on a positioning result.
In one embodiment, generating an electrical stimulation parameter profile for a target stimulation target includes:
Performing brain tissue segmentation on the MRI structure image of the user, and reconstructing a digital head model of the user based on the segmentation result;
simulating the digital head model by using a finite element method, simulating electric field distribution of transcranial time domain interference electric stimulation, and calculating a guide field of transcranial time domain interference electric stimulation based on the simulation method;
based on the guiding field, an iterative optimization algorithm is adopted to generate optimal electrical stimulation parameters.
In one embodiment, the method further comprises the steps of collecting multi-mode physiological signals in the rehabilitation training process of the user according to a preset period, inputting the multi-mode physiological signals into a pre-trained classification prediction model to obtain the activity state of the user, wherein the activity state comprises an abnormal activity state and a normal activity state, and when the user is in the abnormal activity state, the electric stimulation parameters are dynamically adjusted based on the real-time multi-mode physiological signals.
In one embodiment, the method further comprises collecting and evaluating the level of motor impairment symptoms of the user before and after rehabilitation.
In one embodiment, the method further comprises the steps of carrying out regression analysis on the movement disorder symptom level and the multi-mode physiological signals meeting the preset difference before and after rehabilitation training, obtaining multi-mode physiological signals related to the movement disorder symptom level meeting the preset difference, and optimizing an electric stimulation parameter scheme based on the multi-mode physiological signals obtained by the regression analysis.
To facilitate an understanding of the transcranial time domain interferential electrical stimulation based rehabilitation training method according to embodiments of the present application, it is further described below with reference to fig. 2.
The method is characterized by comprising the steps of firstly, acquiring and evaluating the symptom level of the dyskinesia by a pre-training clinical evaluation scale, acquiring and evaluating the multi-mode physiological signals before training, tracing and positioning target stimulation targets, analyzing a stimulation parameter scheme, performing rehabilitation training by using transcranial time domain interferometry electric stimulation guided by the multi-mode physiological signals, acquiring and evaluating the symptom level of the dyskinesia by the post-training clinical evaluation scale, acquiring and evaluating the multi-mode physiological signals after training, and statistically analyzing and optimizing the scheme before and after training.
It should be noted that, when the rehabilitation training system based on transcranial time domain interferometry electric stimulation provided in the above embodiment performs the rehabilitation training method based on transcranial time domain interferometry electric stimulation, only the division of the above functional modules is used for illustration, in practical application, the above functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the rehabilitation training system based on transcranial time domain interferometry electric stimulation provided in the above embodiment and the rehabilitation training method embodiment based on transcranial time domain interferometry electric stimulation belong to the same concept, and detailed implementation processes are shown in the system embodiment, which is not described here again.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A rehabilitation training system based on transcranial time domain interferometry electrical stimulation, comprising:
The physiological signal acquisition module is used for acquiring multi-mode physiological signals of a user;
The software processing module is used for determining a target stimulation target point of a user based on the multi-mode physiological signals and generating an electric stimulation parameter scheme of the target stimulation target point;
And the electric stimulation module is used for conducting transcranial time domain interference electric stimulation on the user based on the electric stimulation parameter scheme.
2. The system of claim 1, wherein the multi-modal physiological signal comprises an electroencephalogram signal, an electromyographic signal, an actional acceleration signal.
3. The system of claim 1, wherein the software processing module comprises:
The target stimulation target point determining unit is used for preprocessing the multi-mode physiological signals, extracting time-frequency characteristics and airspace characteristics of the preprocessed electroencephalogram signals to obtain the time-frequency characteristics and airspace characteristics of the electroencephalogram signals;
Positioning and marking abnormal electroencephalogram data fragments and non-abnormal electroencephalogram data fragments based on the preprocessed electromyogram signals and the motion acceleration signals;
and tracing and positioning an abnormal brain region based on marked abnormal and non-abnormal brain electrical data fragments, time-frequency characteristics and airspace characteristics of brain electrical signals, and determining the target stimulation target point based on a positioning result.
4. The system of claim 1, wherein the software processing module comprises:
the personalized digital head model generating unit is used for carrying out brain tissue segmentation on the MRI structure image of the user and reconstructing the digital head model of the user based on the segmentation result;
simulating the digital head model by using a finite element method, simulating electric field distribution of transcranial time domain interference electric stimulation, and calculating a guide field of transcranial time domain interference electric stimulation based on the simulation method;
and generating optimal electrical stimulation parameters by adopting an iterative optimization algorithm based on the guide field.
5. The system of claim 1, wherein the software processing module further comprises:
The real-time regulation and control unit is used for collecting multi-mode physiological signals in the rehabilitation training process of the user according to a preset period;
Inputting the multi-mode physiological signals into a pre-trained classification prediction model to obtain the activity state of a user, wherein the activity state comprises an abnormal activity state and a normal activity state;
And when the user is in an abnormal activity state, dynamically adjusting the electrical stimulation parameters based on the real-time multi-mode physiological signals.
6. The system of claim 1, further comprising:
And the evaluation module is used for collecting and evaluating the movement disorder symptom level before and after the rehabilitation training of the user.
7. The system of claim 6, wherein the software processing module further comprises:
The optimizing unit is used for carrying out regression analysis on the movement disorder symptom level and the multi-mode physiological signals which meet the preset difference before and after the rehabilitation training, and obtaining the multi-mode physiological signals related to the movement disorder symptom level which meet the preset difference;
based on the multi-mode physiological signals obtained by regression analysis, the electric stimulation parameter scheme is optimized.
8. A rehabilitation training method based on transcranial time domain interferometry electrical stimulation is characterized by comprising the following steps:
Collecting multi-mode physiological signals of a user;
determining a target stimulation target point of a user based on the multi-mode physiological signals, and generating an electrical stimulation parameter scheme of the target stimulation target point;
based on the electrical stimulation parameter scheme, transcranial time domain interferometry electrical stimulation is performed on the user.
9. The method of claim 8, wherein determining a target stimulation target for the user based on the multi-modal physiological signal comprises:
Performing time-frequency feature extraction and airspace feature extraction on the preprocessed electroencephalogram signals to obtain time-frequency features and airspace features of the electroencephalogram signals;
Positioning and marking abnormal electroencephalogram data fragments and non-abnormal electroencephalogram data fragments based on the preprocessed electromyogram signals and the motion acceleration signals;
and tracing and positioning an abnormal brain region based on marked abnormal and non-abnormal brain electrical data fragments, time-frequency characteristics and airspace characteristics of brain electrical signals, and determining the target stimulation target point based on a positioning result.
10. The method of claim 8, wherein generating the electrical stimulation parameter profile for the target stimulation target comprises:
Performing brain tissue segmentation on the MRI structure image of the user, and reconstructing a digital head model of the user based on the segmentation result;
simulating the digital head model by using a finite element method, simulating electric field distribution of transcranial time domain interference electric stimulation, and calculating a guide field of transcranial time domain interference electric stimulation based on the simulation method;
and generating optimal electrical stimulation parameters by adopting an iterative optimization algorithm based on the guide field.
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| CN120412911A (en) * | 2025-07-02 | 2025-08-01 | 杭州市第三人民医院(杭州市惠民医院、浙江中医药大学附属杭州第三医院) | Device for improving needle electrode treatment effect |
| CN120412911B (en) * | 2025-07-02 | 2025-10-14 | 杭州市第三人民医院(杭州市惠民医院、浙江中医药大学附属杭州第三医院) | Device for improving needle electrode treatment effect |
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| CN120285454A (en) * | 2025-06-12 | 2025-07-11 | 脉景（杭州）健康管理有限公司 | A human brain transcranial stimulation circuit and a human brain transcranial stimulation method |
| CN120412911A (en) * | 2025-07-02 | 2025-08-01 | 杭州市第三人民医院(杭州市惠民医院、浙江中医药大学附属杭州第三医院) | Device for improving needle electrode treatment effect |
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