Pelvic floor disorder electroencephalogram data acquisition system

Abstract

The invention discloses a pelvic floor disorder electroencephalogram data acquisition system, which comprises an experimental normal form module, a data acquisition module and a data processing module, wherein the experimental normal form module is used for designing an experimental normal form; the audio and video technology module is used for presenting each stage in the experiment normal form through an audio and video technology and guiding a user to execute a task according to requirements; the electroencephalogram acquisition module is used for acquiring electroencephalogram data of the user in each stage of executing the experiment normal form by utilizing electroencephalogram acquisition equipment; the data preprocessing module is used for preprocessing the collected electroencephalogram data; and the data analysis module is used for carrying out feature extraction, classification identification and correlation analysis on the preprocessed electroencephalogram data so as to realize comprehensive evaluation on the pelvic floor muscle function state. According to the pelvic floor disorder electroencephalogram data acquisition system, the experiment normal form comprehensively covers resting state, memory, anus contraction, defecation and other tasks, audio and video guidance is combined, it is ensured that a user accurately executes and acquires high-quality electroencephalogram data, and a scientific basis is provided for pelvic floor function accurate evaluation and personalized rehabilitation.

Description

Technical Field

[0001] The present invention specifically relates to a pelvic floor disorder electroencephalogram data acquisition system. Background Art

[0002] Pelvic floor dysfunction is a growing concern in today's medical field, especially for individuals who have experienced pregnancy and childbirth. The pelvic floor muscles are a group of muscles that support the organs in the pelvis, including the urethra, bladder, rectum, and uterus. The health of these muscles is directly related to an individual's urinary control, sexual function, and maintenance of organ position. Pelvic floor dysfunction includes symptoms such as urinary incontinence, fecal incontinence, and pelvic organ prolapse, affecting millions of people around the world, especially women. This problem has become more prominent as the population ages and fertility rates change.

[0003] Traditional diagnostic methods for pelvic floor dysfunction often focus on a single indicator, such as pressure measurement or electromyographic signal analysis, which makes it difficult to fully evaluate the functional status of the pelvic floor muscles and has certain limitations. Therefore, it is particularly important to develop a collection method that can comprehensively evaluate the functional status of the pelvic floor muscles. Summary of the invention

[0004] The present invention provides a pelvic floor disorder EEG data acquisition system to solve the above-mentioned technical problems, and specifically adopts the following technical solutions:

[0005] A pelvic floor disorder EEG data acquisition system, comprising:

[0006] An experimental paradigm module, used to design an experimental paradigm, wherein the experimental paradigm includes a resting state, short-term memory, imagined anal contraction, simulated anal contraction, imagined defecation, and simulated defecation tasks;

[0007] The audio and video technology module is used to present the various stages of the experimental paradigm through audio and video technology to guide users to perform tasks as required;

[0008] The EEG acquisition module is used to collect the EEG data of the user at various stages of the experimental paradigm using the EEG acquisition equipment;

[0009] The data preprocessing module is used to preprocess the collected EEG data, including steps such as removing artifacts, filtering and denoising;

[0010] The data analysis module is used to perform feature extraction, classification recognition and correlation analysis on the preprocessed EEG data to achieve a comprehensive assessment of the functional status of the pelvic floor muscles.

[0011] Furthermore, the experimental paradigm designed by the experimental paradigm module specifically includes:

[0012] Resting task: The user remains relaxed and does not perform any pelvic floor muscle activity, which is used to collect the user's baseline EEG data;

[0013] Short-term memory task: users memorize numbers or images presented by audio and video technology, which is used to collect EEG data of users during the memory process;

[0014] Imagine anal contraction task: users imagine themselves doing anal contraction, that is, contraction of pelvic floor muscles, and relevant instructions or images are presented through audio and video technology to help users better understand and perform the imaginary task;

[0015] Simulated anal contraction task: The user actually performs anal contraction, i.e. contraction of the pelvic floor muscles, and the instructions or images presented by audio and video technology ensure that the user performs the action correctly;

[0016] Imagine defecation task: users imagine themselves having a bowel movement, that is, relaxing the pelvic floor muscles, and relevant instructions or images are presented through audio and video technology to help users better understand and perform the imaginary task;

[0017] Simulated defecation task: The user actually performs the defecation action, that is, relaxes the pelvic floor muscles, and the instructions or images presented by audio and video technology ensure that the user performs the action correctly.

[0018] Furthermore, the audio and video technology module includes a video player, an audio player and a display screen, which are used to present each stage in the experimental paradigm in a visual and auditory way to ensure that users can accurately understand and perform tasks.

[0019] Furthermore, the EEG acquisition module uses an electroencephalograph, and the electrode placement follows the international standard 10-20 electrode system to comprehensively acquire EEG data from different brain regions.

[0020] Furthermore, the data preprocessing module uses a method combining time domain analysis and frequency domain analysis to extract statistical characteristics such as mean, variance, kurtosis, skewness and power spectrum density characteristics of each frequency band of the EEG signal.

[0021] Furthermore, the data analysis module uses a support vector machine algorithm to classify and identify feature vectors to obtain classification results of the functional status of the pelvic floor muscles.

[0022] Furthermore, the data analysis module calculates indicators such as correlation coefficient and regression coefficient between the EEG signal and the pelvic floor muscle activity, and evaluates the correlation between the EEG signal and the pelvic floor muscle activity.

[0023] Furthermore, the system further comprises a user feedback module, which comprises an emotion recognition submodule and a task adjustment submodule, wherein:

[0024] The emotion recognition submodule is used to monitor the user's emotional state in real time through voice emotion analysis or facial expression recognition technology;

[0025] The task adjustment submodule is used to automatically adjust the task difficulty in the experimental paradigm or provide psychological support according to the monitoring results of the emotion recognition submodule to improve the user's participation and rehabilitation effect.

[0026] Furthermore, the system further comprises a physiological monitoring module, which comprises a multi-physiological parameter fusion analysis submodule and a real-time health warning submodule, wherein:

[0027] The multi-physiological parameter fusion analysis submodule is used to comprehensively analyze physiological data such as heart rate, blood pressure, blood oxygen saturation and EEG data, and to mine the potential correlation between these physiological parameters through machine learning algorithms to more comprehensively evaluate the user's health status;

[0028] The real-time health warning submodule is used to immediately issue an alarm when an abnormal physiological state of the user is detected, and suspend the current rehabilitation task to ensure the safety of the user.

[0029] Furthermore, the system also includes an intelligent diagnosis module, which includes a personalized rehabilitation program generation submodule, a telemedicine integration submodule and a rehabilitation effect prediction submodule, wherein:

[0030] The personalized rehabilitation program generation submodule is used to generate a personalized rehabilitation program based on the user's EEG data, physiological data and user feedback data, and the program includes rehabilitation training suggestions, lifestyle adjustments and dietary suggestions;

[0031] The telemedicine integration submodule is used to integrate the system with the cloud platform, so that doctors can remotely monitor the user's rehabilitation progress and adjust the rehabilitation plan in real time. At the same time, the cloud platform's big data analysis function is used to collect and analyze a large number of users' rehabilitation data to provide support for clinical research and personalized treatment;

[0032] The rehabilitation effect prediction submodule is used to use historical data and machine learning algorithms to predict the user's rehabilitation effect under different rehabilitation programs, helping doctors and patients to choose the most suitable rehabilitation path and improve rehabilitation efficiency.

[0033] The benefit of the present invention lies in the pelvic floor disorder EEG data acquisition system provided. The experimental paradigm comprehensively covers tasks such as resting state, memory, anal contraction, defecation, etc., combined with audio and video guidance to ensure that users execute accurately and collect high-quality EEG data, providing a scientific basis for accurate evaluation of pelvic floor function and personalized rehabilitation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 It is a schematic diagram of the pelvic floor disorder EEG data acquisition system of the present application. DETAILED DESCRIPTION

[0036] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0037] like Figure 1 The figure shows a pelvic floor disorder EEG data acquisition system of the present application, which includes: an experimental paradigm module, an audio and video technology module, an EEG acquisition module, a data preprocessing module and a data analysis module.

[0038] Among them, the experimental paradigm module is used to design experimental paradigms, which include resting state, short-term memory, imaginary anal contraction, simulated anal contraction, imaginary defecation and simulated defecation tasks. The audio and video technology module is used to present the various stages of the experimental paradigm through audio and video technology, and guide users to perform tasks as required. The EEG acquisition module is used to use EEG acquisition equipment to collect EEG data of users at various stages of the experimental paradigm. The data preprocessing module is used to preprocess the collected EEG data, including steps such as removing artifacts, filtering and denoising. The data analysis module is used to perform feature extraction, classification recognition and correlation analysis on the preprocessed EEG data to achieve a comprehensive assessment of the functional status of the pelvic floor muscles.

[0039] The experimental paradigm includes six stages: resting state, short-term memory, imaginary anal contraction, simulated anal contraction, imaginary defecation and simulated defecation tasks. Each stage has clear instructions and audio and video technology presentation to ensure that users can accurately understand and execute. In the implementation mode of this application, the experimental paradigm designed by the experimental paradigm module specifically includes:

[0040] Resting state task: The user remains relaxed and does not perform any pelvic floor muscle activity. This is used to collect the user's baseline EEG data. The resting state is the starting stage of the experiment. The user needs to remain relaxed and not perform any pelvic floor muscle activity. This stage is used to collect the user's baseline EEG data to provide a reference for subsequent analysis.

[0041] Short-term memory task: Users memorize numbers or images presented by audio and video technology, which is used to collect EEG data of users during the memory process. In the short-term memory stage, users need to memorize numbers or images presented by audio and video technology. This stage is used to collect EEG data of users during the memory process to analyze the impact of memory activities on pelvic floor muscle function.

[0042] Imagine anal contraction task: The user imagines himself doing anal contraction, that is, contraction of the pelvic floor muscles. Relevant instructions or images are presented through audio and video technology to help users better understand and perform the imaginary task. The EEG data collected at this stage can be used to analyze the impact of the imaginary action on the pelvic floor muscle function.

[0043] Simulated anal contraction task: The user actually performs anal contraction, that is, the contraction of the pelvic floor muscles, and the instructions or images presented by audio and video technology ensure that the user performs the action correctly. The EEG data collected at this stage can be used to analyze the impact of the actual action on the pelvic floor muscle function.

[0044] Imagine defecation task: The user imagines that they are having a bowel movement, that is, relaxing the pelvic floor muscles. Relevant instructions or images are presented through audio and video technology to help users better understand and perform the imaginary task. In the imagine defecation stage, the user needs to imagine that they are having a bowel movement, that is, relaxing the pelvic floor muscles. In this stage, relevant instructions or images are presented through audio and video technology to help users better understand and perform the imaginary task. By collecting EEG data at this stage, the impact of imaginary actions on pelvic floor muscle function can be analyzed.

[0045] Simulated defecation task: The user actually performs the defecation action, that is, the relaxation of the pelvic floor muscles, and the instructions or images presented by audio and video technology ensure that the user performs the action correctly. The EEG data collected at this stage can be used to analyze the impact of the actual action on the pelvic floor muscle function.

[0046] The audio and video technology module is used to present each stage in the experimental paradigm through audio and video technology, and guide the user to perform the task as required. In the embodiment of the present application, the audio and video technology module includes a video player, an audio player and a display screen, which are used to present each stage in the experimental paradigm in a visual and auditory way to ensure that the user can accurately understand and perform the task. Video presentation is used to show action demonstrations and instructions. For example, in the stages of imagining anal contraction and simulating anal contraction, the relevant pelvic floor muscle contraction action demonstration is played by a video player to help users better understand and perform actions. In the short-term memory stage, a series of numbers or images are quickly flashed through a video player for users to remember. Audio presentation is used to play instructions and prompts. For example, at the beginning and end of each stage, the corresponding instructions are played through an audio player to prompt the user to enter the next stage or end the current stage. In the stages of imagining anal contraction, simulating anal contraction, imagining defecation and simulating defecation, the relevant pelvic floor muscle activity instructions are played through an audio player to help users better understand and perform actions.

[0047] In the embodiment of the present application, the EEG acquisition module adopts an electroencephalograph, and the electrode placement follows the 10-20 electrode system of international standards to comprehensively collect EEG data from different brain regions. Specifically, electrode placement is one of the key steps in EEG data acquisition. According to the 10-20 electrode system of international standards, the electrodes on the electrode cap are placed at specific positions on the user's scalp. These positions include areas such as the frontal lobe, central area, parietal lobe, occipital lobe and temporal lobe to comprehensively collect EEG data from different brain regions. Data recording is the process of recording and storing the collected EEG signals. The amplified EEG signals are converted into digital signals by a data acquisition card and stored in a computer. The recorded data includes information such as the amplitude, frequency, phase, etc. of the EEG signals for subsequent data analysis and processing.

[0048] In the implementation manner of the present application, the data preprocessing module uses a method combining time domain analysis and frequency domain analysis to extract statistical characteristics such as mean, variance, kurtosis, skewness and power spectral density characteristics of the EEG signal and each frequency band.

[0049] Artifact removal: Artifacts refer to abnormal signals caused by user head movement, poor electrode contact, etc., which need to be removed by an artifact removal algorithm.

[0050] Filtering and denoising: Filtering and denoising are to remove high-frequency noise and low-frequency interference in EEG signals and improve the signal-to-noise ratio.

[0051] In an implementation manner of the present application, the data analysis module uses a support vector machine algorithm to classify and identify feature vectors to obtain a classification result of the pelvic floor muscle function status.

[0052] In an embodiment of the present application, the data analysis module calculates indicators such as the correlation coefficient and regression coefficient between the EEG signal and the pelvic floor muscle activity, and evaluates the correlation between the EEG signal and the pelvic floor muscle activity.

[0053] This module includes steps such as feature extraction, classification recognition and correlation analysis to achieve a comprehensive assessment of the functional status of the pelvic floor muscles.

[0054] Feature extraction: Feature extraction is to extract characteristic information related to pelvic floor muscle function from EEG data. These characteristic information include parameters such as amplitude, frequency, phase of EEG signals, as well as indicators such as connectivity and synchronization between different brain regions. Through feature extraction, complex EEG data can be converted into concise feature vectors, providing a basis for subsequent classification, recognition and correlation analysis.

[0055] Classification and recognition: Classification and recognition is the process of classifying and identifying the extracted feature vectors. Through machine learning algorithms, such as support vector machines (SVM) and neural networks (NN), the feature vectors are classified into different categories to achieve classification and recognition of the functional status of the pelvic floor muscles. The results of classification and recognition can be used to evaluate the functional status of the user's pelvic floor muscles, such as normal, mild disorder, severe disorder, etc.

[0056] Correlation analysis: Correlation analysis is to analyze the connectivity and synchronization between different brain regions, as well as the correlation between EEG signals and pelvic floor muscle activity. By calculating indicators such as coherence and mutual information between different brain regions, the connectivity and synchronization between different brain regions can be evaluated. At the same time, by calculating indicators such as the correlation coefficient or regression coefficient between EEG signals and pelvic floor muscle activity, the correlation between EEG signals and pelvic floor muscle activity can be evaluated. These analysis results can provide important basis for further understanding the neural mechanism of the functional state of pelvic floor muscles.

[0057] In the implementation of the present application, the system also includes a user feedback module, which is used to collect the user's subjective feelings in the process of performing the task in real time, and optimize the experimental paradigm and evaluation results according to the user's feedback information. The user feedback module includes an emotion recognition submodule and a task adjustment submodule, wherein:

[0058] The emotion recognition submodule is used to monitor the user's emotional state in real time through voice emotion analysis or facial expression recognition technology. For example, the system can use deep learning algorithms to analyze the user's voice tone or facial expressions to identify the user's emotional changes, such as anxiety, fatigue, or happiness.

[0059] The task adjustment submodule is used to automatically adjust the task difficulty in the experimental paradigm or provide psychological support based on the monitoring results of the emotion recognition submodule to improve the user's participation and rehabilitation effect. For example, if the system detects that the user is anxious, it can automatically reduce the task difficulty or provide encouraging voice feedback to improve the user's participation and rehabilitation effect.

[0060] In the implementation of the present application, the system also includes a physiological monitoring module for real-time monitoring of the user's heart rate, blood pressure, blood oxygen saturation and other physiological data, and synchronously analyzing these data with EEG data to more comprehensively evaluate the user's health status. The physiological monitoring module includes a multi-physiological parameter fusion analysis submodule and a real-time health warning submodule, wherein:

[0061] The multi-physiological parameter fusion analysis submodule is used to comprehensively analyze physiological data such as heart rate, blood pressure, and blood oxygen saturation with EEG data, and to mine the potential correlation between these physiological parameters through machine learning algorithms to more comprehensively evaluate the user's health status. For example, the system can analyze the relationship between heart rate variability and EEG activity to reveal potential physiological and psychological states.

[0062] The real-time health warning submodule is used to immediately issue an alarm when an abnormal physiological state of the user is detected, and suspend the current rehabilitation task to ensure the safety of the user. For example, if the system detects that the user's heart rate is too high or blood pressure is abnormal, an alarm will be issued immediately and the rehabilitation task will be suspended until the user's state returns to normal.

[0063] In the implementation of the present application, the system also includes an intelligent diagnosis module, which is used to generate a personalized diagnosis report based on EEG data, physiological data and user feedback data, and recommend a corresponding rehabilitation plan. The intelligent diagnosis module includes a personalized rehabilitation plan generation submodule, a telemedicine integration submodule and a rehabilitation effect prediction submodule, wherein:

[0064] The personalized rehabilitation program generation submodule is used to generate a personalized rehabilitation program based on the user's EEG data, physiological data and user feedback data. The program includes rehabilitation training suggestions, lifestyle adjustments and dietary suggestions. For example, the system can recommend a pelvic floor muscle training plan suitable for the user based on the user's EEG data and physiological data, and provide dietary and lifestyle adjustment suggestions.

[0065] The telemedicine integration submodule is used to integrate the system with the cloud platform, enabling doctors to remotely monitor the user's rehabilitation progress and adjust the rehabilitation plan in real time. At the same time, the cloud platform's big data analysis function is used to collect and analyze a large amount of user rehabilitation data to provide support for clinical research and personalized treatment. For example, doctors can view users' rehabilitation data in real time through the cloud platform and adjust rehabilitation plans as needed.

[0066] The rehabilitation effect prediction submodule is used to use historical data and machine learning algorithms to predict the rehabilitation effect of users under different rehabilitation programs, helping doctors and patients choose the most suitable rehabilitation path and improve rehabilitation efficiency. For example, the system can predict the rehabilitation effect of users under different rehabilitation programs based on the user's EEG data and physiological data, and provide reference for doctors and patients.

[0067] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A pelvic floor disorder EEG data acquisition system, characterized in that: include: An experimental paradigm module, used to design an experimental paradigm, wherein the experimental paradigm includes a resting state, short-term memory, imagined anal contraction, simulated anal contraction, imagined defecation, and simulated defecation tasks; The audio and video technology module is used to present the various stages of the experimental paradigm through audio and video technology to guide users to perform tasks as required; The EEG acquisition module is used to collect the EEG data of the user at various stages of the experimental paradigm using the EEG acquisition equipment; The data preprocessing module is used to preprocess the collected EEG data, including steps such as removing artifacts, filtering and denoising; The data analysis module is used to perform feature extraction, classification recognition and correlation analysis on the preprocessed EEG data to achieve a comprehensive assessment of the functional status of the pelvic floor muscles.

2. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The experimental paradigm designed by the experimental paradigm module specifically includes: Resting task: The user remains relaxed and does not perform any pelvic floor muscle activity, which is used to collect the user's baseline EEG data; Short-term memory task: users memorize numbers or images presented by audio and video technology, which is used to collect EEG data of users during the memory process; Imagine anal contraction task: users imagine themselves doing anal contraction, that is, contraction of pelvic floor muscles, and relevant instructions or images are presented through audio and video technology to help users better understand and perform the imaginary task; Simulated anal contraction task: The user actually performs anal contraction, i.e. contraction of the pelvic floor muscles, and the instructions or images presented by audio and video technology ensure that the user performs the action correctly; Imagine defecation task: users imagine themselves having a bowel movement, that is, relaxing the pelvic floor muscles, and relevant instructions or images are presented through audio and video technology to help users better understand and perform the imaginary task; Simulated defecation task: The user actually performs the defecation action, that is, relaxes the pelvic floor muscles, and the instructions or images presented by audio and video technology ensure that the user performs the action correctly.

3. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The audio and video technology module includes a video player, an audio player and a display screen, which are used to present each stage of the experimental paradigm in a visual and auditory way to ensure that users can accurately understand and perform tasks.

4. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The EEG acquisition module uses an electroencephalograph, and the electrode placement follows the international standard 10-20 electrode system to comprehensively collect EEG data from different brain regions.

5. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The data preprocessing module uses a method combining time domain analysis and frequency domain analysis to extract statistical features such as mean, variance, kurtosis, skewness, and power spectrum density features of each frequency band of the EEG signal.

6. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The data analysis module uses a support vector machine algorithm to classify and identify feature vectors to obtain a classification result of the pelvic floor muscle function status.

7. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The data analysis module calculates indicators such as correlation coefficient and regression coefficient between the EEG signal and the pelvic floor muscle activity, and evaluates the correlation between the EEG signal and the pelvic floor muscle activity.

8. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The system further includes a user feedback module, which includes an emotion recognition submodule and a task adjustment submodule, wherein: The emotion recognition submodule is used to monitor the user's emotional state in real time through voice emotion analysis or facial expression recognition technology; The task adjustment submodule is used to automatically adjust the task difficulty in the experimental paradigm or provide psychological support according to the monitoring results of the emotion recognition submodule to improve the user's participation and rehabilitation effect.

9. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The system further includes a physiological monitoring module, which includes a multi-physiological parameter fusion analysis submodule and a real-time health warning submodule, wherein: The multi-physiological parameter fusion analysis submodule is used to comprehensively analyze physiological data such as heart rate, blood pressure, blood oxygen saturation and EEG data, and to mine the potential correlation between these physiological parameters through machine learning algorithms to more comprehensively evaluate the user's health status; The real-time health warning submodule is used to immediately issue an alarm when an abnormal physiological state of the user is detected, and suspend the current rehabilitation task to ensure the safety of the user.

10. The pelvic floor disorder EEG data acquisition system according to claim 1, characterized in that: The system also includes an intelligent diagnosis module, which includes a personalized rehabilitation program generation submodule, a telemedicine integration submodule and a rehabilitation effect prediction submodule, wherein: The personalized rehabilitation program generation submodule is used to generate a personalized rehabilitation program based on the user's EEG data, physiological data and user feedback data, and the program includes rehabilitation training suggestions, lifestyle adjustments and dietary suggestions; The telemedicine integration submodule is used to integrate the system with the cloud platform, so that doctors can remotely monitor the user's rehabilitation progress and adjust the rehabilitation plan in real time. At the same time, the cloud platform's big data analysis function is used to collect and analyze a large number of users' rehabilitation data to provide support for clinical research and personalized treatment; The rehabilitation effect prediction submodule is used to use historical data and machine learning algorithms to predict the user's rehabilitation effect under different rehabilitation programs, helping doctors and patients to choose the most suitable rehabilitation path and improve rehabilitation efficiency.

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