Epilepsy detection device

Abstract

The invention provides an epilepsy detection device, and relates to a medical equipment technology. The epilepsy detection device comprises a wearable device, the wearable device comprises a data acquisition module, a data processing unit, a communication module, a software algorithm and a system platform, and the data acquisition module comprises an electromyogram sensor, a heart rate monitoring sensor, an accelerometer, a gyroscope and a skin electrical activity sensor. The data processing unit comprises a microprocessor and a memory, the communication module comprises a Bluetooth / Wi-Fi module and a mobile application / cloud platform, and the software algorithm comprises a signal processing algorithm and an optimization algorithm. By combining medical history data of a patient, including epileptic seizure history, seizure type, frequency, duration time, preamble before seizure, symptoms after seizure and the like of the patient, genetic history and medical history of epilepsy or other related diseases in a family, a more personalized model is trained, and more accurate monitoring and early warning are provided for the patient.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to an epilepsy detection device. Background Art

[0002] Epilepsy is a common chronic brain disease characterized by recurrent epileptic seizures that are caused by abnormal electrical activity in the brain. Epileptic seizures can take many different forms, from mild convulsions to generalized convulsions and loss of consciousness. The type and severity of epileptic seizures vary from person to person, and seizures may occur at any time. Epilepsy is a controllable but usually incurable disease. With proper treatment and management, many patients can lead a normal life. Usually, professional detection equipment, such as electroencephalogram equipment, can be used to detect abnormal discharges of brain neurons in epileptic patients, and then determine whether the epileptic patient is in a state of epileptic seizure. However, due to the recurrence and suddenness of epilepsy, the above-mentioned epilepsy detection methods cannot achieve real-time detection of the onset of epilepsy in patients, so wearable epilepsy detection devices came into being.

[0003] There are still some problems in the use of existing epilepsy detection devices. Due to the limitations of the algorithm, the device may not be able to accurately distinguish between epileptic seizures and other similar symptoms, resulting in the misidentification of non-epileptic seizures or failure to identify true epileptic seizures. It cannot combine factors such as the patient's medical history and genetic factors for detection, thereby reducing accuracy. Therefore, those skilled in the art provide an epilepsy detection device to solve the problems raised in the above background technology. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides an epilepsy detection device that solves the problem that due to the limitations of the algorithm, the device may not be able to accurately distinguish between epileptic seizures and other similar symptoms, resulting in the incorrect identification of non-epileptic seizures or failure to identify true epileptic seizures, and cannot perform detection based on factors such as the patient's medical history and genetic factors, thereby reducing the accuracy.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an epilepsy detection device, including a wearable device, the wearable device including: a data acquisition module, a data processing unit, a communication module, a software algorithm and a system platform;

[0008] The data acquisition module includes an electromyography sensor for detecting electrical activity in wrist muscles, which may be abnormal during an epileptic seizure;

[0009] Heart rate monitoring sensors that monitor heart rate variability to identify changes in the autonomic nervous system that may be associated with seizures;

[0010] an accelerometer and gyroscope to detect movement and changes in orientation of the wrist, which can accompany involuntary movements during a seizure;

[0011] Electrodermal activity sensors, which monitor changes in skin conductivity that may be associated with autonomic nervous system activity;

[0012] The data processing unit includes a microprocessor for processing data from the sensor in real time and performing algorithm analysis;

[0013] Memory, used to store the collected data for subsequent analysis or retrospective review;

[0014] The communication module includes a Bluetooth / Wi-Fi module for wirelessly transmitting data to a smartphone, tablet computer or cloud server;

[0015] Mobile applications / cloud platforms for real-time monitoring, analysis and storage of data;

[0016] The software algorithms include signal processing algorithms for extracting useful information from sensor data and identifying characteristics of epileptic seizures;

[0017] Optimize the algorithm, optimize the algorithm of the wearable epilepsy detection device to improve the accuracy and reliability of detection. The detailed steps of the optimization algorithm are as follows:

[0018] S1. Medical and genetic history collection, through face-to-face interviews, direct conversations with patients and their families, and review of patients' medical records, previous diagnosis reports and treatment records, etc.;

[0019] S2. Wearing and data collection of wearable devices. Patients wear wearable devices under the guidance of doctors, and the devices continuously monitor the physiological signals of patients.

[0020] S3. Data preprocessing, data cleaning, noise and outliers removal, data quality assurance, feature extraction, extracting useful heart rate variability, electromyographic activity pattern features from raw data, principal component analysis:

[0021] X new =X·W

[0022] Among them, X is the original data matrix, W is the principal component matrix, X new This is the data after Jiang Wei;

[0023] S4. Model optimization, select appropriate machine learning models, support vector machines, random forests and neural networks, hyperparameter tuning, use grid search, random search or Bayesian optimization methods to tune hyperparameters, use training data sets to train models, and use validation sets to evaluate model performance. The support vector machine optimization objective function is:

[0024]

[0025] subjecttoy i (w·x i +b)≥1, for all i=1,…,n

[0026] Among them, w is the weight vector, b is the bias term, and x i is the eigenvector, y i is the corresponding label;

[0027] S5. Ensemble learning, training multiple models, using different algorithms or different data subsets to train multiple models, ensemble strategies, using voting, weighted average or stacking strategies to integrate the prediction results of multiple models, random forest, integrating the average prediction results of multiple decision trees:

[0028]

[0029] in, is the prediction function, f b (x) is the prediction result of the bth decision tree, and B is the total number of trees;

[0030] S6. Deep learning, design a neural network structure suitable for the problem, train the neural network using the training data set, and verify it using the validation set, apply regularization techniques and optimization algorithms to improve model performance, and neural network forward propagation:

[0031] a[ι]=g(z[ι])

[0032] Where a[ι] is the activation value of the ιth layer, z[ι] is the weighted input, and g is the activation function;

[0033] S7. Model evaluation and iteration: Use the test set to evaluate model performance, pay attention to accuracy, recall and F1 score indicators, adjust the model structure or parameters according to the evaluation results, and perform iterative optimization;

[0034] S8. Warning and feedback. When signs of an epileptic seizure are detected, the system issues a warning to the patient or caregiver, and collects feedback from the warning system for further optimization of the model and algorithm. Through feature engineering and model optimization, the model's prediction accuracy for epileptic seizures can be improved, and false positives and negatives can be reduced. The optimized model performs better on unseen data and has stronger generalization capabilities. The optimized model structure may be simpler, with faster prediction speed, suitable for real-time monitoring. Integrated learning and deep learning methods can improve the stability and reliability of the model on different data sets.

[0035] Preferably, the medical history collection in step S1. further includes: basic information, patient name, gender, age, contact information, home address, work environment and living habits;

[0036] Chief complaint, the main symptoms and problems described by the patient or family;

[0037] History of present illness, time of onset, duration, frequency of attacks, specific manifestations during attacks, factors that aggravate or relieve symptoms, stress, sleep and diet;

[0038] Past medical history, including diagnosed diseases, surgical history, hospitalization history, and past treatment experience, including drug treatment, surgical treatment, and other treatment methods;

[0039] Personal history, lifestyle habits, such as diet, exercise, and sleeping habits, social environment, such as work pressure, interpersonal relationships, and family status;

[0040] History of allergies, allergies to medications, foods, or other substances;

[0041] Review of systems, a comprehensive review of all body systems to see if there are other concerning symptoms;

[0042] Family history: Ask about the health status of the patient's family members, especially immediate family members, to find out if there are similar symptoms or confirmed diseases in the family;

[0043] History of genetic diseases: ask if there is a history of genetic diseases and find out if there are cases of early-onset or genetic diseases in the family;

[0044] The health status of family members, including age, health status and cause of death, is collected and combined with the patient's medical history data, including the patient's epileptic seizure history, seizure type, frequency, duration, pre-seizure signs, post-seizure symptoms, genetic history, and family history of epilepsy or other related diseases, to train a more personalized model to provide patients with more accurate monitoring and early warning.

[0045] Preferably, the data preprocessing in step S3. further includes the following steps:

[0046] A. Denoising, using filters to remove noise from the signal;

[0047] Low pass filter:

[0048] High Pass Filter:

[0049] Bandpass filter, a combination of low-pass and high-pass filters;

[0050] Median filter, for discrete signal x[n], median filter output y[n] = median{x[nk], ..., x[n], ..., x[n = k]};

[0051] B. Feature extraction, Fourier transform, convert the signal from time domain to frequency domain:

[0052]

[0053] Fast Fourier transform, quickly calculate the Fourier transform of a discrete signal:

[0054]

[0055] C. Signal enhancement, adaptive filter, minimum mean square error algorithm:

[0056] w n+1 =w n +2μe[n]x[n]

[0057] Where w is the filter coefficient, μ is the step size, e is the error, and x is the input signal;

[0058] D. Noise reduction, wavelet transform, for multi-scale analysis and noise reduction of signals:

[0059]

[0060] Among them, ψ a，b is the wavelet function, a is the scale parameter, and b is the translation parameter;

[0061] E. Signal reconstruction, sparse coding, using sparse representation to reconstruct the signal:

[0062]

[0063] Among them, y is the observed signal, A is the dictionary matrix, x is the sparse representation, and λ is the regularization parameter;

[0064] F. Optimization algorithm, gradient descent method, is used to minimize the objective function:

[0065]

[0066] Among them, w is the parameter, a is the learning rate, is the gradient of the objective function with respect to w;

[0067] G. Model evaluation and selection, cross-validation, used to evaluate the generalization ability of the model, divide the data set into k subsets, use k-1 subsets to train the model in turn, and use the remaining subset for validation;

[0068] H. Parameter tuning, grid search, systematically traverse parameter combinations;

[0069] I. Real-time performance optimization, parallel processing, using multi-core processors or GPUs to accelerate computing, usually involves breaking down the algorithm into tasks that can be executed in parallel and using a parallel computing framework;

[0070] J. Hardware acceleration, dedicated hardware, using FPGA and AS IC dedicated hardware to accelerate specific signal processing tasks, by optimizing the pre-processed signals, it helps the epilepsy detection device to more accurately monitor and analyze the patient's physiological signals, thereby improving the accuracy and reliability of epileptic seizure detection. At the same time, these technologies help reduce false positives and false negatives, improve the user-friendliness of the device, and ensure the security and privacy of the data. In addition, real-time performance optimization and hardware acceleration can improve the response speed of the device and provide patients with timely warnings and interventions.

[0071] (III) Beneficial effects

[0072] The present invention provides an epilepsy detection device having the following beneficial effects:

[0073] 1. In the present invention, a more personalized model is trained based on the patient's medical history data, including the patient's epileptic seizure history, seizure type, frequency, duration, pre-seizure aura, post-seizure symptoms, genetic history, and family history of epilepsy or other related diseases, to provide patients with more accurate monitoring and early warning.

[0074] 2. In the present invention, through feature engineering and model optimization, the prediction accuracy of the model for epileptic seizures can be improved, and false positives and negatives can be reduced. The optimized model performs better on unseen data and has stronger generalization ability. The optimized model structure may be simpler, the prediction speed is faster, and it is suitable for real-time monitoring. Integrated learning and deep learning methods can improve the stability and reliability of the model on different data sets.

[0075] 3. In the present invention, by optimizing the pre-processed signals, the epilepsy detection device can more accurately monitor and analyze the patient's physiological signals, thereby improving the accuracy and reliability of epileptic seizure detection. At the same time, these technologies help reduce false positives and false negatives, improve the user-friendliness of the device, and ensure the security and privacy of the data. In addition, real-time performance optimization and hardware acceleration can improve the response speed of the device and provide patients with timely warnings and interventions. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of the system of the present invention;

[0077] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] Embodiment 1:

[0080] like Figure 1-2 As shown, an embodiment of the present invention provides an epilepsy detection device, including a wearable device, characterized in that: the wearable device includes: a data acquisition module, a data processing unit, a communication module, a software algorithm and a system platform;

[0081] The data acquisition module includes an electromyography sensor to detect electrical activity in wrist muscles, which may be abnormal during an epileptic seizure;

[0082] Heart rate monitoring sensors that monitor heart rate variability to identify changes in the autonomic nervous system that may be associated with seizures;

[0083] an accelerometer and gyroscope to detect movement and changes in orientation of the wrist, which can accompany involuntary movements during a seizure;

[0084] Electrodermal activity sensors, which monitor changes in skin conductivity that may be associated with autonomic nervous system activity;

[0085] The data processing unit includes a microprocessor for processing data from the sensor in real time and performing algorithm analysis;

[0086] Memory, used to store the collected data for subsequent analysis or retrospective review;

[0087] The communication module includes a Bluetooth / Wi-Fi module for wirelessly transmitting data to a smartphone, tablet or cloud server;

[0088] Mobile applications / cloud platforms for real-time monitoring, analysis and storage of data;

[0089] Software algorithms include signal processing algorithms to extract useful information from sensor data and identify characteristics of epileptic seizures;

[0090] Optimize the algorithm, optimize the algorithm of the wearable epilepsy detection device to improve the accuracy and reliability of detection. The detailed steps of the optimization algorithm are as follows:

[0091] S1. Medical and genetic history collection, through face-to-face interviews, direct conversations with patients and their families, and review of patients' medical records, previous diagnosis reports and treatment records, etc.;

[0092] S2. Wearing and data collection of wearable devices. Patients wear wearable devices under the guidance of doctors, and the devices continuously monitor the physiological signals of patients.

[0093] S3. Data preprocessing, data cleaning, noise and outliers removal, data quality assurance, feature extraction, extracting useful heart rate variability, electromyographic activity pattern features from raw data, principal component analysis:

[0094] X new =X·W

[0095] Among them, X is the original data matrix, W is the principal component matrix, X new This is the data after Jiang Wei;

[0096] S4. Model optimization, select appropriate machine learning models, support vector machines, random forests and neural networks, hyperparameter tuning, use grid search, random search or Bayesian optimization methods to tune hyperparameters, use training data sets to train models, and use validation sets to evaluate model performance. The support vector machine optimization objective function is:

[0097]

[0098] subject toys i (w·x i +b)≥1, for alli=1,…,n

[0099] Among them, w is the weight vector, b is the bias term, and x i is the eigenvector, y i is the corresponding label;

[0100] S5. Ensemble learning, training multiple models, using different algorithms or different data subsets to train multiple models, ensemble strategies, using voting, weighted average or stacking strategies to integrate the prediction results of multiple models, random forest, integrating the average prediction results of multiple decision trees:

[0101]

[0102] in, is the prediction function, f b (x) is the prediction result of the bth decision tree, and B is the total number of trees;

[0103] S6. Deep learning, design a neural network structure suitable for the problem, train the neural network using the training data set, and verify it using the validation set, apply regularization techniques and optimization algorithms to improve model performance, and neural network forward propagation:

[0104] a[ι]=g(z[ι])

[0105] Where a[ι] is the activation value of the ιth layer, z[ι] is the weighted input, and g is the activation function;

[0106] S7. Model evaluation and iteration: Use the test set to evaluate model performance, pay attention to accuracy, recall and F1 score indicators, adjust the model structure or parameters according to the evaluation results, and perform iterative optimization;

[0107] S8. Warning and feedback. When signs of an epileptic seizure are detected, the system issues a warning to the patient or caregiver, and collects feedback from the warning system for further optimization of the model and algorithm. Through feature engineering and model optimization, the model's prediction accuracy for epileptic seizures can be improved, and false positives and negatives can be reduced. The optimized model performs better on unseen data and has stronger generalization capabilities. The optimized model structure may be simpler, with faster prediction speed, suitable for real-time monitoring. Integrated learning and deep learning methods can improve the stability and reliability of the model on different data sets.

[0108] According to step S1., the medical history collection also includes: basic information, patient name, gender, age, contact information, home address, work environment and living habits;

[0109] Chief complaint, the main symptoms and problems described by the patient or family;

[0110] History of present illness, time of onset, duration, frequency of attacks, specific manifestations during attacks, factors that aggravate or relieve symptoms, stress, sleep and diet;

[0111] Past medical history, including diagnosed diseases, surgical history, hospitalization history, and past treatment experience, including drug treatment, surgical treatment, and other treatment methods;

[0112] Personal history, lifestyle habits, such as diet, exercise, and sleeping habits, social environment, such as work pressure, interpersonal relationships, and family status;

[0113] History of allergies, allergies to medications, foods, or other substances;

[0114] Review of systems, a comprehensive review of all body systems to see if there are other concerning symptoms;

[0115] Family history: Ask about the health status of the patient's family members, especially immediate family members, to find out if there are similar symptoms or confirmed diseases in the family;

[0116] History of genetic diseases: ask if there is a history of genetic diseases and find out if there are cases of early-onset or genetic diseases in the family;

[0117] The health status of family members, including age, health status and cause of death, is collected and combined with the patient's medical history data, including the patient's epileptic seizure history, seizure type, frequency, duration, pre-seizure signs, post-seizure symptoms, genetic history, and family history of epilepsy or other related diseases, to train a more personalized model to provide patients with more accurate monitoring and early warning.

[0118] According to step S3., the data preprocessing further includes the following steps:

[0119] A. Denoising, using filters to remove noise from the signal;

[0120] Low pass filter:

[0121] High Pass Filter:

[0122] Bandpass filter, a combination of low-pass and high-pass filters;

[0123] Median filter, for discrete signal x[n], median filter output y[n] = median{x[nk], ..., x[n], ..., x[n = k]};

[0124] B. Feature extraction, Fourier transform, convert the signal from time domain to frequency domain:

[0125]

[0126] Fast Fourier transform, quickly calculate the Fourier transform of a discrete signal:

[0127]

[0128] C. Signal enhancement, adaptive filter, minimum mean square error algorithm:

[0129] w n+1 =w n +2μe[n]x[n]

[0130] Where w is the filter coefficient, μ is the step size, e is the error, and x is the input signal;

[0131] D. Noise reduction, wavelet transform, for multi-scale analysis and noise reduction of signals:

[0132]

[0133] Among them, ψ a，b is the wavelet function, a is the scale parameter, and b is the translation parameter;

[0134] E. Signal reconstruction, sparse coding, using sparse representation to reconstruct the signal:

[0135]

[0136] Among them, y is the observed signal, A is the dictionary matrix, x is the sparse representation, and λ is the regularization parameter;

[0137] F. Optimization algorithm, gradient descent method, is used to minimize the objective function:

[0138]

[0139] Among them, w is the parameter, a is the learning rate, is the gradient of the objective function with respect to w;

[0140] G. Model evaluation and selection, cross-validation, used to evaluate the generalization ability of the model, divide the data set into k subsets, use k-1 subsets to train the model in turn, and use the remaining subset for validation;

[0141] H. Parameter tuning, grid search, systematically traverse parameter combinations;

[0142] I. Real-time performance optimization, parallel processing, using multi-core processors or GPUs to accelerate computing, usually involves breaking down the algorithm into tasks that can be executed in parallel and using a parallel computing framework;

[0143] J. Hardware acceleration, dedicated hardware, using FPGA and AS IC dedicated hardware to accelerate specific signal processing tasks, by optimizing the pre-processed signals, it helps the epilepsy detection device to more accurately monitor and analyze the patient's physiological signals, thereby improving the accuracy and reliability of epileptic seizure detection. At the same time, these technologies help reduce false positives and false negatives, improve the user-friendliness of the device, and ensure the security and privacy of the data. In addition, real-time performance optimization and hardware acceleration can improve the response speed of the device and provide patients with timely warnings and interventions.

[0144] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An epilepsy detection device, comprising a wearable device, characterized in that: The wearable device includes: a data acquisition module, a data processing unit, a communication module, a software algorithm and a system platform; The data acquisition module includes an electromyography sensor, a heart rate monitoring sensor, an accelerometer, a gyroscope and a skin electrical activity sensor; The data processing unit includes a microprocessor and a memory; The communication module includes a Bluetooth / Wi-Fi module and a mobile application / cloud platform; The software algorithm includes a signal processing algorithm and an optimization algorithm, which optimizes the algorithm of the wearable epilepsy detection device to improve the accuracy and reliability of detection. The detailed steps of the optimization algorithm are as follows: S1. Medical and genetic history collection, through face-to-face interviews, direct conversations with patients and their families, and review of patients' medical records, previous diagnosis reports and treatment records, etc.; S2. Wearing and data collection of wearable devices. Patients wear wearable devices under the guidance of doctors, and the devices continuously monitor the physiological signals of patients. S3. Data preprocessing, data cleaning, noise and outliers removal, data quality assurance, feature extraction, extracting useful heart rate variability, electromyographic activity pattern features from raw data, principal component analysis: X new =X·W Among them, X is the original data matrix, W is the principal component matrix, X new This is the data after Jiang Wei; S4. Model optimization, select appropriate machine learning models, support vector machines, random forests and neural networks, hyperparameter tuning, use grid search, random search or Bayesian optimization methods to tune hyperparameters, use training data sets to train models, and use validation sets to evaluate model performance. The support vector machine optimization objective function is: subject to y i (w·x i +b)≥1,for all i=1,…,n Among them, w is the weight vector, b is the bias term, and x i is the eigenvector, y i is the corresponding label; S5. Ensemble learning, training multiple models, using different algorithms or different data subsets to train multiple models, ensemble strategies, using voting, weighted average or stacking strategies to integrate the prediction results of multiple models, random forest, integrating the average prediction results of multiple decision trees: in, is the prediction function, f b (x) is the prediction result of the bth decision tree, and B is the total number of trees; S6. Deep learning, design a neural network structure suitable for the problem, train the neural network using the training data set, and verify it using the validation set, apply regularization techniques and optimization algorithms to improve model performance, and neural network forward propagation: a [ι] =g(z [ι] ) Among them, a [ι] is the activation value of layer l, z [ι] is the weighted input, g is the activation function; S7. Model evaluation and iteration: Use the test set to evaluate model performance, pay attention to accuracy, recall and F1 score indicators, adjust the model structure or parameters according to the evaluation results, and perform iterative optimization; S8. Early warning and feedback: When signs of an epileptic seizure are detected, the system issues an early warning to the patient or caregiver, and collects feedback from the early warning system to further optimize the model and algorithm.

2. An epilepsy detection device according to claim 1, characterized in that: The medical history collection according to step S1. also includes: basic information, patient name, gender, age, contact information, home address, work environment and living habits; Chief complaint, the main symptoms and problems described by the patient or family; History of present illness, time of onset, duration, frequency of attacks, specific manifestations during attacks, factors that aggravate or relieve symptoms, stress, sleep and diet; Past medical history, including diagnosed diseases, surgical history, hospitalization history, and past treatment experience, including drug treatment, surgical treatment, and other treatment methods; Personal history, lifestyle habits, such as diet, exercise, and sleeping habits, social environment, such as work pressure, interpersonal relationships, and family status; History of allergies, allergies to medications, foods, or other substances; Review of systems, a comprehensive review of all body systems to see if there are other concerning symptoms; Family history: Ask about the health status of the patient's family members, especially immediate family members, to find out if there are similar symptoms or confirmed diseases in the family; History of genetic diseases: ask if there is a history of genetic diseases and find out if there are cases of early-onset or genetic diseases in the family; Health status of family members, collect health information of family members, including age, health status and cause of death.

3. An epilepsy detection device according to claim 1, characterized in that: The data preprocessing according to step S3. further includes the following steps: A. Denoising, using filters to remove noise from the signal; Low pass filter: High Pass Filter: Bandpass filter, a combination of low-pass and high-pass filters; Median filter, for discrete signal x[n], median filter output y[n] = median{x[nk], ..., x[n], ..., x[n = k]}; B. Feature extraction, Fourier transform, convert the signal from time domain to frequency domain: Fast Fourier transform, quickly calculate the Fourier transform of a discrete signal: C. Signal enhancement, adaptive filter, minimum mean square error algorithm: w n+1 =w n +2μe[n]x[n] Where w is the filter coefficient, μ is the step size, e is the error, and x is the input signal; D. Noise reduction, wavelet transform, for multi-scale analysis and noise reduction of signals: Among them, ψ a，b is the wavelet function, a is the scale parameter, and b is the translation parameter; E. Signal reconstruction, sparse coding, using sparse representation to reconstruct the signal: Among them, y is the observed signal, A is the dictionary matrix, x is the sparse representation, and λ is the regularization parameter; F. Optimization algorithm, gradient descent method, is used to minimize the objective function: Among them, w is the parameter, a is the learning rate, is the gradient of the objective function with respect to w; G. Model evaluation and selection, cross-validation, used to evaluate the generalization ability of the model, divide the data set into k subsets, use k-1 subsets to train the model in turn, and use the remaining subset for validation; H. Parameter tuning, grid search, systematically traverse parameter combinations; I. Real-time performance optimization, parallel processing, using multi-core processors or GPUs to accelerate computing, usually involves breaking down the algorithm into tasks that can be executed in parallel and using a parallel computing framework; J. Hardware acceleration, dedicated hardware, use FPGA and ASIC dedicated hardware to accelerate specific signal processing tasks.

Images