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Epilepsy prediction system based on feature channel fusion and deep learning

A predictive system and deep learning technology, applied in the field of cognitive neuroscience and information, can solve the problems of high dimensionality and high feature information aliasing, and achieve the goal of solving excessive dimensionality, reducing the impact of redundant information, and good classification results Effect

Pending Publication Date: 2022-06-03
NANJING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

It solves the problem of high dimensionality and high feature information aliasing in the previous epilepsy prediction.

Method used

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  • Epilepsy prediction system based on feature channel fusion and deep learning
  • Epilepsy prediction system based on feature channel fusion and deep learning
  • Epilepsy prediction system based on feature channel fusion and deep learning

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Embodiment Construction

[0074] In order to make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0075] The present invention proposes an epilepsy prediction system based on feature channel fusion and deep learning, and the system includes the following modules:

[0076] Data acquisition module: collect and obtain the electrical activity data recorded by the continuous EEG of epilepsy patients, screen the epilepsy data, and use all the filtered electrical activity data to form the original data set;

[0077] Preprocessing module: Preprocess the collected EEG signal original data set to remove the interference caused by equipment and environment to the signal during the EEG signal acquisition process. The preprocessing steps are filtering, channel screening, removing power frequency interference, and eliminating false shadow, re-reference...

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Abstract

The invention discloses an epilepsy prediction system based on feature channel fusion and deep learning, and the system employs a T-distribution random neighbor embedding algorithm (t-SNE) of a nonlinear dimension reduction algorithm to carry out the fusion of feature channel information of epilepsy electroencephalogram signals. Time domain and frequency domain information obtained through short-time Fourier transform calculation serves as features to be input into the deep residual contraction neural network, and epilepsy seizure is predicted by recognizing the epilepsy seizure interval and the epilepsy seizure early stage. According to the method, from the aspects of feature dimension improvement and classifier design, artificial feature extraction is not needed, the expression of feature information is improved, and a new method is provided for subsequently pushing epilepsy prediction to clinical application.

Description

technical field [0001] The invention belongs to the fields of cognitive neuroscience and information technology, and in particular relates to an epilepsy prediction system based on feature channel fusion and deep learning. Background technique [0002] Epilepsy is a chronic neurological disease characterized by sudden abnormal brain response and relapse, caused by abnormal activity of brain neurons. Seizures caused by this neuronal overdischarge are often accompanied by disturbances in motor, sensory, emotional, or mental function. Once this sudden neurological disorder strikes, the patient's brain stops working properly, resulting in abnormal responses such as fainting, body imbalances, convulsions, muscle contractions, loss of consciousness, and intuition. For any epilepsy patient, the seizure of epilepsy has a great impact on all aspects of life of the patient and his family, and even endangers the patient's life. Fear, misunderstanding, discrimination and social stigma...

Claims

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Application Information

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IPC IPC(8): A61B5/369A61B5/00G06K9/00G06N3/04G06N3/08
CPCA61B5/369A61B5/4094A61B5/7203A61B5/7225A61B5/7257A61B5/7267A61B5/725G06N3/08G06N3/045G06F2218/02G06F2218/04G06F2218/08G06F2218/12
Inventor 徐欣张尹纪卓捷吴建盛
Owner NANJING UNIV OF POSTS & TELECOMM
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