Research on Epileptic EEG Signal Based on Partial Mutual Information and Partial Measure of Conditional Mutual Information from the Mixed Embedding

Epilepsy is a common neurological disorder, and a practical approach to analyzing EEG signals in epilepsy is essential for the study of epilepsy. To explore the differences in the coupling relationship between epileptic Electroencephalo-gram(EEG) signals and healthy EEG signals, we analyze the coupling relationship using symbolic partial mutual information and partial measure of conditional mutual information from the mixed embedding(pmime) method. It is shown that the coupling degree of EEG signals in healthy individuals is significantly higher than that of epileptic EEG signals. Symbolized partial mutual information and the pmime method can effectively distinguish between healthy and epileptic EEG signals. This study may be helpful in the diagnosis and treatment of patients with epilepsy in clinical practice.

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Published
2024-10-26
DOI
https://doi.org/10.1109/cisp-bmei64163.2024.10906091
Primary Topic
Neural Networks and Applications
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article
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Research on Epileptic EEG Signal Based on Partial Mutual Information and Partial Measure of Conditional Mutual Information from the Mixed Embedding

Zhixun Xia, Yuman Luo, Chengbiao Lu, Shutao Guo et al.
Neural Networks and Applications
article

Research on Epileptic EEG Signal Based on Partial Mutual Information and Partial Measure of Conditional Mutual Information from the Mixed Embedding

Zhixun Xia, Yuman Luo, Chengbiao Lu, Shutao Guo, Jiafei Dai, Jun Wang
article en

Abstract

Epilepsy is a common neurological disorder, and a practical approach to analyzing EEG signals in epilepsy is essential for the study of epilepsy. To explore the differences in the coupling relationship between epileptic Electroencephalo-gram(EEG) signals and healthy EEG signals, we analyze the coupling relationship using symbolic partial mutual information and partial measure of conditional mutual information from the mixed embedding(pmime) method. It is shown that the coupling degree of EEG signals in healthy individuals is significantly higher than that of epileptic EEG signals. Symbolized partial mutual information and the pmime method can effectively distinguish between healthy and epileptic EEG signals. This study may be helpful in the diagnosis and treatment of patients with epilepsy in clinical practice.

Nanjing General Hospital of Nanjing Military Command (CN), Nanjing University of Posts and Telecommunications (CN), Nanjing University (CN)
Zero hunger
Openalex Percentile: Top 74%
Neural Networks and Applications
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Research on Epileptic EEG Signal Based on Partial Mutual Information and Partial Measure of Conditional Mutual Information from the Mixed Embedding — Zhixun Xia, Yuman Luo, et al. · (2024) | TGRS Research Map | TGRS