Research on Epilepsy Classification Model Based on Variational Mode Quadratic Decomposition
Authors: 
Chen Huang, Zhijun Fan, Kui Xiao, Yan Zhang, Shihui Wang, Jianhua Song, Wei Wu, Chao LiuAuthors Info & Claims
Chen Huang, Zhijun Fan, Kui Xiao, Yan Zhang, Shihui Wang, Jianhua Song, Wei Wu, Chao LiuAuthors Info & ClaimsPages 906 - 914
Abstract
Epilepsy, a widespread neurological disorder, creates substantial physical and psychological challenges for patients. Accurate seizure prediction allows for prompt symptom intervention and treatment guidance. To achieve this goal, we introduce a classification model for epilepsy using variational mode quadratic decomposition. It first processes EEG signals from epilepsy patients with variational mode decomposition. Then, a second variational mode decomposition filters and breaks down the residual signal further. After decomposition, signals are reconstructed into continuous wavelet transform feature images. A combination of convolutional neural network and temporal convolutional network then classifies epileptic seizure periods, including ictal, preictal, interictal, and non-ictal phases. The extensive experimental results show the model reaches 85% accuracy and 88.8% precision in classifying epileptic seizure, pre-ictal, inter-ictal, and non-ictal periods, evidencing the proposed method's effectiveness.
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- Research on Epilepsy Classification Model Based on Variational Mode Quadratic Decomposition
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Published In
May 2024
1379 pages
ISBN:9798400706196
DOI:10.1145/3652583
- General Chairs:
- Cathal Gurrin,
- Rachada Kongkachandra,
- Klaus Schoeffmann,
- Program Chairs:
- Duc-Tien Dang-Nguyen,
- Luca Rossetto,
- Shin'ichi Satoh,
- Liting Zhou
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Published: 07 June 2024
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