High-performance automated detection methods for epilepsy play a crucial role in clinical diagnostic support. To address the challenge of effectively extracting features from epileptic EEG signals, characterized by strong spontaneity and complexity, a novel feature extraction approach based on Window Kullback-Leibler Divergence (WKLD) is proposed, coupled with discrete wavelet analysis for EEG signal feature extraction. Then, a Residual Multidimensional Taylor Network (ResMTN) classifier is applied for epilepsy state classification. Experimental results demonstrate an accuracy of 98% in classifying EEG signals during seizure and interictal periods, with both specificity and sensitivity reaching 98.18%, outperforming existing widely-used feature extraction and classification methods.
