Comparative Analysis of DWT-Based EEG Feature Extraction Using Machine Learning Models for Epileptic Seizure Detection
TL;DR
Findings underscore the potential of higher-order statistical descriptors, particularly kurtosis, for EEG-based epileptic seizure detection under a controlled benchmark setting and underscore the potential of classical machine learning classifiers to distinguish normal and epileptic EEG signals.
Abstract
Epileptic seizure detection from EEG signals remains challenging due to their non-stationary and complex nature. This study presents a comparative analysis of Discrete Wavelet Transform (DWT)-based feature extraction combined with classical machine learning classifiers (SVM, KNN, and MLP) to distinguish normal and epileptic EEG signals. Using the publicly available Bonn University dataset (Sets A and E), EEG signals were decomposed using the Daubechies-4 (db4) wavelet into five decomposition levels corresponding to standard frequency bands (Delta, Theta, Alpha, Beta, Gamma). Seven statistical features—energy, mean amplitude, standard deviation, Shannon entropy, relative wavelet energy (RWE), kurtosis, and skewness—were extracted from each sub-band. A stratified 10-fold cross-validation with a leakage-controlled record-level partitioning strategy was implemented to reduce optimistic bias. Since subject-level identifiers are unavailable in the public Bonn dataset, the validation was designed to avoid re-splitting individual EEG records across training and testing stages. Results demonstrate that kurtosis-based features consistently achieve the highest accuracy (99.8% ± 0.3) across all classifiers, significantly outperforming other features (p < 0.01). These findings underscore the potential of higher-order statistical descriptors, particularly kurtosis, for EEG-based epileptic seizure detection under a controlled benchmark setting.