A New Method for Epileptic Seizure Classification in EEG Using Adapted Wavelet Packets

TL;DR

This paper introduces a novel EEG seizure classification method using adapted wavelet packets and SVM, which improves accuracy over previous algorithms by tailoring wavelet bases to better localize seizure features.

Contribution

The study presents an innovative approach that adapts wavelet packet bases for enhanced seizure detection in EEG signals, outperforming existing methods.

Findings

Outperforms previous seizure classification algorithms

Effective in distinguishing seizure and non-seizure EEG segments

Utilizes adapted wavelet packets for improved feature localization

Abstract

Electroencephalography (EEG), as the most common tool for epileptic seizure classification, contains useful information about different physiological states of the brain. Seizure related features in EEG signals can be better identified when localized in time frequency basis projections. In this work, a novel method for epileptic seizure classification based on wavelet packets (WPs) is presented in which both mother wavelet function and WP bases are adapted a posteriori to improve the seizure classification. A support vector machine (SVM) as classifier is used for seizure versus non-seizure EEG segment classification. In order to evaluate the proposed algorithm, a publicly available dataset containing different groups patient with epilepsy and healthy individuals are used. The obtained results indicate that the proposed method outperforms some previously proposed algorithms in epileptic seizure classification.