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Comparative Analysis of DWT-Based EEG Feature Extraction Using Machine Learning Models for Epileptic Seizure Detection

Author(s): Hindarto Hindarto1*,Ade Eviyanti2,Ahmad Ahfas3,Egha Arya Affandi4

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 14, Issue 2

Publisher : FOREX Publication

Published : 30 June 2026

e-ISSN : 2347-470X

Page(s) : 604-611

DOI: https://doi.org/10.37391/IJEER.140235

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.

Keywords: EEG, DWT, SVM, KNN, MLP, Kurtosis, Epileptic Seizure Detection.




Hindarto Hindarto, Informatics Universitas Muhammadiyah Sidoarjo, Indonesia; Email: hindarto@umsida.ac.id

Ade Eviyanti, Informatics Universitas Muhammadiyah Sidoarjo, Indonesia; Email: adeeviyanti@umsida.ac.id

Ahmad Ahfas,Electrical Engineering Universitas Muhammadiyah Sidoarjo, Indonesia; Email: ahfas_umsida@yahoo.com

Egha Arya Affandi,Informatics Universitas Muhammadiyah Sidoarjo, Indonesia; Email: eghaarya23@gmail.com

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Hindarto Hindarto, Ade Eviyanti, Ahmad Ahfas, and Egha Arya Affandi (2026), Comparative Analysis of DWT-Based EEG Feature Extraction Using Machine Learning Models for Epileptic Seizure Detection. IJEER 14(2), 604-611. DOI: 10.37391/IJEER.140235.