ENERGY AND ENTROPY-BASED FEATURE EXTRACTION FOR EPILEPTIC SEIZURE DETECTION USING EEG SIGNALS

Published 31 January 2021 •  vol 146  • 


Authors:

 

Slim Mechmeche, National Engineering School of Tunis, University of El Manar, UR SITI, 2002, Tunis, Tunisia
Ridha Ben Salah, College of Applied Medical Sciences, Prince Sattam Bin abdulaziz University, Biomedical Technology Department, Riyadh, Saudi Arabia
Zied Lachiri, National Engineering School of Tunis, University of El Manar, UR SITI, 2002, Tunis, Tunisia

Abstract:

 

In the learning machine process, classification performance of the EEG signals describing the epileptic diseases depends largely on the feature extraction process. The main objective of this research is to improve the classification accuracy for detecting epileptic seizure events in the EEG time series signals using supervised learning approach. Four features have been extracted from all EEG samples of Bonn University database: energy-entropy, energy-entropy and standard deviation of level 3 sub-bands and energy-entropy of level 5 sub-bands. Four classifiers have been evaluated using the chosen features: SVM-RBF, SVM-LKF, SVM-QKF and KNN. The empirical analysis showed that the classification accuracies of SVMs and KNN classifiers reached higher values when comparing with the previous studies for various classifiers using the same database for the three diagnosis categories, “healthy vs seizure”, “non-sezure vs seizure” and “Interictal vs Ictal”.
The features used in this paper could be used in the Diagnosis Decision Support Systems.

Keywords:

 

EEG, Epilepsy, Classification Accuracy, Cross-Validation, KNN, Supervised Classification, SVMS

References:

 

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Citations:

 

APA:
Mechmeche, S., Salah, R. B., & Lachiri, Z., (2021). Energy and Entropy-Based Feature Extraction for Epileptic Seizure Detection using EEG Signals. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 146, 29-42. doi: 10.33832/ijast.2021.146.04.

MLA:
Mechmeche, Slim, et al. “Energy and Entropy-Based Feature Extraction for Epileptic Seizure Detection using EEG Signals.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 146, 2021, pp. 29-42. IJAST, http://article.nadiapub.com/IJAST/Vol146/4.html.

IEEE:
[1] S. Mechmeche, R. B. Salah, and Z. Lachiri, "Energy and Entropy-Based Feature Extraction for Epileptic Seizure Detection using EEG Signals." International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 146, pp. 29-42, January 2021.