Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals

Samed Jukic, Muzafer Saracevic, Abdulhamit Subasi and Jasmin Kevric
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Samed Jukic: Faculty of Engineering and Natural Sciences, International Burch University, Francuske Revolucije bb, Ilidza, 71000 Sarajevo, Bosnia and Herzegovina
Muzafer Saracevic: Department of Computer Sciences, University of Novi Pazar, Dimitrija Tucovića bb, 36300 Novi Pazar, Serbia
Abdulhamit Subasi: College of Engineering, Effat University, Jeddah 21478, Saudi Arabia
Jasmin Kevric: Faculty of Engineering and Natural Sciences, International Burch University, Francuske Revolucije bb, Ilidza, 71000 Sarajevo, Bosnia and Herzegovina

Mathematics, 2020, vol. 8, issue 9, 1-16

Abstract: This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.

Keywords: electroencephalogram (EEG); source localization; multi-scale principal component analysis; autoregressive (AR) method; ensemble machine learning methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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