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

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ID: 274004
2020
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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.
Reference Key
jukic2020mathematicscomparison Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Samed Jukic;Muzafer Saracevic;Abdulhamit Subasi;Jasmin Kevric;Jukic, Samed;Saracevic, Muzafer;Subasi, Abdulhamit;Kevric, Jasmin;
Journal Mathematics
Year 2020
DOI
10.3390/math8091481
URL
https://www.mdpi.com/2227-7390/8/9/1481/htm
Keywords
electroencephalogram (eeg) source localization multi-scale principal component analysis autoregressive (ar) method ensemble machine learning methods

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