Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques

Tuncer E., BOLAT E.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, vol.42, no.2, pp.575-595, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1016/j.bbe.2022.04.004
  • Journal Name: BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, INSPEC
  • Page Numbers: pp.575-595
  • Keywords: Machine learning, Pattern recognition, Seizures, Focal, Generalized, Electroencephalogram, NEURAL-NETWORK, CLASSIFICATION, IDENTIFICATION, SELECTION, ENTROPY
  • Kocaeli University Affiliated: Yes

Abstract

Epileptic seizures result from disturbances in the electrical activity of the brain, classified as focal, generalized, or unknown. Failure to correctly classify epileptic seizures may result in inappropriate treatment and continuation of seizures. Therefore, automatic detection of generalized, focal, and other epileptic seizures from EEG signals is important. In this research article, Focal-Generalized classification method is proposed that compares tradi-tional classification algorithms and deep learning methods. Two different classifications: four-class (Case (I) Complex Partial Seizure (CPSZ) (C4-T4 Onset)-CPSZ (FP2-F8 Onset)-CPSZ (T5-O1 Onset)-Absence Seizure (ABSZ)) and two-class (Case (II) CPSZ-ABSZ) problems are considered. This study includes preprocessing of scalp Electroencephalogram (EEG) data, feature extraction with discrete wavelet method, feature selection using Correlation-based Feature Selection (CFS) method, and classification of data with classifier algorithms (K-Nearest Neighbors (Knn), Support Vector Machine (SVM), Random Forest (RF) and Long Short-Term Memory (LSTM). The proposed method was applied on 23 subjects in the Temple University Hospital (TUH) scalp EEG data set, anda classification success rate of 95,92% for case (I) and 98,08% for case (II) was successfully achieved with deep learning architecture LSTM.(c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.