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

• Autorzy: Tuncer Erdem , Bolat Emine Doğru

Identyfikatory

DOI

10.1016/j.bbe.2022.04.004

• Języki publikacji: EN

Abstrakty

EN

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 traditional 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, and a classification success rate of 95,92% for case (I) and 98,08% for case (II) was successfully achieved with deep learning architecture LSTM.

Słowa kluczowe

EN

machine learning; pattern recognition; epileptic seizure; electroencephalogram

PL

uczenie maszynowe; rozpoznawanie wzorców; napad padaczkowy; elektroencefalogram

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 2
• Strony: 575--595
• Opis fizyczny: Bibliogr. 52 poz., rys., tab., wykr.

Twórcy

autor

Tuncer Erdem

• Bahcecik Vocational and Technical Anatolian High School, Electrical and Electronics Department, Kocaeli, Turkey

autor

Bolat Emine Doğru

• Faculty of Technology, Biomedical Eng. Department of Kocaeli University, Kocaeli, Turkey

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