<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Epilepsy is a neurological disorder characterized by recurrent seizures and has a high incidence rate. The aim of this research is to classify EEG signals as either focal and non-focal in order to identify the epileptogenic area of the brain, which can be surgically treated to manage epilepsy. In this paper, was proposed a classification method based on higher order spectra (HOS) parameters and four different classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-Nearest neighbors (KNN), and Mahalanobis distance (MD). The method was evaluated using a public dataset that consists in EEG recordings from epileptic patients. The classifiers performances were evaluated and it was shown that KNN classifier achieves a maximum classification rate of 99.55%, sensitivity of 100%, and specificity of 99.09%. The data classification was performed with maximum values of 0.96 for F1-score, and 0.91 for both Kappa and Matthews Coefficient. The results demonstrate the efficiency of the proposed method to identify the type of EEG signals.</p>
</abstract>

Epilepsy is a neurological disorder characterized by recurrent seizures and has a high incidence rate. The aim of this research is to classify EEG signals as either focal and non-focal in order to identify the epileptogenic area of the brain, which can be surgically treated to manage epilepsy. In this paper, was proposed a classification method based on higher order spectra (HOS) parameters and four different classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-Nearest neighbors (KNN), and Mahalanobis distance (MD). The method was evaluated using a public dataset that consists in EEG recordings from epileptic patients. The classifiers performances were evaluated and it was shown that KNN classifier achieves a maximum classification rate of 99.55%, sensitivity of 100%, and specificity of 99.09%. The data classification was performed with maximum values of 0.96 for F1-score, and 0.91 for both Kappa and Matthews Coefficient. The results demonstrate the efficiency of the proposed method to identify the type of EEG signals.

Discrimination of Focal and Non-Focal Epileptic Eeg Signals Using Different Types of Classifiers

madalina-giorgiana.murariu@student.tuiasi.ro

“Gheorghe Asachi” Technical University of Iaşi

Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Epilepsy is a neurological disorder characterized by recurrent seizures and has a high incidence rate. The aim of this research is to classify EEG signals as...

Discrimination of Focal and Non-Focal Epileptic Eeg Signals Using Different Types of Classifiers

Publié en ligne: 08 mai 2023

Pages: 61 - 79

Reçu: 27 juin 2022

Accepté: 13 févr. 2023

DOI: https://doi.org/10.2478/bipie-2022-0011

Mots clés
epilepsy, focal EEG, non-focal EEG, classification method, signal

© 2022 Mădălina-Giorgiana Murariu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Discrimination of Focal and Non-Focal Epileptic Eeg Signals Using Different Types of Classifiers

Publié en ligne: 08 mai 2023

Pages: 61 - 79

Reçu: 27 juin 2022

Accepté: 13 févr. 2023

DOI: https://doi.org/10.2478/bipie-2022-0011

Mots clésepilepsy, focal EEG, non-focal EEG, classification method, signal

© 2022 Mădălina-Giorgiana Murariu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
epilepsy, focal EEG, non-focal EEG, classification method, signal