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Epileptic seizures identification with autoregressive model and firefly optimization based classification

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Abstract

Identifying epilepsy cases and epileptic seizures from electroencephalogram (EEG) signals is a challenging issue, which usually needs high level of skilled neurophysiologists. Numerous works have attempted to develop tools that can provide an assistant to neurophysiologist in analyzing the EEG for epileptic seizures detection. This paper proposes a new automatic framework to identify and classify the epileptic seizure from EEG using a machine learning method. In particular, the feature extraction process of the proposed scheme utilizes autoregressive model (AR) and firefly optimization (FA) to procure an optimal model order (P). Namely, the main aim of FA is to find the best model order (P) with minimum residual variance using Akaike information criterion (AIC) as an objective function of FA algorithm. A support vector machine (SVM) classifier is employed for the classification of the epileptic seizures signals. The presented scheme is also effective for short segment of EEG signals owing to use of AR model in features extraction stage. Experiments with the publicly available Bonn database that is composed of healthy (nonepileptic), interictal and ictal EEG samples show promising results with high accuracy.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Mathematics and Informatics, Mohamed El Bachir El Ibrahimi University of Bordj Bou Arreridj, 34000, Bordj Bou Arreridj, Algeria

    Abdelouahab Attia

  2. Computer Science Department, Faculty of Sciences, Ferhat Abbas University Setif I, Setif, Algeria

    Abdelouahab Moussaoui

  3. Image Team GREYC, CNRS, UMR 6072, University of Caen, Caen, France

    Youssef Chahir

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  1. Abdelouahab Attia
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  2. Abdelouahab Moussaoui
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  3. Youssef Chahir
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Correspondence to Abdelouahab Attia.

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Attia, A., Moussaoui, A. & Chahir, Y. Epileptic seizures identification with autoregressive model and firefly optimization based classification. Evolving Systems 12, 827–836 (2021). https://doi.org/10.1007/s12530-019-09319-z

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  • DOI: https://doi.org/10.1007/s12530-019-09319-z

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