Abstract
Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.
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Available on UCI machine learning repository https://archive.ics.uci.edu/ml.
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Authors thank to the SDAS Research Group (www.sdas-group.com) for its valuable support.
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Vega-Gualán, E., Vargas, A., Becerra, M., Umaquinga, A., Riascos, J.A., Peluffo, D. (2019). Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_19
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