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Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods by Chahira Mahjoub; Régine Le Bouquin Jeannès; Tarek Lajnef; Abdennaceur Kachouri is a Medicine article available to read on EtoBox.

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## Abstract Electroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms

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Author

Chahira Mahjoub; Régine Le Bouquin Jeannès; Tarek Lajnef; Abdennaceur Kachouri

Publisher

Walter de Gruyter GmbH

Published

2019

Language

EN

Field

Medicine (Health Sciences)