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Journal articles

Regularity and Matching Pursuit Feature Extraction for the Detection of Epileptic Seizures

Abstract : Background The neurological disorder known as epilepsy is characterized by involuntary recurrent seizures that diminish a patient's quality of life. Automatic seizure detection can help improve a patient's interaction with her/his environment, and while many approaches have been proposed the problem is still not trivially solved. Methods In this work, we present a novel methodology for feature extraction on EEG signals that allows us to perform a highly accurate classification of epileptic states. Specifically, Hölderian regularity and the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings. Results Several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. Comparison with existing methods: A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance. Conclusions The experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis.
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Contributor : Pierrick Legrand Connect in order to contact the contributor
Submitted on : Thursday, October 27, 2016 - 8:14:59 PM
Last modification on : Wednesday, February 2, 2022 - 3:54:37 PM




Emigdio Z-Flores, Leonardo Trujillo, Arturo Sotelo, Pierrick Legrand, Luis Coria. Regularity and Matching Pursuit Feature Extraction for the Detection of Epileptic Seizures. Journal of Neuroscience Methods, Elsevier, 2016, 266, pp.107-125. ⟨10.1016/j.jneumeth.2016.03.024⟩. ⟨hal-01389051⟩



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