A review of feature extraction for EEG epileptic seizure detection and classification

Epileptic seizure is one of the most common neurological diseases around the world. It is clinical symptoms and/or signs due to abnormal excessive or synchronous neuronal activity in the human brain. Electroencephalogram (EEG) that measures the electrical activity of the brain generated by the cereb...

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Bibliographic Details
Published in2017 40th International Conference on Telecommunications and Signal Processing (TSP) pp. 456 - 460
Main Authors Boubchir, Larbi, Daachi, Boubaker, Pangracious, Vinod
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
classification
Electroencephalogram (EEG)
Electroencephalography
Entropy
Epileptic seizure detection
Feature extraction
Support vector machines
Time-domain analysis
Time-frequency analysis
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DOI10.1109/TSP.2017.8076027

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Summary:Epileptic seizure is one of the most common neurological diseases around the world. It is clinical symptoms and/or signs due to abnormal excessive or synchronous neuronal activity in the human brain. Electroencephalogram (EEG) that measures the electrical activity of the brain generated by the cerebral cortex nerve cells, is the most utilized test to detect the seizure activities by visual scanning of EEG signal recordings. Many techniques and methods have been proposed and developed to help the neurophysiologists to automatically detect the seizure activities with high accuracy. This paper presents a review of EEG features that have been proposed to characterize the epileptic seizure activities for the purpose of EEG seizure detection and classification. The relevant and discriminate features are analyzed, and their performance are also compared and discussed.
DOI:10.1109/TSP.2017.8076027