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A review of feature extraction for EEG epileptic seizure detection and classification - 2017

A Review Of Feature Extraction For Eeg Epileptic Seizure Detection And Classification

Survey Paper on Feature Extraction For Eeg Epileptic Seizure Detection And Classification

Research Area:  Machine Learning


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.

Feature Extraction
Epileptic Seizure Detection
Machine Learning
Deep Learning

Author(s) Name:  Larbi Boubchir; Boubaker Daachi; Vinod Pangracious

Journal name:  

Conferrence name:  40th International Conference on Telecommunications and Signal Processing (TSP)

Publisher name:  IEEE

DOI:  10.1109/TSP.2017.8076027

Volume Information: