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.
Keywords:
Feature Extraction
Eeg
Epileptic Seizure Detection
Classification
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:
