Open AccessAutomated Epileptic Seizures Detection and Classification
Author(s) -
S Harshavarthini,
M P Aswathy,
P Harshini,
G. Priyanka
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit1951136
Subject(s) - electroencephalography , epilepsy , epileptic seizure , pattern recognition (psychology) , artificial intelligence , computer science , feature extraction , neuroscience , psychology
Detection of epileptic seizure activities from multi-channel electroencephalogram (EEG) signals plays a giant position inside the timely treatment of the sufferers with epilepsy. Visual identification of epileptic seizure in long-time period EEG is bulky and tedious for neurologists, which may additionally cause human errors. An automated device for accurate detection of seizures in a protracted-time period multi-channel EEG is crucial for the scientific prognosis. The features selection is based on discrete wavelet transformation (DWT).and feature extraction based GLCM. In the last stage, Probabilistic Neural Network is employed to classify the Normal and epileptic EEG signals.