Epileptic seizure detection in EEG using improved entropy Online publication date: Fri, 14-Aug-2020
by Arumai Thangam Phareson Gini; Manuel Packiaselvam Flower Queen
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 33, No. 4, 2020
Abstract: Epilepsy is a chronic disorder of the brain that impacts people all around the world. It is tremendously challenging to investigate the chronicled EEG signal and the analysis of epileptic activity is a time consuming procedure. In this article, we suggest a novel ANN based epileptic seizure detection with the help of the improved entropy feature. The anticipated technique includes stages like, pre-processing, feature abstraction and seizure detection. In the primary phase, we sample all the input information set. In second phase, a fuzzy entropy algorithm is utilised to abstract the features of EEG signal. Finally, we utilise artificial neural network for to recognise epilepsy seizures in exaggerated patient. Lastly, we associated the anticipated technique with prevailing technique for the perceiving epileptic sections. The function is utilised to compute the following parameters like accuracy, specificity, FAR, sensitivity, FRR, GAR which established the effectiveness of the anticipated epilepsy seizure recognition system.
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