Detection of Epilepsy patients using coot optimization based feed forward multilayer neural network

Abstract

ABSTRACTA familiar nervous system disorder characterised by seizures is called as Epilepsy. It is indeed hard to control the suitable type as an outcome of insufficient EEG information. In order to overcome these issues, a Multilayer Neural Network (MLNN)-based classifier is proposed to recognise if the patients are affected by epileptic disease or not. EEG signal is a contribution, and the input signal is preprocessed using antialiasing filter, finite impulse response, and band pass filter to eradicate unwanted noise present in the signal. After preprocessing, the features extracting process is done, and four extraction techniques are proposed in order to calculate the feature coefficient. The feature extraction outcome is fed into the MLNN classifier to predict the disease. MLNN performs with Coot-Optimization to reduce error and increase prediction accuracy. The future ideal applied in Matlab-software carried out numerous act metrics, and these parameters attained better performance such as accuracy of 96.5%, error of 0.03, precision of 98%, specificity is 97%, sensitivity is 95%, and so on. This displays the effectiveness of the future ideal than existing approaches such as ANN, SVM, KNN and NB. Based on this proposed classification, the epileptic disease prediction can be improved on this technique and can provide a living standard for patients.KEYWORDS: Epilepsy diseaseeeg signalmulti-layer neural networkantialiasing filterfinite impulse response Author contributionsThe corresponding author claims the major contribution of the paper including formulation, analysis and editing. The co-author provide guidance to verify the analysis result and manuscript editing.Compliance with ethical standardsThis article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the journal’s editorial board decides not to accept it for publication.Disclosure statementNo potential conflict of interest was reported by the author(s).FundingThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.