Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

Abstract

Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure. Noise aware Epileptic Seizure Detection using Thirteen Layer Convolution Neural Network (NESD-TLCNN) is adopted in this research to mitigate this issue and thereby ensuring the prediction rate more precisely. Furthermore, Hybrid Dynamic Time Wrapping based Hidden Markov Model (HDWT-HMM) is greatly utilized for primary background noise detection and removal by estimating the noise depending on distance metric. Once after the completion of noise estimation, perfect detection of epileptic seizure is accomplished using feature extraction. The peculiar features involved are saccade, fixation and blink features. Subsequently, Particle swarm optimization (PSO) technique is also involved in this research for optimal feature selection. Thirteen Layer Convolution Neural Network (TLCNN) is applied at last for learning and differentiation of epileptic seizure from the normal eyes. This research is being carried out in MATLAB platform which also reveals that the anticipated methodology produces improved outcomes when contrasted with the existing research work.