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Received: 1 February 2020 Accepted: 5 May 2020 The unusual disturbance caused in the neuronal electrical activities inside the brain may lead to epileptic seizures. It is difficult to recognize normal epileptic seizures, as it requires extreme care to identify the epileptic seizure with precise and genuine manner. The existing work did not bridge the gap how accurate the eyeball movement signals can help the detection of epileptic seizure. In this paper, the Electrooculography (EOG) signals are processed to implement a method to predict epileptic seizure. The proposed method epileptic seizure detection using genetic based convolutional neural network (ESD-GCNN) helps in finding the epileptic seizure accurately from the eyeball movement signals. The Normalized Data Nonlinearity (NDN)-LMS is employed in the noise reduction process. During the saccade feature extraction and fixation feature extraction, the proposed method ensures the precision of epileptic seizure detection. The genetic-based CNN helps to make perfect decisions on the existence of epileptic seizure. The proposed research work and the entire research analysis is evaluated in MATLAB environment and the intention is to obtain optimal efficiency for veracity of epileptic seizure detection, compared to current research strategies.

A New Framework for Recognizing Normal and Epileptic Seizure from Eye Movement Signals Using Genetic Based Convolutional Neural Network