Adaptive Hybrid Optimization-Based Deep Learning for Epileptic Seizure Prediction

The abnormal electrical brain activity causes Epilepsy, otherwise termed as a seizure. In other words, an electrical storm inside the head is called as Epilepsy. An effectual seizure prediction technique is required to decrease the lifetime risk of epilepsy patients. Recently, numerous research works have been devised to predict epileptic seizure (ES) based on Electroencephalography (EEG) signal analysis. In this paper, a novel epileptic seizure prediction (ESP) method, namely the proposed Adaptive Exponential Squirrel Atom Search Optimization (Adaptive Exp-SASO)-based Deep Residual Neural Network (DRNN) is introduced. Here, the Gaussian filters removes the artifacts that exist in the EEG signal. In order to increase the detection performance, the statistical and spectral features are excavated. In addition, the significant features suitable for prediction are chosen by the Fuzzy Information Gain (FIG). Furthermore, the ES is predicted by the DRNN, wherein the proposed adaptive Exp-SASO approach tunes the weight of DRNN. Besides, the experimental result revealed that proposed adaptive Exp-SASO method provides the accuracy of 97.87%, sensitivity of 97.85%, and specificity of 98.88%.