PHASE TRANSITION-BASED OPTIMIZED ADAPTING MODEL FOR REAL-TIME SEIZURE PREDICTION APPLICATION

Abstract

Epilepsy is a dynamic process that will undoubtedly continue throughout an individual’s lifetime. One part of the brain may get affected, other brain regions also can possibly get involved in this disorder. Such seizures are sometimes life threatening; therefore, a warning signal is required to provide information to their respective caretakers in order to ensure the safety of the epileptic patients. The following are the four states of seizure: interictal (between seizures), preictal (before seizure), ictal (seizure), and postictal (after seizures). The ability to accurately distinguish the unique preictal state from the other seizure states is essential for seizure prediction. The Seizures can only be predicted by early detection of the preictal condition, which helps to differentiate seizure prediction from seizure detection. Preictal signals, which typically appear minutes to hours before a seizure occurs, are predicted in order to prevent seizures. In this paper, the discussions on the seizure predictions and its procedures are done. An optimized adapting model based on the phase transitions for real-time seizure prediction is developed and implemented in this study. We proposed a classification model called as enhanced convolutional neural networks (ECNNs) that has been tuned for speeding convergence and reducing model complexity utilizing the Fletcher Reeves Algorithm (WO-FRA) based on Walrus Optimization. Furthermore, the Phase Transition Predictor (PTP) based on the Kullback–Leibler (KL) divergence yields a Premium Seizure Prediction Horizon (PSPH). The suggested model’s empirical results, which were verified using 1000 EEG recordings from the CHB-MIT, NINC, and SRM databases, outperform the current techniques with 98% accuracy, 0.07 h[Formula: see text] false prediction rate and with 99% sensitivity.