Anticipating Epileptic Seizures in Advance through Explainable AI

Recurrent seizures that interfere with the quality of a patient's life make up epilepsy, a neurological disorder affecting millions worldwide. Clinical treatment and better patient outcome can be improved with accurate seizure prediction. Yet, current prediction methods are not always interpretable enough such that their use in healthcare is feasible. The focus of this work is to develop and evaluate the use of advanced artificial intelligence (AI) models for seizure prediction, paying particular attention to explainable artificial intelligence (XAI) techniques for enhancing model transparency and trustworthiness. The research attempts to develop very accurate and interpretable predictive models. The various multimodal data sources include EEG recordings and patient demographics. RNNs are used for the prediction of EEG signals, taking into account not only the temporal dependencies of the signals but also improving the predictive performance. First, the datasets will be used in AI models comprising traditional machine learning and XAI methods that are trained on them and rigorously tested through systematic testing as well as prospective clinical trials to assure reliability, practicality, and efficacy in realworld settings. The finding of this study demonstrates how AIpowered seizure prediction, especially with the use of RNNs, can revolutionize customized epilepsy treatment. With an accuracy of 97%, it represents a strong advance in the management of epilepsy: timely and personalized interventions have a high impact on the patient's outcomes and the quality of life of the patients. The techniques like LIME and SHAP help to establish trust with physicians and patients alike, as these models provide information about their underlying decision-making process.