Authors: Research Scholar Praveen A. Andhale, Professor Dr. Varsha H. Patil
Abstract: Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal electri-cal activity in the brain. Predicting seizures before their onset can sig-nificantly improve patient safety and enable timely medical intervention. Recent advancements in wearable biosensors and artificial intelligence have enabled continuous monitoring of physiological signals in real-world environments. This paper presents a survey of epileptic seizure prediction approaches based on physiological signals including electroencephalography (EEG), electrocardiography (ECG), pho-toplethysmography (PPG), and electromyography (EMG). The study summarizes commonly used public datasets such as CHB-MIT, Bonn EEG, and the Temple University EEG corpus that are widely used to develop and evaluate prediction algorithms. Furthermore, traditional machine learning approaches as well as recent deep learning architectures for biomedical signal analysis are reviewed. The review also highlights recent progress in wearable monitoring systems and multimodal signal fusion strategies. Finally, major research chal-lenges including limited datasets, signal noise, patient variability, and large number of false alerts are analyzed, and future research directions such as multimodal sensing, personalized prediction models, wearable AI systems, and explainable artificial intelligence are outlined.
