Epileptic Seizure Prediction through ML And DL Models: A Survey

Epileptic seizures are unpredictable and pose significant risks to individuals affected by epilepsy. Electroencephalogram (EEG) signals offer a promising avenue for early seizure prediction, enabling timely intervention and improved quality of life for patients. This survey paper provides a comprehensive review of Deep learning (DL) and Machine learning (ML) learning techniques applied to EEG-based epileptic seizure prediction. It covers traditional ML models, advanced DL architectures, and optimization algorithms, highlighting their effectiveness and limitations. Additionally, the paper discusses feature extraction methods, signal processing techniques, and evaluation metrics used in this domain. Real-world applications, current challenges, and future research directions are explored to offer a holistic view of the field. By examining state-of-the-art methods, this survey aims to provide valuable insights into the development of robust, real-time seizure prediction systems, fostering advancements in clinical applications and patient care.