A Machine Learning Framework for Robust Epileptic Seizure Detection from EEG Sign

Abstract

Millions of individuals throughout the world suffer from epilepsy, a neurological illness characterized by frequent seizures. To improve patient safety and quality of life, seizure detection must be timely and reliable. With the possibility for realtime monitoring and intervention, machine learning has recently become a promising tool for automated seizure detection. This paper provides a thorough analysis of recent developments in machine learning-based epileptic seizure identification. We talk about the transition from using conventional feature engineering techniques to applying deep learning algorithms. Additionally, we consider the function of publicly accessible datasets and assess standard performance indicators. The difficulties and unsolved issues in the field, including inter-subject variability and realtime application, are also highlighted in this review. This paper synthesizes the literature and offers useful insights for researchers and practitioners striving to enhance the precision and effectiveness of epileptic seizure detection systems, thereby enhancing patient care and management.