Extensive Review of Epileptic Seizure Detection Techniques: Performance, Achievements, and Future Directions

Epilepsy is a neurological illness that affects the brain on a chronic and crucial basis. It is characterized by recurring seizures. These seizures involve phases of synchronous, abnormal neural activity, lasting from a few seconds to several minutes. Epileptic seizures manifest as transient episodes of involuntary body movements, often accompanied by a loss of consciousness. Although individual patients experience seizures infrequently, the impacts on physical health, social interactions, and emotional well-being are substantial, necessitating careful diagnosis and treatment.This review provides a thorough examination of the many machine learning methods employed for epilepsy detection, drawing insights from numerous research studies. It evaluates the features considered in each study and offers a thorough comparison of their performance metrics. Additionally, the survey investigates the highest performance levels achieved, the datasets utilized, and the simulation tools applied in the research. The review concludes by identifying existing research gaps and challenges, providing valuable insights for future advancements within the domain of epileptic seizure detection.