Review Paper: Machine Learning Approaches for Epileptic Seizure Detection in EEG Signals

Abstract

Epileptic seizures, a hallmark symptom of epilepsy, are neurological events that can have devastating effects on an individual’s quality of life. The timely detection of seizures is crucial for preventing injury and improving patient outcomes. Electroencephalography (EEG) signals are commonly used for diagnosing and monitoring epilepsy. However, the manual interpretation of EEG data is time-consuming and requires specialized expertise. Machine learning (ML) techniques have emerged as powerful tools to automate the detection of epileptic seizures from EEG signals, improving both the accuracy and efficiency of diagnosis. This review explores various machine learning approaches that have been applied to epileptic seizure detection, focusing on data preprocessing, feature extraction, and classification models. Additionally, the paper discusses the challenges, limitations, and future directions of this research field.