Analysis of Stress-Related Seizure and Machine Learning Classification of Epilepsy

Abstract

This research paper delves into the cognitive aspects of stress-related seizures and explores the efficacy of machine learning algorithms in classifying epilepsy. Stress I s a well-established trigger for seizures in individuals with epilepsy, yet the underlying cognitive mechanisms remain incompletely understood. Through a comprehensive review of existing literature, this paper examines the cognitive processes implicated in stress-related seizures, including attention, memory, and emotion regulation. Furthermore, it investigates the potential of machine learning techniques, such as support vector machines and deep learning models, in accurately identifying epilepsy based on neuroimaging data, electroencephalogram (EEG) recordings, and clinical variables. By integrating cognitive science with computational approaches, this study aims to advance our understanding of stress-related seizures and enhance diagnostic strategies for epilepsy management.