Automated Epilepsy Seizure Detection and Classification from EEG Signals: A Machine Learning Perspective
Keywords:
Epilepsy Detection, EEG Signals, Seizure Classification, Machine LearningAbstract
Epilepsy, a chronic neurological disorder characterized by recurrent seizures, affects millions worldwide. Electroencephalography (EEG) is a primary diagnostic tool, but manual interpretation of vast EEG data is time-consuming, prone to subjectivity, and often insufficient for timely intervention. This article explores the application of machine learning (ML) and deep learning (DL) techniques for the automated detection and classification of epileptic seizures from EEG signals. We review various methodologies, including preprocessing, feature extraction, and the implementation of traditional ML algorithms (e.g., Support Vector Machines, Random Forests) and advanced deep learning architectures (e.g., Convolutional Neural Networks, Autoencoders). Findings from recent studies demonstrate the significant potential of these computational approaches to enhance diagnostic accuracy, reduce expert workload, and potentially enable real-time monitoring. While challenges related to data variability, model generalizability, and interpretability persist, the continued advancement in computational methods holds promise for revolutionizing epilepsy management and improving patient outcomes.
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