Review Paper: Machine Learning Approaches for Epileptic Seizure Detection in EEG Signals
Keywords:
Machine Learning, Epileptic SeizureAbstract
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.
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Copyright (c) 2021 Pankaj Saraswat, Dr. Sandeep Chahal (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an Open Access article distributed under the term's of the Creative Common Attribution 4.0 International License permitting all use, distribution, and reproduction in any medium, provided the work is properly cited.