Abstract
Epileptic seizure detection is a crucial area of research aimed at identifying and predicting seizure events through advanced techniques, primarily utilizing electroencephalogram (EEG) signal. Despite significant progress, the field faces numerous challenges, including the need for diverse and comprehensive datasets, high computational complexity, and difficulties in generalizing models across various patient populations. This survey systematically reviews approximately 30 research articles, focusing on the methodologies employed, the challenges encountered, and the results obtained in seizure detection. By critically analyzing the strengths and limitations of existing approaches such as deep learning, machine learning, and hybrid models this research provides valuable insights into current practices and identifies opportunities for enhancing the effectiveness and reliability of seizure detection systems in clinical settings. Ultimately, the research aims to inform future developments in this vital domain, facilitating improved patient outcomes through timely and accurate seizure prediction.
Published Version
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