Advancements in Deep Learning Models for Epileptic Seizure Detection: Algorithms, Applications, and Future Perspectives

Deep learning is increasingly in demand these days. It is a form of machine learning based on artificial intelligence in which higher level features are extracted from data using numerous computational layers. We do predictions using single layer but additional hidden layers can give more accurate and refined results. A computer trains to do classification tasks directly from images, text, or audio sound using deep learning and sometime gives more accurate results comparative to humans. This review covers the colossal information of deep learning under one article. We have also studied the different types of deep learning algorithms, types of training, their challenges, and architecture applications. In this paper, deep learning algorithms are applied in medical field to detect the problem of epileptic patients in a very short time by reducing manual errors and improve the quality of life of patients. Epilepsy is a disorder of nervous system that are identified by electroencephalogram (EEG) signals and magnetic resonance image (MRI) scan that are analyzed by deep learning neural networks for epilepsy detection.