A Comprehensive Review on Epilepsy Diagnosis and Prognosis using Machine Learning and Deep Learning Approaches

Epilepsy is an illness that affects the nervous system and may lead to abnormal behavior, sensations, and even loss of consciousness in certain people. When someone is having a seizure, abnormal patterns of brain activity might be seen. About 50 million individuals all over the globe suffer from epilepsy
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. It is essential to begin treatment as soon as possible after an epileptic seizure, and it is also essential to diagnose or anticipate an approaching seizure as early as possible. This will allow for a reduction in the intensity and frequency of any potential bad effects. The rapidly decreasing cost of noninvasive EEG collecting equipment as well as the rapid development of machine learning algorithms have fueled passionate debate in this field of study. This paper investigates the methods that have been applied to the CHB-MIT scalp EEG database of epileptic signals, as well as a number of machine learning and deep learning algorithms that have been developed over the last several years and are based on a variety of characteristics and classifiers. The results of this study will help to choose the best machine learning or deep learning models for predicting epilepsy.