Computational techniques, classification, datasets review and way forward with modern analysis of epileptic seizure – a study

According to World Health Organization (WHO), it is estimated that approximately 50 million people have Epilepsy worldwide. 10 million people are effected in India by Epilepsy hardly very few come out with the disorder and undertake proper treatment rest risk their life without recognition of disease. Epilepsy is a chronic disease of the brain which is non-communicable. An epileptic seizure is the second most commonly occurring neurological disorder of the brain. It can be detected using Electroencephalography (EEG) signals, an effective diagnostic tool used to study brain anatomy. Technological advancements and modernization of analytical tools have improved the scope of validation and decision-making. It is important to predict and evaluate epilepsy in its initial stages to avoid the risk and complication. This review paper focuses on the methodology, techniques, and dataset used from different sources to classify and categorize epileptic seizures. The paper includes a detailed review of dataset sources, challenges, and ways forward in understanding epileptic seizures for futuristic learning and decision making. It also concentrates on the comparison of machine learning and deep learning methodology used with different datasets.