A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

Abstract

Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

“…Secured, reproducible AI algorithms, good quality data, and efficient computing horse power are the major elements for the development of early detection, and prediction of epileptic waveforms through EEG signals [2,3]. There are several types of EEGs such as intracranial, scalp, ambulatory, etc through which good quality data can be generated.…”