Deep Learning Techniques for Analyzing EEG Data

Abstract

Deep Learning models’ applicability to the prediction and classification of EEG-seizures has certainly been a challenge to researchers and academia. This paper examines the relation of deep learning approaches and the decoding of seizure events building upon the vast amount of empirical data present in the EEG recordings of the epilepsy patients. We are able to discover several prospects of using deep learning in seizure prediction models by understanding the aspects of signal processing, classification accuracy, and clinical relevance. Our conclusions point out that more than deep learning model accuracy is needed to implement effective systems integration. Approaches to solve these problems used by successful models in EEG of patients are useful recommendations for both scientists and practitioners. The paper focuses on the practical advice concerning the best ways to employ deep learning techniques to the problem of seizure prediction and classification based on EEG data and ultimately improving the efficiency of the treatment of patients suffering from epilepsy.