Comparison of Deep Learning Model with other Automatic Learning Models for Recognizing Spikes Expressed from Epilepsy Patients ☆

In the clinical diagnosis of epilepsy using EEG data, the ability to automatically detect and correctly classify epilepsy spikes is helpful and significant for medicine. The article introduces a new approach for automatic detection of spike. Currently, epilepsy classification has been progressed based on many combined methods of machine learning models. This study investigates a new combined model that specifically takes Deep learning as a subset of Machine learning to perform the classification of epilepsy based on existing standard data sources. The study also implements experimental models in other deep learning models to evaluate the applicability of the model in detecting epilepsy spikes. Experimental results show that the proposed model has high accuracy (98.8%) compared to other studies.