Seizure Type Classification Using EEG Based on Gramian Angular Field Transformation and Deep Learning

classification of seizure types plays a crucial role in diagnosis and prognosis of epileptic patients which has not been addressed properly, while most of the works are surrounded by seizure detection only. However, in recent times, few works have been attempted on the classification of seizure types using deep learning (DL). In this work, a novel approach based on DL has been proposed to classify four types of seizures - complex partial seizure, generalized non-specific seizure, simple partial seizure, tonic-clonic seizure, and seizure-free. Certainly, one of the most efficient classes of DL, convolution neural network (CNN) has achieved exemplary success in the field of image recognition. Therefore, CNN has been employed to perform both automatic feature extraction and classification tasks after generating 2D images from 1D electroencephalogram (EEG) signal by employing an efficient technique, called gramian angular summation field. Next, these images fed into CNN to perform binary and multiclass classification tasks. For experimental evaluation, the Temple University Hospital (TUH, v1.5.2) EEG dataset has been taken into consideration. The proposed method has achieved classification accuracy for binary and multiclass - 3, 4, and 5 up to 96.01%, 89.91%, 84.19%, and 84.20% respectively. The results display the potentiality of the proposed method in seizure type classification.Clinical relevance-gramian angular summation field, seizure types, convolution neural network.