Deep feature fusion based childhood epilepsy syndrome classification from electroencephalogram

Accurate classification of the children's epilepsy syndrome is vital to the diagnosis and treatment of epilepsy. But existing literature mainly focuses on seizure detection and few attention has been paid to the children's epilepsy syndrome classification. In this paper, we present a study on the classification of two most common epilepsy syndromes: the benign childhood epilepsy with centrotemporal spikes (BECT) and the infantile spasms (also known as the WEST syndrome), recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). A novel feature fusion model based on the deep transfer learning and the conventional time-frequency representation of the scalp electroencephalogram (EEG) is developed for the epilepsy syndrome characterization. A fully connected network is constructed for the feature learning and syndrome classification. Experiments on the CHZU database show that the proposed algorithm can offer an average of 92.35% classification accuracy on the BECT and WEST syndromes and their corresponding normal cases. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Accurate classification of children's epilepsy syndromes is crucial for the diagnosis and treatment of epilepsy. This paper presents a study on the classification of two common epilepsy syndromes using a novel feature fusion model based on deep transfer learning and conventional time-frequency representation. Experimental results demonstrate high classification accuracy of the proposed algorithm.