3D residual-attention-deep-network-based childhood epilepsy syndrome classification

Abstract

Interictal electroencephalograms (EEGs) usually contain important information for epilepsy analysis and diagnosis. However, the focus of existing research has mainly been on epilepsy seizure onset detection, and only a few studies have been conducted on childhood epilepsy syndrome classification, which is usually more complicated than seizure detection. In this study, a novel 3D residual-attention-module-based deep network (AR3D) is developed to explore the spatial and time–frequency features of multichannel EEGs. The interictal EEGs of 37 patients with five typical childhood epilepsy syndromes, namely, benign childhood epilepsy with centrotemporal spikes, childhood absence epilepsy, febrile seizures plus, infantile spasms, unknown epilepsy syndrome, and one control group, are studied. The proposed AR3D algorithm, with a 97.03% F1 score, outperforms several state-of-the-art 2D and 3D convolution deep networks.