GFBLS: Graph-regularized fuzzy broad learning system for detection of interictal epileptic discharges

Abstract

Epilepsy as the most common neurological disorder globally has drawn more and more attention. However, it is time-consuming and labor-intensive for manual detection of interictal epileptic discharges (IEDs). Thus, there is an urgent need to develop an efficient and automated detection approach as a more accurate diagnostic alternative for epilepsy detection. Recently, fuzzy broad learning system (FBLS) has been recognized as an alternative to deep learning and utilized in various fields. Nevertheless, FBLS ignores the locally invariant property of data. To effectively address this issue and further improve the performance of FBLS, a novel graph-regularized fuzzy broad learning system (GFBLS) is first proposed based on graph regularization. Moreover, an automated GFBLS-based approach is proposed for IEDs detection from EEG recordings. In the proposed method, graph convolutional neural networks (GCN) is firstly utilized to extract features from line graphs with undirected connections, which are constructed by EEG recordings, then extracted features by GCN are fed into GFBLS for IEDs detection. The experimental results demonstrated that GFBLS can achieve accuracy of 92.20%, specificity of 90.90% and precision of 91.13% with the training time of only 31.6 s, which is superior or comparable performance compared with other state-of-the-art approaches.