Scalp EEG-Based Automatic Detection of Epileptiform Events via Graph Convolutional Network and Bi-Directional LSTM Co-Embedded Broad Learning System

The Scalp Electroencephalogram (EEG) signal of epileptic patients often contains Interictal Epileptiform Discharges (IED) during the period of seizures. Detection of IEDs is significant for the diagnosis of epilepsy and the prediction of seizures. In this letter, we proposed a graph convolutional network and bi-directional LSTM co-embedded broad learning system to detect IEDS. Here, we represent EEG signal as a graph and utilize Graph Convolutional Networks (GCN) to extract contextual features. In addition, bi-directional LSTM is also adopted for extracting the temporal feature from signals. Then these features are incorporated into Broad Learning System (BLS) to automatically detect epileptiform events. Experimental results indicate the proposed approach can achieve superior accuracy in the classification of IEDs than other commonly used time series processing models and reach a consensus with neurologists in predicting the lead of an EEG recording.