Interactive local and global feature coupling for EEG-based epileptic seizure detection

Abstract

Automatic seizure detection based on scalp electroencephalogram (EEG) can accelerate the progress of epilepsy diagnosis. Current seizure detection methods based on deep learning usually rely on single convolutional neural network (CNN) or recurrent neural network (RNN) models. In terms of feature extraction, single model often has limitations. Since CNN is good at extracting local features, and Transformer can capture global information, we put forward a seizure detection method based on interactive local and global feature coupling. Local feature and global representation of the EEG are respectively extracted by convolution operation and self-attention mechanism. To make up of the shortcomings of local feature and global representation, a feature coupling block (FCB) is utilized to fuse the two kinds of information in an interactive way. The enhanced feature representation is fed to the classifier for seizure and normal EEG classification. Extensive experiments are conducted on CHB-MIT and Siena scalp EEG datasets. Experimental results demonstrate that the method can effectively perform epileptic seizure detection from the original EEG signals without extra feature extraction.