Abstract: Spike recognition in scalp EEG recording can contribute greatly to diagnosis process of epilepsy, however, problems exist in reported methods including low specificity and lack of inter-channel information. This study implements a deep neural network based algorithm to achieve automatic detection of spikes in epileptic EEG. The study used clinical scalp EEG records and established a stratified convolutional and graph attention deep neural network model for the first time, which achieved effective feature extraction from inner-channels and inter-channels of the EEG electrodes. The experiment not only found that the classification result reached the state of art 93.55% accuracy and 96.83% specificity. At the same time, it was found that the attention index between different electrode channels had a positive correlation with the discharge frequency of the region - the electrodes with spikes were aggregating and detached from the background electrodes in the graph, which could be used as auxiliary information for focus localization. The above algorithm innovatively utilizes cross channel relations in a data-driven manner, achieves superior accuracy and has promising clinical application prospects.

Key words: scalp EEG, spike detection, deep learning, attention model, convolutional neural network