Epileptic Spike Detection by Recurrent Neural Networks with Self-Attention Mechanism

Automated identification of epileptiform discharges in electroencephalograms (EEG) for the diagnosis of epilepsy can mitigate the burden of manual searches. Recent effective methods based on machine learning–based classification have used detection of candidate waveforms with signal processing and pattern matching as preprocessing, and this method can determine the overall performance. This paper thus considers a scenario where candidates are not detected; that is, we propose a recurrent neural network (RNN)–based self-attention model that can be fitted from the EEG segments generated without spike candidates being detected. In comparison with the state-of-the-art machine learning models that can be applied to EEG classification (LightGBM and EEGNet), the proposed model achieved higher performance (average accuracy: 90.2%). This result strongly suggests that the selfattention mechanism is suitable to automated identification of the epileptiform discharge in the EEG.