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 self-attention mechanism is suitable to automated identification of the epileptiform discharge in the EEG.