Automatic sleep staging method of EEG signal based on transfer learning and fusion network

Abstract

Automatic sleep staging technology is a current research hotspot in brain-computer interfaces, freeing sleep specialists from the time-consuming task of manually diagnosing sleep. It performs complex sleep staging tasks by characterising sleep waves. Current research cannot fully interpret the meaning of EEG signals due to drawbacks such as high signal-to-noise ratio and insufficiency of EEG signals, and weak interpretability of depth models. In this research, we proposed an automatic sleep staging network of EEG signal based on transfer learning and integration of single-channel and multi-channel features. In Epoch processing block (EPB) stage and sequence processing block (SPB) stage, the frequency information and long-term features were extracted from the raw EEG and time-frequency data. Beside that, the transfer learning strategy was still adopted to overcome the data-variability and data-inefficiency issues and enable transferring knowledge from a large dataset to a small cohort. And then, the learning results of the two neural networks were fused adaptively and classified by using LightGBM. Lastly, we used the public dataset sleep-edf expanded to evaluate the performance of the system. The overall highest accuracy rate obtained on the sleep-edf expanded sleep-cassette (sc) subset was 87.84%, on the basis of not relying on massive training data, the accuracy rate is 1.57%-6.73% higher than other methods in the same experimental environment.