Automatic sleep-stage classification for children using single-channel EEG

Nearly one-third of children suffer from sleep disorders. Although many researches have been conducted on the automatic sleep-stage classification for adults, the sleep stages of children have different characteristics. Therefore, there is an urgent need for sleep-stage classification specifically for children. The paper proposes a deep-learning model for the children automatic sleep-stage classification based on raw single-channel EEG. In the model, we utilize 1D convolutional neural networks (1D-CNN) to extract time-invariant features, and gated recurrent unit (GRU) to learn transition rules among sleep stages automatically from 30 s EEG epochs. Our method is tested on a dataset for children from 2 to 12 years of age. We use six different single-channel EEGs (F3-M2, F4-M1, C3-M2, C4-M1, O1-M2, O2- M1) to train the model separately, where the F4-M1 channel achieves the best results. Experimental results show that our method yields an overall classification accuracy of 83.36% and macro F1-score of 80.98%. This result indicates that our method has a great potential and lays the foundation for further research on the children sleep-stage classification.