A single-channel EEG based automatic sleep stage classification method leveraging deep one-dimensional convolutional neural network and hidden Markov model

Bufang Yang, Xilin Zhu, Yitian Liu, Hongxing Liu

Abstract: Highlights•Our proposed 1D-CNN-HMM model combines 1D-CNN and HMM. 1D-CNN could extract features from raw EEG to perform epoch-wise classification, and HMM works as post-processing step to correct unreasonable sleep stage transitions.•We have demonstrated that HMM refinement is effective for 1D-CNN, and HMM improved the classification performance of 1D-CNN by improving the performance on S1 and REM stages with p < 0.05.•Results demonstrate that our method outperformed most of the existing single-channel EEG based methods under subject-independent paradigm using Sleep-EDFx and DRM-SUB datasets.