  Humans approximately spend a third of their life sleeping, which makes
monitoring sleep an integral part of well-being. In this paper, a 34-layer deep
residual ConvNet architecture for end-to-end sleep staging is proposed. The
network takes raw single channel electroencephalogram (Fpz-Cz) signal as input
and yields hypnogram annotations for each 30s segments as output. Experiments
are carried out for two different scoring standards (5 and 6 stage
classification) on the expanded PhysioNet Sleep-EDF dataset, which contains
multi-source data from hospital and household polysomnography setups. The
performance of the proposed network is compared with that of the
state-of-the-art algorithms in patient independent validation tasks. The
experimental results demonstrate the superiority of the proposed network
compared to the best existing method, providing a relative improvement in
epoch-wise average accuracy of 6.8% and 6.3% on the household data and
multi-source data, respectively. Codes are made publicly available on Github.

Machine Learning;Computer Vision and Pattern Recognition;Signal Processing;Machine Learning