  Many sleep studies suffer from the problem of insufficient data to fully
utilize deep neural networks as different labs use different recordings set
ups, leading to the need of training automated algorithms on rather small
databases, whereas large annotated databases are around but cannot be directly
included into these studies for data compensation due to channel mismatch. This
work presents a deep transfer learning approach to overcome the channel
mismatch problem and transfer knowledge from a large dataset to a small cohort
to study automatic sleep staging with single-channel input. We employ the
state-of-the-art SeqSleepNet and train the network in the source domain, i.e.
the large dataset. Afterwards, the pretrained network is finetuned in the
target domain, i.e. the small cohort, to complete knowledge transfer. We study
two transfer learning scenarios with slight and heavy channel mismatch between
the source and target domains. We also investigate whether, and if so, how
finetuning entirely or partially the pretrained network would affect the
performance of sleep staging on the target domain. Using the Montreal Archive
of Sleep Studies (MASS) database consisting of 200 subjects as the source
domain and the Sleep-EDF Expanded database consisting of 20 subjects as the
target domain in this study, our experimental results show significant
performance improvement on sleep staging achieved with the proposed deep
transfer learning approach. Furthermore, these results also reveal the
essential of finetuning the feature-learning parts of the pretrained network to
be able to bypass the channel mismatch problem.
