Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

Abstract--Although large annotated sleep databases are publicly available, and might be used to train automated scorin g algorithms, it might still be a challenge to develop an optim al algorithm for your personal sleep study, which might have fe w subjects or rely on a different recording setup. Both direct ly applying a learned algorithm or retraining the algorithm on your rather small database is suboptimal. And definitely sta te-of- the-art sleep staging algorithms based on deep neural netwo rks demand a large amount of data to be trained. This work present s a deep transfer learning approach to overcome the channel mismatch problem and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two netw orks adhering to this framework as a device for transfer learning . The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain, i.e. the small cohort, to complete knowle dge transfer . We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source dom ain and study deep transfer learning on four different target do - mains: the Sleep Cassette subset and the Sleep T elemetry sub set of the Sleep-EDF Expanded database, the Surrey-cEEGGrid database, and the Surrey-PSG database. The target domains are purposely adopted to cover different degrees of channel mismatch to the source domain. Our experimental results sho w significant performance improvement on automatic sleep sta ging on the target domains achieved with the proposed deep transf er learning approach and we discuss the impact of various fine tuning approaches. Index T erms --Automatic sleep staging, sequence-to-sequence, deep learning, transfer learning.