Table of Contents
Advances in Artificial Neural Systems
Volume 2012, Article ID 107046, 9 pages
Research Article

Sleep Stage Classification Using Unsupervised Feature Learning

Center for Applied Autonomous Sensor Systems, Örebro University, 701 82 Örebro, Sweden

Received 17 February 2012; Revised 5 May 2012; Accepted 6 May 2012

Academic Editor: Juan Manuel Gorriz Saez

Copyright © 2012 Martin Längkvist et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.