Sleep stage classification using unsupervised feature learning

Authors:
Martin Längkvist;Lars Karlsson;Amy Loutfi
Affiliations:
Center for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden;Center for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden;Center for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden
Venue:
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Year:
2012

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Abstract

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.