Sleep Stage Classification Using Unsupervised Feature Learning
Joint Authors
Karlsson, Lars
Loutfi, Amy
Längkvist, Martin
Source
Advances in Artificial Neural Systems
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-07-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Längkvist, Martin& Karlsson, Lars& Loutfi, Amy. 2012. Sleep Stage Classification Using Unsupervised Feature Learning. Advances in Artificial Neural Systems،Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-446919
Modern Language Association (MLA)
Längkvist, Martin…[et al.]. Sleep Stage Classification Using Unsupervised Feature Learning. Advances in Artificial Neural Systems No. 2012 (2012), pp.1-9.
https://search.emarefa.net/detail/BIM-446919
American Medical Association (AMA)
Längkvist, Martin& Karlsson, Lars& Loutfi, Amy. Sleep Stage Classification Using Unsupervised Feature Learning. Advances in Artificial Neural Systems. 2012. Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-446919
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-446919