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Sleep stage classification using unsupervised feature learning
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-0458-2146
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-3122-693X
2012 (English)In: Advances in Artificial Neural Systems, ISSN 1687-7594, E-ISSN 1687-7608, p. 107046-Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2012. p. 107046-
National Category
Engineering and Technology Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-24199DOI: 10.1155/2012/107046OAI: oai:DiVA.org:oru-24199DiVA, id: diva2:542617
Available from: 2012-08-02 Created: 2012-08-02 Last updated: 2018-01-12Bibliographically approved
In thesis
1. Modeling time-series with deep networks
Open this publication in new window or tab >>Modeling time-series with deep networks
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Örebro: Örebro university, 2014. p. 56
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 63
Keywords
multivariate time-series, deep learning, representation learning, unsupervised
National Category
Computer and Information Sciences
Research subject
Information technology
Identifiers
urn:nbn:se:oru:diva-39415 (URN)978-91-7529-054-6 (ISBN)
Public defence
2015-02-02, Hörsalen, Musikhögskolan, Örebro universitet, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2014-12-08 Created: 2014-12-08 Last updated: 2018-04-05Bibliographically approved

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Längkvist, MartinKarlsson, LarsLoutfi, Amy

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