Using Hidden Markov Models to Build an Automatic, Continuous and Probabilistic Sleep Stager

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	Similar Articles Articles by A. Flexer Articles by P. Sykacek Articles by G. Dorffner Articles by I. Rezek

IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3

Como, Italy

July 24-July 27

ISBN: 0-7695-0619-4

	ASCII Text	x
	A. Flexer, P. Sykacek, G. Dorffner, I. Rezek, "Using Hidden Markov Models to Build an Automatic, Continuous and Probabilistic Sleep Stager," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 3, pp. 3627, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000.

	BibTex	x
	@article{ 10.1109/IJCNN.2000.861392, author = {A. Flexer and P. Sykacek and G. Dorffner and I. Rezek}, title = {Using Hidden Markov Models to Build an Automatic, Continuous and Probabilistic Sleep Stager}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {3}, year = {2000}, issn = {1098-7576}, pages = {3627}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861392}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - Using Hidden Markov Models to Build an Automatic, Continuous and Probabilistic Sleep Stager SN - 1098-7576 SP EP A1 - A. Flexer, A1 - P. Sykacek, A1 - G. Dorffner, A1 - I. Rezek, PY - 2000 VL - 3 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

A. Flexer, The Austrian Research Institute for Artificial Intelligence

P. Sykacek, The Austrian Research Institute for Artificial Intelligence

G. Dorffner, The Austrian Research Institute for Artificial Intelligence

I. Rezek, University of Oxford

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861392

We report about an automatic continuous sleep stager, which is based on probabilistic principles employing Hidden Markov Models (HMM). Our sleep stager offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1 second instead of 30 second), and being based on solid probabilistic principles rather than a predefined set of rules (Rechtschaffen & Kales). Results obtained for nine whole night sleep recordings are reported.

Citation:

A. Flexer, P. Sykacek, G. Dorffner, I. Rezek, "Using Hidden Markov Models to Build an Automatic, Continuous and Probabilistic Sleep Stager," ijcnn, vol. 3, pp.3627, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000

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