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A reliable probabilistic sleep stager based on a single EEG signal
- Source :
-
Artificial Intelligence in Medicine . Mar2005, Vol. 33 Issue 3, p199-207. 9p. - Publication Year :
- 2005
-
Abstract
- Objective: We developed a probabilistic continuous sleep stager based on Hidden Markov models using only a single EEG signal. It offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1 s instead of 30 s), and being based on solid probabilistic principles rather than a predefined set of rules (Rechtschaffen & Kales) Methods and material: Sixty-eight whole night sleep recordings from two different sleep labs are analysed using Gaussian observation Hidden Markov models. Results: Our unsupervised approach detects the cornerstones of human sleep (wakefulness, deep and rem sleep) with around 80% accuracy based on data from a single EEG channel. There are some difficulties in generalizing results across sleep labs. Conclusion: Using data from a single electrode is sufficient for reliable continuous sleep staging. Sleep recordings from different sleep labs are not directly comparable. Training of separate models for the sleep labs is necessary. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09333657
- Volume :
- 33
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Artificial Intelligence in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 16873441
- Full Text :
- https://doi.org/10.1016/j.artmed.2004.04.004