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A reliable probabilistic sleep stager based on a single EEG signal.
- Source :
-
Artificial intelligence in medicine [Artif Intell Med] 2005 Mar; Vol. 33 (3), pp. 199-207. - 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 (1s instead of 30s), and being based on solid probabilistic principles rather than a predefined set of rules (Rechtschaffen & Kales)<br />Methods and Material: Sixty-eight whole night sleep recordings from two different sleep labs are analysed using Gaussian observation Hidden Markov models.<br />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.<br />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.
- Subjects :
- Adult
Aged
Aged, 80 and over
Algorithms
Electroencephalography statistics & numerical data
Female
Humans
Male
Markov Chains
Middle Aged
Neural Networks, Computer
Normal Distribution
Probability
Reproducibility of Results
Sleep physiology
Sleep, REM physiology
Time Factors
Wakefulness physiology
Electroencephalography methods
Signal Processing, Computer-Assisted
Sleep Stages physiology
Subjects
Details
- Language :
- English
- ISSN :
- 0933-3657
- Volume :
- 33
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Artificial intelligence in medicine
- Publication Type :
- Academic Journal
- Accession number :
- 15811785
- Full Text :
- https://doi.org/10.1016/j.artmed.2004.04.004