Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervise...

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Veröffentlicht in:Computers in biology and medicine Jg. 59; S. 54 - 63
Hauptverfasser: Yaghouby, Farid, Sunderam, Sridhar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.04.2015
Elsevier Limited
Schlagworte:
Accuracy
Adolescent
Adult
Aged
Algorithms
Alzheimer's disease
Automatic sleep scoring
EEG
Electroencephalography
Female
Gaussian mixture
Hidden Markov model
Humans
Internal Medicine
Male
Middle Aged
Models, Statistical
Other
Polysomnography - methods
PSG
Quasi-supervised
Signal Processing, Computer-Assisted
Sleep
Sleep - physiology
Supervised
Unsupervised
Young Adult
Quasi-supervised
PSG
EEG
Automatic sleep scoring
Hidden Markov model
Gaussian mixture
Unsupervised
Supervised
ISSN:0010-4825, 1879-0534, 1879-0534
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Zusammenfassung:The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18–79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models—specifically Gaussian mixtures and hidden Markov models—are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen׳s Κ statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations. •Three common problems associated with clinical sleep scoring are identified.•A quasi-supervised classification framework is proposed to address these problems.•The proposed framework compensates for rater uncertainty with reasonable accuracy.
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SourceType-Scholarly Journals-1
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2015.01.012