Sleep spindle detection using multivariate Gaussian mixture models
Language English Country England, Great Britain Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
29034521
DOI
10.1111/jsr.12614
Knihovny.cz E-resources
- Keywords
- Sigma index, expectation maximization, infinite impulse response filters,
- MeSH
- Algorithms MeSH
- Databases, Factual * MeSH
- Adult MeSH
- Electroencephalography methods MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Multivariate Analysis MeSH
- Normal Distribution MeSH
- Polysomnography methods MeSH
- Data Collection methods MeSH
- Cluster Analysis MeSH
- Sleep physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
In this research study we have developed a clustering-based automatic sleep spindle detection method that was evaluated on two different databases. The databases consisted of 20 all-night polysomnograph recordings. Past detection methods have been based on subject-independent and some subject-dependent parameters, such as fixed or variable thresholds to identify spindles. Using a multivariate Gaussian mixture model clustering technique, our algorithm was developed to use only subject-specific parameters to detect spindles. We have obtained an overall sensitivity range (65.1-74.1%) at a (59.55-119.7%) false positive proportion.
Interdisciplinary Sleep Centre at Charite Universitaetsmedizin Berlin Berlin Germany
International Clinical Research Center St Anne's University Hospital Brno Brno Czech Republic
School of Engineering RMIT University Melbourne Vic Australia
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