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Sleep spindle detection using multivariate Gaussian mixture models

CR. Patti, T. Penzel, D. Cvetkovic,

. 2018 ; 27 (4) : e12614. [pub] 20171016

Language English Country Great Britain

Document type Journal Article, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc19045593

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.

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