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J Sleep Res. 2018 Aug;27(4):e12614. doi: 10.1111/jsr.12614. Epub 2017 Oct 16.

Sleep spindle detection using multivariate Gaussian mixture models.

Author information

1
School of Engineering, RMIT University, Melbourne, Vic., Australia.
2
Interdisciplinary Sleep Centre at Charite Universitaetsmedizin Berlin, Berlin, Germany.
3
International Clinical Research Center, St Anne's University Hospital Brno, Brno, Czech Republic.

Abstract

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.

KEYWORDS:

Sigma index; expectation maximization; infinite impulse response filters

PMID:
29034521
DOI:
10.1111/jsr.12614
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