Sleep spindle detection using multivariate Gaussian mixture models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F18%3A00069354" target="_blank" >RIV/00159816:_____/18:00069354 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1111/jsr.12614" target="_blank" >http://dx.doi.org/10.1111/jsr.12614</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/jsr.12614" target="_blank" >10.1111/jsr.12614</a>

Result language
angličtina
Original language name
Sleep spindle detection using multivariate Gaussian mixture models
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30103 - Neurosciences (including psychophysiology)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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