Sleep spindle detection using multivariate Gaussian mixture models - Patti, Chanakya Reddy; Penzel, Thomas; Cvetkovic, Dean (Journal of sleep research, 2018)
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: dospělí; Female; multivariační analýza; Adult; mladý dospělý; ženské pohlaví; Multivariate Analysis; Adolescent; shluková analýza; lidé; polysomnografie; infinite impulse response filters; databáze faktografické; sběr dat; Algorithms; Polysomnography; spánek; Male; Data Collection; Young Adult; Sleep; normální rozdělení; algoritmy; Databases, Factual; Sigma index; mužské pohlaví; Normal Distribution; expectation maximization; Cluster Analysis; elektroencefalografie; mladiství; Electroencephalography; Humans