Sleep spindle detection using multivariate Gaussian mixture models.

Summary: 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. [ABSTRACT FROM AUTHOR]