title: DiBa: a data-driven Bayesian algorithm for sleep spindle detection.
abstract: Although the spontaneous brain rhythms of sleep have commanded much recent interest, their detection and analysis remains suboptimal. In this paper, we develop a data-driven Bayesian algorithm for sleep spindle detection on the electroencephalography (EEG). The algorithm exploits the Karhunen-Loève transform and Bayesian hypothesis testing to produce the instantaneous probability of a spindle's presence with maximal resolution. In addition to possessing flexibility, transparency, and scalability, this algorithm could perform at levels superior to standard methods for EEG event detection.
authors.affiliation: Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114, USA. behtash@nmr.mgh.harvard.edu
authors.forename: Behtash
authors.initials: B
authors.lastname: Babadi
authors.forename: Scott M
authors.initials: SM
authors.lastname: McKinney
authors.forename: Vahid
authors.initials: V
authors.lastname: Tarokh
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Ellenbogen
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*