DiBa: a data-driven Bayesian algorithm for sleep spindle detection.

Behtash Babadi, Scott M. McKinney, Vahid Tarokh, Jeffrey M. Ellenbogen

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)483-493
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Volume59
Issue number2
DOIs
Publication statusPublished - Feb 2012
Externally publishedYes

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ASJC Scopus subject areas

  • Medicine(all)

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