DiBa: a data-driven Bayesian algorithm for sleep spindle detection.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 22084041.
- Also identified by DOI 10.1109/TBME.2011.2175225.
- No licence information is recorded for this record.
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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.
Medical subject headings
- Algorithms
- Bayes Theorem
- Electroencephalography
- Signal Processing, Computer-Assisted
- Sleep Stages