Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 59, Issue 2, Pages 483-493Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2011.2175225
Keywords
Bayesian methods; electroencephalography (EEG); Karhunen-Loeve (KL) transform; medical signal detection; sleep spindles
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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-Loeve 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.
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