Benchmarking matching pursuit to find sleep spindles.

Pós Graduação em Clínica Médica da Universidade Federal do Rio Grande do Sul, Hospital de Clínicas de Porto Alegre, Brazil.
Journal of Neuroscience Methods (Impact Factor: 1.96). 10/2006; 156(1-2):314-21. DOI: 10.1016/j.jneumeth.2006.01.026
Source: PubMed

ABSTRACT The aim of this study is to evaluate performance of Matching Pursuit (MP) algorithm against visual analysis for automatic sleep spindle (SS) detection in a sample of sleep stages 2-4 and REM pertaining to nine healthy young subjects. MP-SS voltage, frequency and duration characteristics were investigated for the amplitude threshold (AT) that maximized yield between test sensitivity and specificity. Parameter distribution curves were also built for correctly detected (true positive) and false-positive events. For sleep stage 2, MP reached 80.6% sensitivity and specificity for an AT value of 58.8. For all stages together, 81.2% sensitivity and specificity were reached for an AT value of 46.6. Specificity curves were adequate for all stages; sensitivity was lower for S3+4. Sigma frequency range activity with atypical characteristics was detected within REM sleep. Prevalence indexes obtained with MP were much higher than visual prevalence indexes for all stages; similar voltage, frequency and duration distribution curves were obtained for true positive and false positive events. For this sample of young male healthy subjects, the free-ware MP algorithm showed satisfactory performance for SS detection in sleep stage 2 as reported earlier, acceptable performance in sleep stages 3+4, although with lowered sensitivity, and sigma frequency range activity within REM sleep that needs better understanding. Within NREM sleep, correspondence between the MP automatic and the visual method was supported.

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    ABSTRACT: In this paper we introduce a procedure based on Matching Pursuit with Gabor-chirping dictionary and apply it to the spectral decomposition of a whole-night EEG sig-nal from a group of nine young normal subjects. It was thus possible to identify sleep spindles (SS) and quantify them not only in frequency but also in modulation of this fre-quency. We identified a slowing pattern for these elements that should be explained in terms of their thalamocortical generating mechanism.


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Jun 2, 2014