title: Automatic K-complexes detection in sleep EEG recordings using likelihood thresholds.
abstract: In this paper, we present an automatic method for K-complexes detection based on features extraction and the use of fuzzy thresholds. The validity of our process was examined on the basis of two visual K-complexes scorings performed on 5 excerpts of 30 minutes. Results were investigated through all different sleep stages. The algorithm provides global true positive rates of 61.72% and 60.94%, respectively with scorer 1 and scorer 2. The false positive proportions (compared to the total number of visually scored K-complexes) are of 19.62% and 181.25%, while the false positive rates estimated on a one 1 second resolution are only of 0.53% and 1.53%. These results suggest that our approach is completely suitable since its performances are similar to those of the human scorers.
authors.affiliation: TCTS Lab, Université de Mons - UMONS, B-7000, Belgium. stephanie.devuyst@umons.ac.be
authors.forename: S
authors.initials: S
authors.lastname: Devuyst
authors.forename: T
authors.initials: T
authors.lastname: Dutoit
authors.forename: P
authors.initials: P
authors.lastname: Stenuit
authors.forename: M
authors.initials: M
authors.lastname: Kerkhofs
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004056:Differential Threshold / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012890:Sleep / Q000502:physiology*