title: Nearest-neighbor based wavelet entropy rate measures for intrapartum fetal heart rate variability.
abstract: The interpretation and analysis of intrapartum fetal heart rate (FHR), enabling early detection of fetal acidosis, remains a challenging signal processing task. The ability of entropy rate measures, amongst other tools, to characterize temporal dynamics of FHR variability and to discriminate non-healthy fetuses has already been massively investigated. The present contribution aims first at illustrating that a k-nearest neighbor procedure yields estimates for entropy rates that are robust and well-suited to FHR variability (compared to the more commonly used correlation-integral algorithm). Second, it investigates how entropy rates measured on multiresolution wavelet and approximation coefficients permit to improve classification performance. To that end, a supervised learning procedure is used, that selects the time scales at which entropy rates contribute to discrimination. Significant conclusions are obtained from a high quality scalp electrode database of nearly two thousands subjects collected in a French public university hospital.
authors.forename: J
authors.initials: J
authors.lastname: Spilka
authors.forename: S G
authors.initials: SG
authors.lastname: Roux
authors.forename: N B
authors.initials: NB
authors.lastname: Garnier
authors.forename: P
authors.initials: P
authors.lastname: Abry
authors.forename: P
authors.initials: P
authors.lastname: Goncalves
authors.forename: M
authors.initials: M
authors.lastname: Doret
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D019277:Entropy; D005260:Female; D006340:Heart Rate, Fetal / Q000502:physiology*; D006801:Humans; D011247:Pregnancy; D058067:Wavelet Analysis