title: Interpretation of Doppler blood flow velocity waveforms using neural networks.
abstract: Doppler umbilical artery blood flow velocity waveform measurement is used in perinatal surveillance for the evaluation of pregnancy status. There is an ongoing debate on the predictive value of Doppler measurements concerning the critical effect of the selection of parameters for the evaluation of Doppler output. In this paper, we describe how neural network methods can be used both to discover relevant classification features and subsequently to classify patients. Classification accuracy varied from 92-99% correct.
authors.affiliation: Dept. of Computer Science, University of Maryland, College Park 20742.
authors.forename: N
authors.initials: N
authors.lastname: Baykal
authors.forename: J A
authors.initials: JA
authors.lastname: Reggia
authors.forename: N
authors.initials: N
authors.lastname: Yalabik
authors.forename: A
authors.initials: A
authors.lastname: Erkmen
authors.forename: M S
authors.initials: MS
authors.lastname: Beksac
mesh_terms: D001783:Blood Flow Velocity; D002965:Classification / Q000379:methods; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D011247:Pregnancy; D018608:Ultrasonography, Doppler / Q000145:classification*; D016216:Ultrasonography, Prenatal / Q000145:classification*; D014469:Umbilical Arteries / Q000000981:diagnostic imaging* / Q000502:physiology