title: Multivariate analysis based on linear and non-linear FHR parameters for the identification of IUGR fetuses.
abstract: Fetal Heart Rate (FHR) monitoring represents a powerful tool for checking the arousal of pathological fetal conditions during pregnancy. This paper proposes a multivariate approach for the discrimination of Normal and Intra Uterine Growth Restricted (IUGR) fetuses based on a small set of parameters computed on the FHR signal. We collected FHR recordings in a population of 120 fetuses (60 normals and 60 IUGRs) at approximately the same gestational week through a standard CTG non-stress test. A set of 8 linear and non-linear indices were selected and computed on each recording, on the basis of their "stand-alone" discriminative properties, demonstrated in previous studies. By using the Orange® data mining suite we checked various multivariate discrimination models. The results show that a Logistic Regression performed on a limited set of only 4 parameters can reach 92.5% accuracy in the correct identification of fetuses, with 93% sensitivity and 91.5% specificity.
authors.forename: G
authors.initials: G
authors.lastname: Magenes
authors.forename: R
authors.initials: R
authors.lastname: Bellazzi
authors.forename: A
authors.initials: A
authors.lastname: Fanelli
authors.forename: M G
authors.initials: MG
authors.lastname: Signorini
mesh_terms: D005260:Female; D005317:Fetal Growth Retardation / Q000503:physiopathology*; D005333:Fetus / Q000503:physiopathology; D005865:Gestational Age; D006340:Heart Rate, Fetal / Q000502:physiology*; D006801:Humans; D016015:Logistic Models; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D011247:Pregnancy; D012372:ROC Curve