Multivariate analysis based on linear and non-linear FHR parameters for the identification of IUGR fetuses

G. Magenes, R. Bellazzi, A. Fanelli, M. G. Signorini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1868-1871
Number of pages4
ISBN (Print)9781424479290
DOIs
Publication statusPublished - Nov 2 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: Aug 26 2014Aug 30 2014

Other

Other2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period8/26/148/30/14

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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