A multi-dimensional Hidden Markov Model approach to automated identification of fetal cardiac valve motion

Annu Int Conf IEEE Eng Med Biol Soc. 2014:2014:1885-8. doi: 10.1109/EMBC.2014.6943978.

Abstract

Fetal cardiac assessment techniques are aimed to identify fetuses at risk of intrauterine compromise or death. Evaluation of the electromechanical coupling as a fundamental part of the fetal heart physiology, provides valuable information about the fetal wellbeing during pregnancy. It is based on the opening and closing time of the cardiac valves and the onset of the QRS complex of the fetal electrocardiogram (fECG). The focus of this paper is on the automated identification of the fetal cardiac valve opening and closing from Doppler Ultrasound signal and fECG as a reference. To this aim a novel combination of Emprical Mode Decomposition (EMD) and multi-dimensional Hidden Markov Models (MD-HMM) was employed which provided beat-to-beat estimation of cardiac valve event timings with improved precision (82.9%) compared to the one dimensional HMM (77.4%) and hybrid HMM-Support Vector Machine (SVM) (79.8%) approaches.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aortic Valve / physiology
  • Automation
  • Electrocardiography
  • Female
  • Fetal Heart / diagnostic imaging
  • Fetal Heart / physiology*
  • Fetus / physiology
  • Heart Valves / physiology*
  • Humans
  • Markov Chains*
  • Models, Theoretical
  • Motion*
  • Pregnancy
  • Reproducibility of Results
  • Ultrasonography