Model-based estimation of aortic and mitral valves opening and closing timings in developing human fetuses

Faezeh Marzbanrad, Yoshitaka Kimura, Kiyoe Funamoto, Sayaka Oshio, Miyuki Endo, Naoaki Sato, Marimuthu Palaniswami, Ahsan H. Khandoker

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Electromechanical coupling of the fetal heart can be evaluated noninvasively using doppler ultrasound (DUS) signal and fetal electrocardiography (fECG). In this study, an efficient model is proposed using K-means clustering and hybrid Support Vector Machine-Hidden Markov Model (SVM-HMM) modeling techniques. Opening and closing of the cardiac valves were detected from peaks in the high frequency component of the DUS signal decomposed by wavelet analysis. It was previously proposed to automatically identify the valve motion by hybrid SVM-HMM[1] based on the amplitude and timing of the peaks. However, in the present study, six patterns were identified for the DUS components which were actually variable on a beat-to-beat basis and found to be different for the early gestation (16-32 weeks), compared to the late gestation fetuses (36-41 weeks). The amplitude of the peaks linked to the valve motion was different across the six patterns and this affected the precision of valve motion identification by the previous hybrid SVM-HMM method. Therefore in the present study, clustering of the DUS components based on K-means was proposed and the hybrid SVM-HMM was trained for each cluster separately. The valve motion events were consequently identified more efficiently by beat-to-beat attribution of the DUS component peaks. Applying this method, more than 98.6% of valve motion events were beatto-beat identified with average precision and recall of 83.4% and 84.2% respectively. It was an improvement compared to the hybrid method without clustering with average precision and recall of 79.0% and 79.8%. Therefore, this model would be useful for reliable screening of fetal wellbeing.

Original languageEnglish
Article number6926782
Pages (from-to)240-248
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Publication statusPublished - 2016 Jan


  • Doppler ultrasound
  • Fetal assessment
  • Fetal cardiac intervals
  • Hidden Markov models (HMM)
  • Hybrid SVM-HMM
  • K-means clustering
  • Support vector machine (SVM)
  • Wavelet analysis

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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