Automated Identification of fetal cardiac valve timings

Faezeh Marzbanrad, Ahsan H. Khandoker, Kiyoe Funamoto, Rika Sugibayashi, Miyuki Endo, Velayo Clarissa, Yoshitaka Kimura, Marimuthu Palaniswami

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

10 Citations (Scopus)

Abstract

In this paper a new noninvasive method is proposed for automated estimation of opening and closure timings of fetal cardiac valves. These timings are obtained from Doppler Ultrasound (DUS) signal and fetal electrocardiogram (fECG) as a reference. Empirical Mode Decomposition (EMD) is first applied to the DUS signal to decompose it into different components called Intrinsic Mode Functions (IMFs). The envelope of the first IMF is then taken and its peaks are identified. The opening and closure of the valves are then automatically assigned to the IMF peaks by using Hidden Markov Model (HMM). It is shown that this new method can continuously evaluate fetal cardiac valves' (aortic and mitral) motion timings for 82.5∼99.7% of cardiac cycles. The estimated timings are verified using the Pulsed Doppler images. These findings can be used as sensitive markers for evaluating the fetal cardiac performance.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3893-3896
Number of pages4
ISBN (Print)9781457702167
DOIs
Publication statusPublished - 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: 2013 Jul 32013 Jul 7

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period13/7/313/7/7

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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