@inproceedings{4a4a83ce6c24420a854f49ba2457e97a,
title = "A multi-dimensional Hidden Markov Model approach to automated identification of fetal cardiac valve motion",
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-Suppeort Vector Machine (SVM) (79.8%) approaches.",
author = "Faezeh Marzbanrad and Khandoker, {Ahsan H.} and Miyuki Endo and Yoshitaka Kimura and Marimuthu Palaniswami",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 ; Conference date: 26-08-2014 Through 30-08-2014",
year = "2014",
month = nov,
day = "2",
doi = "10.1109/EMBC.2014.6943978",
language = "British English",
series = "2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1885--1888",
booktitle = "2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014",
address = "United States",
}