TY - JOUR
T1 - Automated measurement of fetal isovolumic contraction time from Doppler ultrasound signals without using fetal electrocardiography
AU - Marzbanrad, Faezeh
AU - Kimura, Yoshitaka
AU - Endo, Miyuki
AU - Palaniswami, Marimuthu
AU - Khandoker, Ahsan H.
PY - 2014
Y1 - 2014
N2 - Isovolumic Contraction Time (ICT) is the interval from mitral closing to aorta opening. Fetal ICT can be noninvasively measured from Doppler Ultrasound (DUS) signal. Automated identification of opening and closing of mitral and aortic valves from DUS signal was proposed in recent studies. Fetal electrocardiogram (fECG) has a crucial role as a reference in automated methods by identifying the onset of each cardiac cycle. However simultaneous recording of abdominal ECG and DUS and separation of fECG from the noisy mixture of ECG complicate this technique. In this study the automated identification of valve motion events without using fECG was investigated. The DUS signal was decomposed by Empirical Mode Decomposition (EMD) to high and low frequency components linked to valve and wall motion, respectively. The peaks of the latter were used for segmentation of the high frequency component as a substitute for fECG. The mitral and aortic valve motion was then automatically identified by hybrid Support Vector Machine (SVM)-Hidden Markov Model (HMM). Results show a significant positive linear correlation between average ICT obtained with and without using fECG (r=0.90, p<0.0001) with the mean absolute difference of 1.4 msec.
AB - Isovolumic Contraction Time (ICT) is the interval from mitral closing to aorta opening. Fetal ICT can be noninvasively measured from Doppler Ultrasound (DUS) signal. Automated identification of opening and closing of mitral and aortic valves from DUS signal was proposed in recent studies. Fetal electrocardiogram (fECG) has a crucial role as a reference in automated methods by identifying the onset of each cardiac cycle. However simultaneous recording of abdominal ECG and DUS and separation of fECG from the noisy mixture of ECG complicate this technique. In this study the automated identification of valve motion events without using fECG was investigated. The DUS signal was decomposed by Empirical Mode Decomposition (EMD) to high and low frequency components linked to valve and wall motion, respectively. The peaks of the latter were used for segmentation of the high frequency component as a substitute for fECG. The mitral and aortic valve motion was then automatically identified by hybrid Support Vector Machine (SVM)-Hidden Markov Model (HMM). Results show a significant positive linear correlation between average ICT obtained with and without using fECG (r=0.90, p<0.0001) with the mean absolute difference of 1.4 msec.
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M3 - Conference article
AN - SCOPUS:84931453006
VL - 41
SP - 485
EP - 488
JO - Computing in Cardiology
JF - Computing in Cardiology
SN - 2325-8861
IS - January
M1 - 7043085
T2 - 41st Computing in Cardiology Conference, CinC 2014
Y2 - 7 September 2014 through 10 September 2014
ER -