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Automated measurement of fetal Isovolumic Contraction Time from Doppler Ultrasound Signals without using Fetal Electrocardiography

机译:自动测量从多普勒超声信号的胎儿存贮收缩时间,而无需使用胎儿心电图

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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.
机译:储存的收缩时间(ICT)是二尖瓣关闭到主动脉开口的间隔。可以从多普勒超声(DUS)信号中非侵略地测量胎儿ICT。在最近的研究中提出了从DUS信号的二尖瓣和主动脉瓣的打开和关闭的自动识别。胎儿心电图(FECG)通过识别每个心循环的发作,具有自动化方法的参考作用。然而,同时记录腹部心电图和DUS,从ECG的嘈杂混合使FECG复杂化这种技术。在这项研究中,研究了不使用FECG的阀门运动事件的自动识别。 DUS信号分别通过与阀门和墙壁运动连接的高和低频分量分解进行经验模式分解(EMD)。后者的峰用于高频分量的分割作为FECG的替代品。然后通过混合支持向量机(SVM)-HMM)自动识别二尖瓣和主动脉瓣运动。结果表明,在使用和不使用FECG(r = 0.90,p <; 0.0001)的情况下,平均ICT之间具有显着的正线性相关性,其平均绝对差为1.4毫秒。

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