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

机译:从多普勒超声信号自动测量胎儿等容收缩时间,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:等容收缩时间(ICT)是从二尖瓣关闭到主动脉张开的间隔。胎儿ICT可以通过多普勒超声(DUS)信号进行无创测量。最近的研究提出了根据DUS信号自动识别二尖瓣和主动脉瓣的打开和关闭的方法。胎儿心电图(fECG)通过识别每个心动周期的发作,在自动化方法中起着至关重要的作用。但是,同时记录腹部ECG和DUS以及从嘈杂的ECG混合物中分离出fECG会使这项技术复杂化。在这项研究中,研究了在不使用fECG的情况下自动识别气门运动事件。 DUS信号通过经验模式分解(EMD)分解为分别与阀门和壁运动相关的高频和低频分量。后者的峰用于分割高频成分,以代替fECG。然后通过混合支持向量机(SVM)-隐马尔可夫模型(HMM)自动识别二尖瓣和主动脉瓣运动。结果显示,在使用和不使用fECG的情况下,平均ICT之间存在显着的线性正相关(r = 0.90,p <; 0.0001),平均绝对差为1.4毫秒。

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