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首页> 外文期刊>Ultrasound in Medicine and Biology >MULTIDIMENSIONAL ULTRASOUND DOPPLER SIGNAL ANALYSIS FOR FETAL ACTIVITY MONITORING
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MULTIDIMENSIONAL ULTRASOUND DOPPLER SIGNAL ANALYSIS FOR FETAL ACTIVITY MONITORING

机译：胎儿活动监测的多维超声多普勒信号分析

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Fetal activity parameters such as movements, heart rate and the related parameters are essential indicators of fetal wellbeing, and no device provides simultaneous access to and sufficient estimation of all of these parameters to evaluate fetal health. This work was aimed at collecting these parameters to automatically separate healthy from compromised fetuses. To achieve this goal, we first developed a multi-sensor-multi-gate Doppler system. Then we recorded multidimensional Doppler signals and estimated the fetal activity parameters via dedicated signal processing techniques. Finally, we combined these parameters into four sets of parameters (or four hyperparameters) to determine the set of parameters that is able to separate healthy from other fetuses. To validate our system, a data set consisting of two groups of fetal signals (normal and compromised) was established and provided by physicians. From the estimated parameters, an instantaneous Manning-like score, referred to as the ultrasonic score, was calculated and was used together with movements, heart rate and the associated parameters in a classification process employing the support vector machine method. We investigated the influence of the sets of parameters and evaluated the performance of the support vector machine using the computation of sensibility, specificity, percentage of support vectors and total classification error. The sensitivity of the four sets ranged from 79% to 100%. Specificity was 100% for all sets. The total classification error ranged from 0% to 20%. The percentage of support vectors ranged from 33% to 49%. Overall, the best results were obtained with the set of parameters consisting of fetal movement, short-term variability, long-term variability, deceleration and ultrasound score. The sensitivity, specificity, percentage of support vectors and total classification error of this set were respectively 100%, 100%, 35% and 0%. This indicated our ability to separate the data into two sets (normal fetuses and pathologic fetuses), and the results highlight the excellent match with the clinical classification performed by the physicians. This work indicates the feasibility of detecting compromised fetuses and also represents an interesting method of close fetal monitoring during the entire pregnancy. (C) 2015 World Federation for Ultrasound in Medicine & Biology.
机译：诸如活动，心率和相关参数之类的胎儿活动参数是胎儿健康的重要指标，并且没有任何设备可以同时访问所有这些参数并对其进行充分估计以评估胎儿的健康状况。这项工作旨在收集这些参数，以自动将健康胎儿与受损胎儿区分开。为了实现这一目标，我们首先开发了多传感器多门多普勒系统。然后，我们记录了多维多普勒信号，并通过专用信号处理技术估算了胎儿活动参数。最后，我们将这些参数组合成四组参数（或四个超参数），以确定能够将健康与其他胎儿区分开的一组参数。为了验证我们的系统，由医生建立并提供了由两组胎儿信号（正常和受损）组成的数据集。根据估计的参数，计算出一个瞬时的类似于Manning的得分，称为超声得分，并与运动，心率和相关参数一起在采用支持向量机方法的分类过程中使用。我们调查了参数集的影响，并通过敏感性，特异性，支持向量的百分比和总分类误差的计算来评估支持向量机的性能。四组的灵敏度范围为79％至100％。所有组的特异性均为100％。总分类误差为0％至20％。支持向量的百分比范围从33％到49％。总体而言，使用胎儿运动，短期变异性，长期变异性，减速度和超声评分组成的一组参数可获得最佳结果。该组的敏感性，特异性，支持载体的百分比和总分类误差分别为100％，100％，35％和0％。这表明我们有能力将数据分为两组（正常胎儿和病理性胎儿），结果突出显示了与医师进行的临床分类的极佳匹配。这项工作表明了检测受损胎儿的可行性，也代表了在整个怀孕期间密切监测胎儿的有趣方法。 （C）2015年世界医学和生物学超声联合会。

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