首页> 外文会议>33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Conduction velocity of the uterine contraction in serial magnetomyogram (MMG) data: Event based simulation and validation
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Conduction velocity of the uterine contraction in serial magnetomyogram (MMG) data: Event based simulation and validation

机译:串行肌电图(MMG)数据中子宫收缩的传导速度:基于事件的模拟和验证

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We propose a novel approach to calculate the conduction velocity (CV) of the uterine contraction bursts in magnetomyogram (MMG) signals measured using a multichannel SQUID array. For this purpose, we partition the sensor coordinates into four different quadrants and identify the contractile bursts using a previously proposed Hilbert-wavelet transform approach. If contractile burst is identified in more than one quadrant, we calculate the center of gravity (CoG) in each quadrant for each time point as the sum of the product of the sensor coordinates with the Hilbert amplitude of the MMG signals normalized by the sum of the Hilbert amplitude of the signals over all sensors. Following this we compute the delay between the CoGs of all (six) possible quadrant pairs combinations. As a first step, we validate this approach by simulating a stochastic model based on independent second-order autoregressive processes (AR2) and we divide them into 30 second disjoint windows and insert burst activity at specific time instances in preselected sensors. Also we introduce a lag of 5 ± 1 seconds between different quadrants. Using our approach we calculate the CoG of the signals in a quadrant. To this end, we compute the delay between CoGs obtained from different quadrants and show that our approach is able to reliably capture the delay incorporated in the model. We apply the proposed approach to 19 serial MMG data obtained from two subjects and show an increase in the CV as the subjects approached labor.
机译:我们提出了一种新颖的方法来计算使用多通道SQUID阵列测量的子宫肌电图(MMG)信号中子宫收缩爆发的传导速度(CV)。为此,我们将传感器坐标划分为四个不同的象限,并使用先前提出的希尔伯特小波变换方法来识别收缩性爆发。如果在一个以上的象限中发现了收缩爆发,我们将每个时间点在每个象限中的重心(CoG)计算为传感器坐标乘以MMG信号的希尔伯特幅度的乘积之和,并以所有传感器上信号的希尔伯特幅度。接下来,我们计算所有(六个)可能象限对组合的CoG之间的延迟。第一步,我们通过模拟基于独立二阶自回归过程(AR2)的随机模型来验证此方法,然后将它们分为30秒不相交的窗口,并在特定时间点将突发活动插入预选传感器中。此外,我们在不同象限之间引入了5±1秒的延迟。使用我们的方法,我们可以计算一个象限中信号的CoG。为此,我们计算了从不同象限获得的CoG之间的延迟,并表明我们的方法能够可靠地捕获模型中包含的延迟。我们将拟议的方法应用于从两个受试者获得的19个MMG序列数据,并显示随着受试者接近分娩,CV会增加。

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