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Extracting fetal heart rate from abdominal ECGs based on fast multivariate empirical mode decomposition

机译:基于快速多变量经验模型分解,从腹部ECG提取胎心率

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Abdominal electrocardiogram is an important means to obtain fetal health condition during high-risk pregnancy. In this paper, a novel method for extracting fetal heart rate from multi-channel mother abdomen electrocardiograms is proposed using fast multivariate empirical mode decomposition technique (FMEMD). Firstly, FMEMD decomposes the multichannel ECG signals into a set of modes. Two significant channels are selected according to the standard deviation of the fifth layer. Then the continuous wavelet transform technique (CWT) is applied to these two channels to denoise. The baseline is removed by zero-crossing rate. Following, the interference of the mother QRS complexes and non-overlapped fetal R-peaks can be eliminated and detected by CWT coefficient. The overlapped fetal R-peaks are obtained by combining the dynamic pattern matching program and creative algorithm. The proposed method achieves an accuracy of 99.9% on the existing data set, and the calculating time is only 1/6.39 of the MEMD-based method.
机译:腹心电图是在高危怀孕期间获得胎儿健康状况的重要手段。本文采用快速多元经验模型分解技术(FMEMD)提出了一种从多通道母腹心电图中提取来自多通道母腹心电图的新方法。首先,FMEMD将多声道ECG信号分解成一组模式。根据第五层的标准偏差选择两种重要通道。然后将连续小波变换技术(CWT)应用于这两个通道以去噪。通过过零率去除基线。遵循母QRS复合物的干扰和不重叠的胎儿R峰值可以通过CWT系数消除和检测。通过组合动态模式匹配程序和创意算法来获得重叠的胎儿R峰。所提出的方法在现有数据集上实现了99.9%的精度,并且计算时间仅为基于MEMD的方法的1 / 6.39。

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