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基于过零率的电子宫收缩信号自动识别。

Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate.

机构信息

Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China.

Department of Obstetrics, Peking Union Medical College Hospital, Beijing, 100730, China.

出版信息

Sci Rep. 2021 Jan 21;11(1):1956. doi: 10.1038/s41598-021-81492-1.

DOI:10.1038/s41598-021-81492-1
PMID:33479344
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7820321/
Abstract

Uterine contraction (UC) is an essential clinical indicator in the progress of labour and delivery. Electrohysterogram (EHG) signals recorded on the abdomen of pregnant women reflect the uterine electrical activity. This study proposes a novel algorithm for automatic recognition of UCs with EHG signals to improve the accuracy of detecting UCs. EHG signals by electrodes, the tension of the abdominal wall by tocodynamometry (TOCO) and maternal perception were recorded simultaneously in 54 pregnant women. The zero-crossing rate (ZCR) of the EHG signal and its power were calculated to modulate the raw EHG signal and highlight the EHG bursts. Then the envelope was extracted from the modulated EHG for UC recognition. Besides, UC was also detected by the conventional TOCO signal. Taking maternal perception as a reference, the UCs recognized by EHG and TOCO were evaluated with the sensitivity, positive predictive value (PPV), and UC parameters. The results show that the sensitivity and PPV are 87.8% and 93.18% for EHG, and 84.04% and 90.89% for TOCO. EHG detected a larger number of UCs than TOCO, which is closer to maternal perception. The duration and frequency of UC obtained from EHG and TOCO were not significantly different (p > 0.05). In conclusion, the proposed UC recognition algorithm has high accuracy and simple calculation which could be used for real-time analysis of EHG signals and long-term monitoring of UCs.

摘要

子宫收缩（UC）是分娩过程中的一个重要临床指标。记录在孕妇腹部的电子宫图（EHG）信号反映了子宫的电活动。本研究提出了一种新的算法，用于自动识别 EHG 信号中的 UC，以提高 UC 检测的准确性。同时记录了 54 名孕妇的电极 EHG 信号、腹压（TOCO）和母体感知。计算 EHG 信号的过零率（ZCR）及其功率来调制原始 EHG 信号并突出 EHG 爆发。然后从调制后的 EHG 中提取包络线以识别 UC。此外，还通过传统的 TOCO 信号检测 UC。以母体感知为参考，评估 EHG 和 TOCO 识别的 UC 的灵敏度、阳性预测值（PPV）和 UC 参数。结果表明，EHG 的灵敏度和 PPV 分别为 87.8%和 93.18%，TOCO 的灵敏度和 PPV 分别为 84.04%和 90.89%。EHG 检测到的 UC 数量多于 TOCO，更接近母体感知。EHG 和 TOCO 获得的 UC 持续时间和频率无显著差异（p>0.05）。总之，所提出的 UC 识别算法具有高精度和简单的计算，可用于 EHG 信号的实时分析和 UC 的长期监测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d6/7820321/2365fdf8556a/41598_2021_81492_Fig7_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d6/7820321/2365fdf8556a/41598_2021_81492_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62d6/7820321/20d688655551/41598_2021_81492_Fig1_HTML.jpg
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Deep neural network for semi-automatic classification of term and preterm uterine recordings.用于足月和早产子宫记录半自动分类的深度神经网络。
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Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions.
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