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

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 sign...

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Autores principales: Song, Xiaoxiao, Qiao, Xiangyun, Hao, Dongmei, Yang, Lin, Zhou, Xiya, Xu, Yuhang, Zheng, Dingchang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820321/
https://www.ncbi.nlm.nih.gov/pubmed/33479344
http://dx.doi.org/10.1038/s41598-021-81492-1
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author Song, Xiaoxiao
Qiao, Xiangyun
Hao, Dongmei
Yang, Lin
Zhou, Xiya
Xu, Yuhang
Zheng, Dingchang
author_facet Song, Xiaoxiao
Qiao, Xiangyun
Hao, Dongmei
Yang, Lin
Zhou, Xiya
Xu, Yuhang
Zheng, Dingchang
author_sort Song, Xiaoxiao
collection PubMed
description 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.
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spelling pubmed-78203212021-01-22 Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate Song, Xiaoxiao Qiao, Xiangyun Hao, Dongmei Yang, Lin Zhou, Xiya Xu, Yuhang Zheng, Dingchang Sci Rep Article 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. Nature Publishing Group UK 2021-01-21 /pmc/articles/PMC7820321/ /pubmed/33479344 http://dx.doi.org/10.1038/s41598-021-81492-1 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Song, Xiaoxiao
Qiao, Xiangyun
Hao, Dongmei
Yang, Lin
Zhou, Xiya
Xu, Yuhang
Zheng, Dingchang
Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate
title Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate
title_full Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate
title_fullStr Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate
title_full_unstemmed Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate
title_short Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate
title_sort automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820321/
https://www.ncbi.nlm.nih.gov/pubmed/33479344
http://dx.doi.org/10.1038/s41598-021-81492-1
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