Effect of electrode configuration on recognizing uterine contraction with electrohysterogram: Analysis using a convolutional neural network

Dongmei Hao, Xiaoxiao Song, Qian Qiu, Xin Xin, Lin Yang, Xiaohong Liu, Hongqing Jiang, Dingchang Zheng

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)
    441 Downloads (Pure)

    Abstract

    This paper aimed to evaluate the effect of various electrode configurations on applying a convolutional neural network (CNN) to recognize uterine contraction (UC) with Electrohysterogram (EHG) signals. Seven 8-electrode configurations and thirteen 4-electrode configurations were selected from the 4 × 4 electrode grid in the Icelandic 16-electrode EHG database. EHG signals were divided into UC and non-UC sections of 45 seconds and saved as images. Each 8-electrode configuration with 7152 images and 4-electrode configuration with 3576 images were applied to train CNN to recognize UCs. A scoring method was proposed based on the area under the curve (AUC) and the accuracy to evaluate the effect of electrode configurations on recognizing UCs. The EHG signals from the 4 electrodes on the upper left of the uterus showed the best classification performance (AUC = 0.79, Accuracy = 0.72, Score = 2.30).

    Original languageEnglish
    Pages (from-to)972-980
    Number of pages9
    JournalInternational Journal of Imaging Systems and Technology
    Volume31
    Issue number2
    Early online date27 Oct 2020
    DOIs
    Publication statusPublished - Jun 2021

    Bibliographical note

    This is the peer reviewed version of the following article: Hao, D, Song, X, Qiu, Q, Xin, X, Yang, L, Liu, X, Jiang, H & Zheng, D 2021, 'Effect of electrode configuration on recognizing uterine contraction with electrohysterogram: Analysis using a convolutional neural network', International Journal of Imaging Systems and Technology, vol. 31, no. 2, pp. 972-980, which has been published in final form at https://doi.org/10.1002/ima.22505. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

    Funder

    National Key R&D Program of China (2019YFC0119700), Bill & Melinda Gates Foundation (OPP1148910), Beijing Natural Science Foundation (7172015), Beijing Science and Technology Project (Z161100000116005).

    Funding

    Beijing Science and Technology Project, Grant/Award Number: Z161100000116005; Bill and Melinda Gates Foundation, Grant/Award Number: OPP1148910; National Key R&D Program of China, Grant/Award Number: 2019YFC0119700; Natural Science Foundation of Beijing Municipality, Grant/Award Number: 7172015 Funding information This research was funded by National Key R&D Program of China (2019YFC0119700), Bill & Melinda Gates Foundation (OPP1148910), Beijing Natural Science Foundation (7172015), Beijing Science and Technology Project (Z161100000116005).

    FundersFunder number
    Bill and Melinda Gates FoundationOPP1148910
    Natural Science Foundation of Beijing Municipality7172015
    National Key Research and Development Program of China2019YFC0119700
    Beijing Science and Technology Planning ProjectZ161100000116005

      Keywords

      • convolutional neural network
      • electrode configuration
      • electrohysterogram
      • uterine contraction

      ASJC Scopus subject areas

      • Electronic, Optical and Magnetic Materials
      • Software
      • Computer Vision and Pattern Recognition
      • Electrical and Electronic Engineering

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