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

    1 Citation (Scopus)
    355 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).

    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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