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

doi:10.1002/ima.22505

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

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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 language	English
Pages (from-to)	972-980
Number of pages	9
Journal	International Journal of Imaging Systems and Technology
Volume	31
Issue number	2
Early online date	27 Oct 2020
DOIs	https://doi.org/10.1002/ima.22505
Publication status	Published - 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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10.1002/ima.22505

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

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).",

keywords = "convolutional neural network, electrode configuration, electrohysterogram, uterine contraction",

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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.",

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AU - Yang, Lin

AU - Liu, Xiaohong

AU - Jiang, Hongqing

AU - Zheng, Dingchang

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N2 - 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).

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

Abstract

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Keywords

ASJC Scopus subject areas

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