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 journalArticle

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)(In-Press)
JournalInternational Journal of Imaging Systems and Technology
Volume(In-Press)
Early online date27 Oct 2020
DOIs
Publication statusE-pub ahead of print - 27 Oct 2020

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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