Estimation of 8-Electrode Configuration for Recognition of Uterine Contraction with Electrohysterogram

Hao, Dongmei1; Qiao, Xiangyun1; Song, Xiaoxiao1; Wang, Ying1; Qiu, Qian1; Jiang, Hongqing2; Chen, Fei3

10.1109/EMBC.2019.8857389

2019

1557-170X

978-1-5386-1312-2

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

672-675

23-27 July 2019

Berlin, Germany

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

As the representative of electrical activity from uterine muscle, electrohysterogram (EHG) is recorded non-invasively by multiple electrodes positioned on the abdominal surface. The purpose of our paper is to estimate different electrode configurations for recognizing uterine contractions (UCs) with EHG signals. 8-electrode configuration was taken as an example to show our novel method with convolutional neural network (CNN) classification and score. The open accessed Icelandic 16-electrode EHG database was adopted in our study. With 8-electrode configuration, EHG signals corresponding to UCs and non-UCs were segmented and saved as image patches. The CNN was established and trained by thousands of EHG segments. The performance of CNN was evaluated by the area under curve (AUC) and accuracy of recognizing UCs and non-UCs. Seven different 8-electrode configurations were scored and ranked. It was found the 8-electrode configuration with 4 on the uterine fundus, 2 on the body and 2 on the cervix achieved the AUC of 0.766 and the highest score of 2.197. Among the configurations we have tried, it is concluded that the 8 electrodes in 4-2-2 configuration placed along the uterus as an upside-down pear could provide the most important information for recognition of UC based on our experiments.
© 2019 IEEE.

Electrodes Databases Image segmentation Feature extraction Convolutional neural networks Convolution Kernel

其他

英语

EI ; CPCI-S

Bill and Melinda Gates Foundation[OPP1148910] ; Natural Science Foundation of Beijing Municipality[7172015]

Engineering

Engineering, Biomedical ; Engineering, Electrical & Electronic

WOS:000557295301024

20200308035662

Convolutional neural networks ; Medical imaging

Biomedical Engineering:461.1 ; Imaging Techniques:746

EV Compendex

1.College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing; 100024, China
2.Haidian Maternal and Children Health Hospital, Beijing; 100080, China
3.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen; 518055, China

Hao, Dongmei,Qiao, Xiangyun,Song, Xiaoxiao,et al. Estimation of 8-Electrode Configuration for Recognition of Uterine Contraction with Electrohysterogram[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:672-675.