Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram

Hao, Dongmei and Peng, Jin and Wang, Ying and Liu, Juntao and Zhou, Xiya and Zheng, Dingchang (2019) Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram. Computers in Biology and Medicine, 113. p. 103394. ISSN 1879-0534

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Official URL: https://doi.org/10.1016/j.compbiomed.2019.103394


Uterine contraction (UC) activity is commonly used to monitor the approach of labour and delivery. Electrohysterograms (EHGs) have recently been used to monitor UC and distinguish between efficient and inefficient contractions. In this study, we aimed to identify UC in EHG signals using a convolutional neural network (CNN). An open-access database (Icelandic 16-electrode EHG database from 45 pregnant women with 122 recordings, DB1) was used to develop a CNN model, and 14000 segments with a length of 45 s (7000 from UCs and 7000 from non-UCs, which were determined with reference to the simultaneously recorded tocography signals) were manually extracted from the 122 EHG recordings. Five-fold cross-validation was applied to evaluate the ability of the CNN to identify UC based on its sensitivity (SE), specificity (SP), accuracy (ACC), and area under the receiver operating characteristic curve (AUC). The CNN model developed using DB1 was then applied to an independent clinical database (DB2) to further test its generalisation for recognizing UCs. The EHG signals in DB2 were recorded from 20 pregnant women using our multi-channel system, and 308 segments (154 from UCs and 154 from non-UCs) were extracted. The CNN model from five-fold cross-validation achieved average SE, SP, ACC, and AUC of 0.87, 0.98, 0.93, and 0.92 for DB1, and 0.88, 0.97, 0.93, and 0.87 for DB2, respectively. In summary, we demonstrated that CNN could effectively identify UCs using EHG signals and could be used as a tool for monitoring maternal and foetal health.

Item Type: Journal Article
Keywords: electrohysterogram, uterine contraction, convolutional neural network, maternal health, monitoring labour
Faculty: ARCHIVED Faculty of Medical Science (until September 2018)
Depositing User: Professor D Zheng
Date Deposited: 29 Aug 2019 08:23
Last Modified: 09 Sep 2021 18:54
URI: https://arro.anglia.ac.uk/id/eprint/704688

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