Anglia Ruskin Research Online (ARRO)
Browse
Peng_et_al_2019.pdf (1.53 MB)

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Download (1.53 MB)
journal contribution
posted on 2023-07-26, 14:49 authored by Jin Peng, Dongmei Hao, Haipeng Liu, Juntao Liu, Xiya Zhou, Dingchang Zheng
Background. Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). Materials and Methods. In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. Results. The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak. Conclusion. The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN.

History

Refereed

  • Yes

Volume

2019

Page range

3168541

Publication title

BioMed Research International

ISSN

2314-6141

Publisher

Hindawi

File version

  • Published version

Language

  • eng

Legacy posted date

2019-11-25

Legacy creation date

2019-11-25

Legacy Faculty/School/Department

Faculty of Health, Education, Medicine & Social Care

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Keywords

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC