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

Joint Authors

Peng, Jin
Hao, Dongmei
Liu, Haipeng
Liu, Juntao
Zhou, Xiya
Zheng, Dingchang

Source

BioMed Research International

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-10-13

Country of Publication

Egypt

No. of Pages

9

Main Subjects

Medicine

Abstract EN

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.

American Psychological Association (APA)

Peng, Jin& Hao, Dongmei& Liu, Haipeng& Liu, Juntao& Zhou, Xiya& Zheng, Dingchang. 2019. Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks. BioMed Research International،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1124411

Modern Language Association (MLA)

Peng, Jin…[et al.]. Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks. BioMed Research International No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1124411

American Medical Association (AMA)

Peng, Jin& Hao, Dongmei& Liu, Haipeng& Liu, Juntao& Zhou, Xiya& Zheng, Dingchang. Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1124411

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1124411