Abstract
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.
Highlights
Uterine contraction (UC) is the tightening and shortening of the uterine muscles
EHG signals were first manually segmented into UCs and non-UCs based on UC annotations and TOCO signals. 7136 UCs and 7136 non-UCs of 60 s duration were extracted from 107 recordings of 40 pregnant women and used to establish a convolutional neural network (CNN) model. en, 1056 UCs and 1056 non-UCs were extracted from the other 15 recordings of 5 pregnant women
The EHG segments of 10 s, 20 s, and 30 s were classified as UC and non-UC using the established CNN model. e EHG segments of different durations were evaluated based on their sensitivity (SE), specificity (SP), and accuracy (ACC)
Summary
Uterine contraction (UC) is the tightening and shortening of the uterine muscles. UC can reflect the progress of pregnancy towards delivery and is a major observation for estimating the approach of delivery [1]. Electrohysterogram (EHG), which reflects uterine electrical activities, is a promising noninvasive technology for external UC monitoring [2]. It is still ambiguous which EHG segments are appropriate for recognizing UC. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. We aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). The developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. E proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN Conclusion. e proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
