ArticleComputers in biology and medicine2019
Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram.
Article in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review.Sensors (Basel, Switzerland) · 2024Review
- Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.Sensors (Basel, Switzerland) · 2022Article
- Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors.Sensors (Basel, Switzerland) · 2022Article
- Recognition of uterine contractions with electrohysterogram and exploring the best electrode combination.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- IDENTIFICATION OF UTERINE CONTRACTIONS BY AN ENSEMBLE OF GAUSSIAN PROCESSES.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2021Article
- Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography.Sensors (Basel, Switzerland) · 2021Article
- Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate.Scientific reports · 2021Article
- COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.Interdisciplinary sciences, computational life sciences · 2020Article
- Acquisition and Analysis of Electrohysterogram Signal.Journal of medical systems · 2020Article
- Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks.BioMed research international · 2019Article
- Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest.Biocybernetics and biomedical engineeringArticle
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Authors and funding
6 authors.
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Abstract
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.
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