Key result
Hybrid 1D-CNN and GRU model classifies abnormal fetal status from cardiotocographic signals with 96% accuracy.
Why the study?
Classifying whether a fetus is abnormal during pregnancy is essential to help physicians carry out early intervention and avoid fetal heart hypoxia or death.
Does a 1D-CNN and Bi-directional GRU hybrid model accurately classify abnormal cardiotocographic signals?
Does a 1D-CNN and Bi-directional GRU hybrid model accurately classify abnormal cardiotocographic signals?
A 1D-CNN and Bi-directional GRU hybrid model can accurately classify cardiotocographic signals to detect fetal abnormalities, potentially providing automated decision support for physicians.
Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is vital for pregnant women before delivery. During pregnancy, it is essential to classify whether the fetus is abnormal, which helps physicians carry out early intervention to avoid fetal heart hypoxia and even death. Fetal heart rate and uterine contraction signals obtained by fetal heart monitoring equipment are essential to estimate fetal health status. In this paper, we pre-process the obtained data set and enhance them using Hermite interpolation on the abnormal classification in the samples. We use the 1D-CNN and GRU hybrid models to extract the abstract features of fetal heart rate and uterine contraction signals. Several evaluation metrics are used for evaluation, and the accuracy is 96 %, while the sensitivity is 95 %, and the specificity is 96 %. The experiments show the effectiveness of the proposed method, which can provide physicians and users with more stable, efficient, and convenient diagnosis and decision support.
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Liang et al. (2022) studied Fetal abnormality. 1D-CNN and GRU hybrid models was evaluated on Classification accuracy. A 1D-CNN and GRU hybrid model classified abnormal fetal status from cardiotocographic signals with 96% accuracy, 95% sensitivity, and 96% specificity.
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