Key result
A 1D convolutional neural network using pulse pressure wave data detected pregnancy with an accuracy of 85% and an AUC of 91%.
Why the study?
The quantitative relationship between pulse waves and health conditions has not been investigated in modern medicine.
Does deep learning analysis of pulse pressure wave (PPW) accurately detect pregnancy?
Cross-Sectional (n=1,840)
No
Does deep learning analysis of pulse pressure wave (PPW) accurately detect pregnancy?
Effect estimate: AUC 91.04%
Deep learning analysis of pulse pressure waves can detect pregnancy with 84% accuracy, providing a proof of concept for modernizing traditional pulse diagnosis.
Deep learning on pulse pressure waves may support pregnancy detection; leaves open prospective clinical validation.
Pulse feeling , representing the tactile arterial palpation of the heartbeat, has been widely used in traditional Chinese medicine (TCM) to diagnose various diseases. The quantitative relationship between the pulse wave and health conditions however has not been investigated in modern medicine. In this paper, we explored the correlation between pulse pressure wave (PPW), rather than the pulse key features in TCM, and pregnancy by using deep learning technology. This computational approach shows that the accuracy of pregnancy detection by the PPW is 84% with an area under the curve (AUC) of 91%. Our study is a proof of concept of pulse diagnosis and will also motivate further sophisticated investigations on pulse waves.
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Chen et al. (2019) conducted a cross-sectional in Pregnancy (n=1,840). Pulse pressure wave (PPW) data analyzed by 1D Convolutional Neural Network vs. Standard pregnancy tests (true pregnancy status) was evaluated on Accuracy and AUC of pregnancy detection (AUC 91.04%). A 1D convolutional neural network using pulse pressure wave data detected pregnancy with an accuracy of 85% and an AUC of 91%.
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