A machine learning method correlating pulse pressure wave data with pregnancy
This provides a proof of concept for pulse diagnosis in modern medicine, potentially aiding in non-invasive health monitoring, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of detecting pregnancy using pulse pressure wave data, achieving an accuracy of 84% and an AUC of 91% with a deep learning approach.
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 AUC of 91%. Our study is a proof of concept of pulse diagnosis and will also motivate further sophisticated investigations on pulse waves.