A machine learning method correlating pulse pressure wave data with pregnancy

TL;DR

This study uses deep learning to analyze pulse pressure wave data, achieving 84% accuracy in detecting pregnancy, thus bridging traditional pulse diagnosis with modern medical technology.

Contribution

It introduces a novel deep learning approach to correlate pulse pressure wave data with pregnancy, providing a quantitative basis for pulse diagnosis in modern medicine.

Findings

Pregnancy detection accuracy of 84%.

AUC of 91% for pulse wave-based diagnosis.

Proof of concept for pulse diagnosis in pregnancy detection.

Abstract

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.