TY - JOUR
T1 - A machine learning method correlating pulse pressure wave data with pregnancy
AU - Chen, Jianhong
AU - Huang, Huang
AU - Hao, Wenrui
AU - Xu, Jinchao
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
UR - https://onlinelibrary.wiley.com/doi/10.1002/cnm.3272
UR - http://www.scopus.com/inward/record.url?scp=85075121634&partnerID=8YFLogxK
U2 - 10.1002/cnm.3272
DO - 10.1002/cnm.3272
M3 - Article
SN - 2040-7939
VL - 36
JO - International Journal for Numerical Methods in Biomedical Engineering
JF - International Journal for Numerical Methods in Biomedical Engineering
IS - 1
ER -