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A machine learning method correlating pulse pressure wave data with pregnancy
Author(s) -
Chen Jianhong,
Huang Huang,
Hao Wenrui,
Xu Jinchao
Publication year - 2020
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.3272
Subject(s) - pulse (music) , pressure wave , pregnancy , acoustics , computer science , mechanics , physics , optics , detector , biology , genetics
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.