Abstract

BackgroundThe radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals.ObjectiveThe objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data.MethodsVarious machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier.ResultsIt was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted.ConclusionsWe expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.

Highlights

  • Pulse-taking is widely used in disease diagnosis and personal health monitoring

  • Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients

  • JMIR Med Inform 2020 | vol 8 | iss. 6 | e18134 | p. 1 researchers are increasingly attempting to detect a variety of subclinical diseases such as atrial fibrillation (AF) by radial artery pulse waves in the early stages of disease progression

Read more

Summary

Introduction

Pulse-taking is widely used in disease diagnosis and personal health monitoring. For example, in traditional Chinese medicine (TCM), pulse-taking is an important approach to differentiate TCM syndrome patterns in which the physician uses their fingers to detect patients’ pulsations. Researchers in the field of TCM diagnostics and hemodynamics have successfully utilized the information contained in single-period pulse waves to differentiate traditional syndrome patterns and diseases such as hypertension, diabetes, and other diseases not directly related to heart rhythm and to fit modern clinical indices through objective recording [3,4,5,6,7]. Incorporating single-period pulse wave signals in smartwatches may improve the accuracy of existing applications in an interpretable way and expand the application scope of radial artery pulse waves. The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call