Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Nov 01, 2021)
Date Submitted: Jul 1, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram
ABSTRACT
Background:
Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk.
Objective:
In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment.
Methods:
The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation.
Results:
Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak.
Conclusions:
The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. Clinical Trial: 2015-0104
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