Photoplethysmogram based vascular aging assessment using the deep convolutional neural network.

ABSTRACT: Arterial stiffness due to vascular aging is a major indicator during the assessment of cardiovascular risk. In this study, we propose a method for age estimation by applying deep learning to a photoplethysmogram (PPG) for the non-invasive assessment of the vascular age. The proposed deep learning-based age estimation model consists of three convolutional layers and two fully connected layers, and was developed as an explainable artificial intelligence model with Grad-Cam to explain the contribution of the PPG waveform characteristic to vascular age estimation. The deep learning model was developed using a segmented PPG by pulse from a total of 752 adults aged 20-89 years, and the performance was quantitatively evaluated using the mean absolute error, root-mean-squared-error, Pearson's corr

SUBMITTER: Shin H 

PROVIDER: S-EPMC9256729 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature