This paper is in the following e-collection/theme issue:

Machine Learning (1501)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33439, first published .
Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study

Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study

Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study

Authors of this article:

Junyung Park1 Author Orcid Image ;   Hangsik Shin2 Author Orcid Image
Article Authors Cited by (7) Tweetations (2) Metrics

Journals

  1. Shin H, Noh G, Choi B. Photoplethysmogram based vascular aging assessment using the deep convolutional neural network. Scientific Reports 2022;12(1) View
  2. Abushouk A, Kansara T, Abdelfattah O, Badwan O, Hariri E, Chaudhury P, Kapadia S. The Dicrotic Notch: Mechanisms, Characteristics, and Clinical Correlations. Current Cardiology Reports 2023;25(8):807 View
  3. Karimpour P, May J, Kyriacou P. Photoplethysmography for the Assessment of Arterial Stiffness. Sensors 2023;23(24):9882 View
  4. Shin H. XGBoost Regression of the Most Significant Photoplethysmogram Features for Assessing Vascular Aging. IEEE Journal of Biomedical and Health Informatics 2022;26(7):3354 View
  5. Ferizoli R, Karimpour P, May J, Kyriacou P. Arterial stiffness assessment using PPG feature extraction and significance testing in an in vitro cardiovascular system. Scientific Reports 2024;14(1) View