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

Machine Learning (1934)

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 (10) 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
  6. Abrisham K, Alipour K, Tarvirdizadeh B, Ghamari M. Neural network models for predicting vascular age from PPG signals: A comparative study. IET Wireless Sensor Systems 2025;15(1) View
  7. Saran Khalid M, Shahid Quraishi I, Wasim Nawaz M, Sajjad H, Yaseen H, Mehmood A, Mahboob Ur Rahman M, Abbasi Q. A low-cost PPG sensor-based empirical study on healthy aging based on changes in PPG morphology. Physiological Measurement 2025;46(2):025005 View
  8. Ali S, Niaz M, Rehman M, Mehmood A, Rahman M, Riaz K, Abbasi Q. Evaluation of a Low-Cost Single-Lead ECG Module for Vascular Ageing Prediction and Studying Smoking-Induced Changes in ECG. Circuits, Systems, and Signal Processing 2025;44(8):5852 View

Conference Proceedings

  1. Abrisham K, Alipour K, Tarvirdizadeh B, Ghamari M. 2023 11th RSI International Conference on Robotics and Mechatronics (ICRoM). Predicting Vascular Age using PPG Signals and Machine Learning Algorithms: A Non-Invasive Approach for Early Cardiovascular Risk Detection View
  2. Nuñez A, Ipar E, Armentano R, Cymberknop L. 2024 IEEE Biennial Congress of Argentina (ARGENCON). Feature Engineering Analysis for Predicting Cardiovascular Aging: An In-Silico Approach View