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

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 View