ArticleScientific reports2020
Prediction of vascular aging based on smartphone acquired PPG signals.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Admission photoplethysmography-based mortality prediction in hospitalized Ugandan children with suspected or confirmed infection: A feasibility study.PLOS digital health · 2026Article
- Vascular age estimation using a consumer wearable sleep tracker.PLOS digital health · 2026Article
- Static vs. Immersive: A Neuromarketing Exploratory Study of Augmented Reality on Packaging Labels.Behavioral sciences (Basel, Switzerland) · 2025Article
- WF-PPG: A Wrist-finger Dual-Channel Dataset for Studying the Impact of Contact Pressure on PPG Morphology.Scientific data · 2025Article
- Age group classification based on optical measurement of brain pulsation using machine learning.Scientific reports · 2025Article
- Predicting cardiovascular disease risk using photoplethysmography and deep learning.PLOS global public health · 2024Article
- I DARE: IULM Dataset of Affective Responses.Frontiers in human neuroscience · 2024Article
- Photoplethysmography for the Assessment of Arterial Stiffness.Sensors (Basel, Switzerland) · 2023Review
- Multispectral sensor fusion in SmartWatch for in situ continuous monitoring of human skin hydration and body sweat loss.Scientific reports · 2023Article
- Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features.Sensors (Basel, Switzerland) · 2023Article
- Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.European heart journal · 2023Article
- A calibration method for smartphone camera photophlethysmography.Frontiers in digital health · 2023Article
- Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors.Sensors (Basel, Switzerland) · 2022Article
- Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis.Sensors (Basel, Switzerland) · 2022Article
- Photoplethysmogram based vascular aging assessment using the deep convolutional neural network.Scientific reports · 2022Article
- Research on recognition and classification of pulse signal features based on EPNCC.Scientific reports · 2022Article
- Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study.JMIR medical informatics · 2022Article
- Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research.European heart journal. Digital health · 2021Article
- Age-Related Changes in the Characteristics of the Elderly Females Using the Signal Features of an Earlobe Photoplethysmogram.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) - the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking - was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.