ArticleFrontiers in physiology2021
Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Electro-mechanical risk score for coronary artery disease risk estimation using seismocardiography.European heart journal. Digital health · 2026Article
- Clinical Readiness of Hemodynamic Estimation from Cardiac Vibration Signals: An Endpoint-Specific Critical Review.Annals of biomedical engineering · 2026Review
- Bio-Inspired Deep Learning for Parkinson's Disease Detection: A Comparative Study Based on Vocal Biomarkers and Archimedean Spiral Analysis.Biomimetics (Basel, Switzerland) · 2026Article
- Non-exercise estimation of peak oxygen uptake in patients with ischaemic heart disease and heart failure using seismocardiography.European heart journal. Digital health · 2026Article
- From video to vital signs: a new method for contactless multichannel seismocardiography.NPJ cardiovascular health · 2025Article
- Non-contact heart vibration measurement using computer vision-based seismocardiography.Scientific reports · 2023Article
- Wearables in Cardiovascular Disease.Journal of cardiovascular translational research · 2023Review
- Precordial Vibrations: A Review of Wearable Systems, Signal Processing Techniques, and Main Applications.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we present a non-invasive solution to identify patients with coronary artery disease (CAD) defined as ⩾50% stenosis in at least one coronary artery. The solution is based on the analysis of linear acceleration (seismocardiogram, SCG) and angular velocity (gyrocardiogram, GCG) of the heart recorded in the x, y, and z directional axes from an accelerometer/gyroscope sensor mounted on the sternum. The database was collected from 310 individuals through a multicenter study. The time-frequency features extracted from each SCG and GCG data channel were fed to a one-dimensional Convolutional Neural Network (1D CNN) to train six separate classifiers. The results from different classifiers were later fused to estimate the CAD risk for each participant. The predicted CAD risk was validated against related results from angiography. The SCG z and SCG y classifiers showed better performance relative to the other models (
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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.