ArticleScientific data2025
A Multi-Pathology Ballistocardiogram Dataset for Cardiac Function Monitoring and Arrhythmia Assessment.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Clinical Readiness of Hemodynamic Estimation from Cardiac Vibration Signals: An Endpoint-Specific Critical Review.Annals of biomedical engineering · 2026Review
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Cardiac activity monitoring for the heart rate classification with enhanced BiLSTM model.Scientific reports · 2026Article
- AI-Enabled Sensor Technologies for Remote Arrhythmic Monitoring in High-Risk Cardiomyopathy Genotypes.Sensors (Basel, Switzerland) · 2026Review
- A Multi-Pathology Ballistocardiogram Dataset for Cardiac Function Monitoring and Arrhythmia Assessment.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Cardiac dysfunction plays a critical role in clinical diagnostics and treatment. Although traditional methods like echocardiography and blood biomarkers are effective, their limitations highlight the need for noninvasive and continuous monitoring solutions. Ballistocardiography (BCG), which captures subtle body vibrations generated by cardiac mechanical activity, has emerged as a promising tool for remote cardiovascular monitoring. This study presents a multi-pathology BCG dataset comprising recordings from healthy participants, patients with heart failure (HF), and those with arrhythmias such as atrial fibrillation (AF), premature ventricular contractions (PVCs), and premature atrial contractions (PACs). Synchronized electrocardiogram (ECG) and M-mode echocardiography recordings are also included, providing a comprehensive overview of cardiac function under diverse physiological and pathological conditions. The dataset aims to support the development of advanced algorithms and promote clinical validation of BCG as a tool for noninvasive cardiovascular monitoring.
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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 Socratic graph.