A Multi-Pathology Ballistocardiogram Dataset for Cardiac Function Monitoring and Arrhythmia Assessment
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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05287-z |
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| Summary: | 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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| ISSN: | 2052-4463 |