Zhan Jing, Li Zhengying, Wu Xiaoyan, Zhang Chao, Zhao Tao, Chen Kewei, Lu Zhibing
Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
National Engineering Research Center of Optical Fiber Sensing Technology and Networks, Wuhan University of Technology, Wuhan, 430070, Hubei, China.
Sci Data. 2025 Jun 9;12(1):963. doi: 10.1038/s41597-025-05287-z.
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.
心脏功能障碍在临床诊断和治疗中起着关键作用。尽管超声心动图和血液生物标志物等传统方法是有效的,但其局限性凸显了对无创和连续监测解决方案的需求。心冲击图(BCG)可捕捉心脏机械活动产生的细微身体振动,已成为远程心血管监测的一种有前景的工具。本研究展示了一个多病理BCG数据集,包括来自健康参与者、心力衰竭(HF)患者以及患有心律失常(如心房颤动(AF)、室性早搏(PVC)和房性早搏(PAC))患者的记录。还包括同步心电图(ECG)和M型超声心动图记录,提供了在不同生理和病理条件下心脏功能的全面概述。该数据集旨在支持先进算法的开发,并促进BCG作为无创心血管监测工具的临床验证。