Abstract
This paper describes an open-access database for seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the United States, as well as Southeast University, Nanjing Medical University, and the first affiliated hospital of Nanjing Medical University in China. It includes electrocardiogram (ECG), SCG, and GCG recordings collected from 100 patients with various conditions of valvular heart diseases such as aortic and mitral stenosis. The recordings were collected from clinical environments with the same types of wearable sensor patch. Besides the raw recordings of ECG, SCG, and GCG signals, a set of hand-corrected fiducial point annotations is provided by manually checking the results of the annotated algorithm. The database also includes relevant echocardiogram parameters associated with each subject such as ejection fraction, valve area, and mean gradient pressure.
Highlights
Valvular heart diseases (VHDs) are one of the most common cardiovascular diseases around the globe, accounting for 20% of cardiac surgical procedures performed in the United States (Benjamin et al, 2018)
of the Database The database consists of 100 patients in total
The summary and comparison of demographic information to state-of-the-art databases are shown in Table 1
Summary
Valvular heart diseases (VHDs) are one of the most common cardiovascular diseases around the globe, accounting for 20% of cardiac surgical procedures performed in the United States (Benjamin et al, 2018). Wearable devices that record electrocardiograms (ECG), such as the conventional Holter monitor and newly-developed singlelead ECG patches are practiced for daily monitoring of cardiovascular activities to detect diseases such as arrhythmias and atrial fibrillation (Cai et al, 2020). These devices generally record the ECG signals from 24 h to 14 days while the subject is outside the clinic and is free to move. ECG-based devices are not effective in detecting VHDs
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