Abstract Cardiovascular diseases remain the leading cause of mortality worldwide, underscoring the need for improved diagnostic tools. Seismocardiography (SCG), a noninvasive technique that records chest surface vibrations generated by cardiac activity, holds promise for such applications. However, the mechanistic origins of SCG waveforms, particularly under varying physiological conditions, remain insufficiently understood. This study presents a finite element modeling approach to simulate SCG signals by tracking the propagation of cardiac wall motion to the chest surface. The computational model, constructed from 4D CT scans of healthy adult subjects, incorporates the lungs, ribcage, muscles, and adipose tissue. Cardiac displacement boundary conditions were extracted using the Lucas-Kanade algorithm, and elastic properties were assigned to different tissues. The simulated SCG signals in the dorsoventral direction were compared to realistic SCG recordings, showing consistency in waveform morphology. Key cardiac events, such as mitral valve closure, aortic valve opening, and closure, were identified on the modeled SCG waveforms and validated with concurrent CT images and left ventricular volume changes. A systematic sensitivity analysis was also conducted to examine how variations in tissue properties, soft tissue thickness, and boundary conditions influence SCG signal characteristics. The results highlight the critical role of personalized anatomical modeling in accurately capturing SCG features, thereby improving the potential of SCG for individualized cardiovascular monitoring and diagnosis.
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An Open-Access Database for the Evaluation of Cardio-Mechanical Signals From Patients With Valvular Heart Diseases
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.
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- Award ID(s):
- 1855394
- PAR ID:
- 10317675
- Date Published:
- Journal Name:
- Frontiers in Physiology
- Volume:
- 12
- ISSN:
- 1664-042X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Seismocardiography (SCG) has attracted significant interest for monitoring cardiac health and diagnosing cardiovascular conditions. While traditional SCG methods rely on uncomfortable chest-mounted accelerometers, recent research explores non-contact approaches, including analyzing video recordings of the chest. In this study, three computer vision-based methods including Lucas-Kanade optical flow, template tracking, and Gunnar-Farneback optical flow were evaluated for extracting SCG signals from ordinary camera-recorded chest videos. The study focused on right-to-left and head-to-foot SCG signals obtained from 13 healthy subjects during breath-hold at the end of exhalation and inhalation. Comparative analysis was performed by calculating the mean squared error (MSE) and root MSE (RMSE) between the vision-based SCG signals and the gold-standard accelerometer signals. Visual and quantitative analyses showed that the Lucas-Kanade and template tracking methods estimated vision-based SCG signals closely resembling the accelerometer data, particularly in the head-to-foot direction. The Lucas-Kanade method had MSE values ranging from 0.14 to 0.93, RMSE values from 0.38 to 0.96, average correlation values of 0.82±0.09. The template tracking method showed MSE values between 0.12 to 0.94, RMSE values from 0.35 to 0.97, and average correlation values of 0.83±0.10. In comparison, the Farneback method had higher MSE values ranging from 0.20 to 1.07, RMSE values from 0.44 to 1.03, and average correlation values of 0.76±0.11. These results suggest the effectiveness of Lucas-Kanade and template tracking methods for non-contact SCG signal extraction from chest video data.more » « less
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