Abstract
By virtue of its direct association with the cardiovascular (CV) functions and compatibility to unobtrusive measurement during daily activities, the limb ballistocardiogram (BCG) is receiving an increasing interest as a viable means for ultra-convenient CV health and disease monitoring. However, limited insights on its physical implications have hampered disciplined interpretation of the BCG and systematic development of the BCG-based approaches for CV health monitoring. In this study, a mathematical model that can predict the limb BCG in responses to the arterial blood pressure (BP) waves in the aorta was developed and experimentally validated. The validated mathematical model suggests that (i) the limb BCG waveform reveals the timings and amplitudes associated with the aortic BP waves; (ii) mechanical filtering exerted by the musculoskeletal properties of the body can obscure the manifestation of the arterial BP waves in the limb BCG; and (iii) the limb BCG exhibits meaningful morphological changes in response to the alterations in the CV risk predictors. The physical insights garnered by the analysis of the mathematical model may open up new opportunities toward next generation of the BCG-based CV healthcare techniques embedded with transparency, interpretability, and robustness against the external variability.
Highlights
Cardiovascular disease (CVD) is a leading cause of mortality and morbidity that produces immense health and economic impacts in the United States and globally[1]
We elucidated based on a mathematical model-based analysis that the force exerted on the body due to the blood ejected by the heart results from the arterial blood pressure (BP) gradients in the ascending and descending aorta[40], indicating that the morphology of the BCG waveform has a close association with the underlying aortic BP waveforms
Considering that (i) the parameters in the mathematical model were only minimally calibrated (i.e., except for the scale-related parameters (c5 and k5), only one parameter (i.e., c14) was calibrated), (ii) they were fixed at constant values in predicting these waves associated with all subjects, and that (iii) the subjects associated with the BP waveforms used in the model prediction and those associated with the experimental BCG waveforms were largely different, the ability of the mathematical model to predict primary waves in the scale and wrist BCG with acceptable quantitative www.nature.com/scientificreports agreement with independent experimental data appears to strongly support the validity of the mathematical model in predicting the limb BCG waveforms
Summary
Cardiovascular disease (CVD) is a leading cause of mortality and morbidity that produces immense health and economic impacts in the United States and globally[1]. It is contended that a viable solution to address these challenges is to drastically enhance the physiological understanding of the BCG, its association with the underlying CV physiology, and its variability with respect to the alterations in the instrument, posture, and location Such physical insights, if established and properly integrated with the ongoing success of the data-driven BCG-based approaches to CV health monitoring, may open up new opportunities toward generation of BCG-based CV healthcare techniques embedded with transparency, interpretability, and robustness against the external variability.
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