Abstract
Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.
Highlights
Cardiovascular diseases (CVDs) are disorders of the heart and blood vessels, including heart failure, stroke and hypertension, and represent the first leading cause of death worldwide (World Health Organization, 2021)
We investigate the use of an evolutionary algorithm (EA) to obtain personalized estimates of the cardiovascular model parameters based on the comparison between model-predicted and experimentally-measured BCG curves on a specific subject
We begin by comparing the BCG curves measured experimentally with those predicted by the EA algorithm
Summary
Cardiovascular diseases (CVDs) are disorders of the heart and blood vessels, including heart failure, stroke and hypertension, and represent the first leading cause of death worldwide (World Health Organization, 2021). Cardiovascular function and oxygen delivery to the tissues depends on adequate hemoglobin stores, oxygen uptake from the lungs and cardiac output (CO) This delivery system relies on a complex interplay between the pumping action of the heart and the biomechanical properties of the vasculature (Chang et al, 2002; Vincent and De Backer, 2013). Effective cardiovascular monitoring should provide a quantitative assessment of both cardiac and vascular functions (Holcroft et al, 2006; Vincent and De Backer, 2013). Traditional monitoring techniques, such as electrocardiography, echocardiography and intravascular catheterization, focus primarily on the heart, providing information on its electrical, mechanical, and fluiddynamical functions. The acquisition of BCG signals is not invasive and does not require body contact, thereby eliminating the risk of infections and making it a viable option for both hospital and in-home monitoring
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