Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography - Marazzi, Nicholas Mattia; Guidoboni, Giovanna; Zaid, Mohamed; Sala, Lorenzo; Ahmad, Salman; Despins, Laurel; Popescu, Mihail; Skubic, Marjorie; Keller, James (Frontiers in Physiology, 2022)
Purpose: This work proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography (ECG) and ballistocardiography (BCG) 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: ECG, 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 the blood pressure measured by a cuff placed on the arm of the subject. 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 the cardiovascular parameters pertaining to ventricular function and arterial stiffness can be estimated by the EA in a consistent manner when the search is performed over 5 different BCG curves corresponding to 5 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. Conclusions: 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 the blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.