Key result
Combining evolutionary algorithms with physiology-based modeling of ECG and BCG signals successfully estimated personalized cardiovascular parameters and accurately predicted blood pressure.
Why the study?
The study aimed to propose and evaluate a novel method for personalized, noninvasive cardiovascular parameter estimation by combining electrocardiography, ballistocardiography, physiology-based modeling, and evolutionary algorithms.
Does personalized cardiovascular assessment combining electrocardiography, ballistocardiography, physiology-based modeling, and evolutionary algorithms provide accurate estimates of blood pressure in healthy subjects?
Does personalized cardiovascular assessment combining electrocardiography, ballistocardiography, physiology-based modeling, and evolutionary algorithms provide accurate estimates of blood pressure in healthy subjects?
Combining evolutionary algorithms with physiology-based modeling of ECG and BCG signals provides accurate personalized estimates of blood pressure, demonstrating potential for noninvasive cardiovascular monitoring.
May support noninvasive personalized BP monitoring; leaves open validation in clinical populations.
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.
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Marazzi et al. (2022) studied Healthy subjects (n=3). Physiology-based modeling and evolutionary algorithms using ECG and BCG vs. Cuff-based blood pressure measurement was evaluated on Match between model-predicted and experimentally-measured BCG curves and blood pressure. Combining evolutionary algorithms with physiology-based modeling of ECG and BCG signals successfully estimated personalized cardiovascular parameters and accurately predicted blood pressure.
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