Cardiovascular Hemodynamic Characterization: Prospects and Challenges

Dear Colleagues,

Cardiovascular hemodynamics characterization involves the comprehensive study and analysis of the dynamic behavior of blood flow within the cardiovascular system. It aims to understand the intricate interactions between various parameters, such as the structure of the vasculature, blood pressure and properties, flow velocity, shear stress, and wall mechanics. By measuring and analyzing these parameters, researchers can gain an in-depth understanding of the functionality of and potential abnormalities in the cardiovascular system. Characterizing cardiovascular hemodynamics involves the integration of computational modeling, experimental techniques, and clinical observations. In silico models simulate blood flow patterns and interactions within the vasculature, allowing researchers and clinicians to investigate different scenarios and understand how changes in parameters or structures affect hemodynamic behavior. Experimental techniques, such as imaging modalities and flow measurement devices, provide direct observations and measurements of blood flow characteristics in both in vitro and in vivo settings. These experimental data, combined with computational models, enable a more comprehensive characterization of cardiovascular hemodynamics. This Special Issue of Bioengineering showcases the latest developments in computational and experimental modeling techniques, and through research articles and review papers, it aims to present groundbreaking research and advancements in cardiovascular hemodynamics characterization, including its prospects and challenges and encompassing a range of pathologies from healthy to diseased subjects.

Prof. Dr. Eduardo Divo
Prof. Dr. Alain Kassab
Dr. Arka Das
Dr. Ray Prather
Guest Editors

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• cardiovascular system
• biofluid mechanics
• hemodynamics
• in-silico modeling
• computational fluid dynamics
• in-vitro modeling
• flow visualization and tracking

Title: Beat-by-beat Estimation of Hemodynamic Parameters in Left Ventricle Based on PCG and PPG Using Deep Learning Model
Authors: Jiachen Mi; Tengfei Feng; Hongkai Wang; Hong Tang*
Affiliation: 1 School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China 2 Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian 116024, China
Abstract: Objective: Beat-by-beat monitoring of hemodynamic parameters in left ventricle contributes to early diagnosis and treatment of heart failure, valvular heart disease, and other cardiovascular diseases. Current accurate measurement methods for ventricular hemodynamic parameters are inconvenient to monitor hemodynamic indexes for daily life. Here, we report an attempt of estimating intraventricular hemodynamic parameters based on non-invasive PCG and PPG signals. Methods: A deep neural network is built to estimate simultaneously four hemodynamic parameters of one cardiac cycle by inputting PCG and PPG of the cardiac cycle. The model built in this study consists of residual convolutional module and bidirectional recurrent neural network module which learn the local features and context relations respectively. Five-fold cross-validation is used to evaluate the performance on dog experiment data, which contains over 12000 cardiac cycles and has a large range of parameter values generated by injecting epinephrine. Results: The average correlation coefficients between estimated values and measured values are 0.941, 0.864, 0.951 and 0.923 for left ventricular systolic blood pressure (SBP), left ventricular diastolic blood pressure (DBP), the maximum rate of left ventricular pressure rise (MRR) and the maximum of left ventricular decline (MRD) respectively. Mean absolute errors and standard deviations for SBP, DBP, MRR and MRD are 7.23 8.33 mmHg, 2.12 3.0 mmHg, 298 4.5 mmHg/s, and 172 386mmHg/s. Conclusions: The performance indicates that hemodynamic parameters can be estimated by PCG and PPG even in different occasions for a certain subject. With the rapid development of wearable devices, it has up-and-coming application for self-monitoring in home healthcare environment.