This study presents a design and evaluation of a mock circulatory platform, which can reproduce blood pressure and its waveforms to provide palpation experience based on the human cardiovascular physiology. To reproduce the human cardiovascular behavior, especially the blood pressure, the proposed platform includes three major modules: heart, artery and reservoir modules. The heart module reproduces source pressure exerted on the whole system with a controlled time-profile. The artery module consists of a resistance valve to adjust the open area of the vessel and a compliance chamber adjusting the wall stiffness of the ascending aorta. The designed platform was cross validated by comparing the theory with a lumped model, i.e., the windkessel model, the measurements from the mock circulatory platform and the real human body data. As a result, the ventricular and aortic pressure waveforms measured from the designed platform were well matched with those of the actual human body. Parametric studies regarding peripheral resistance and aortic compliance were done for the detailed correlation analysis between human cardiovascular physiology and blood pressure. Since the proposed platform is based on the actual cardiovascular physiology, adjusting the structural parameters of the components can reproduce realistic blood pressure waveforms in a controllable manner. This platform is applicable to blood pressure measurement sensor calibration, palpation training, and haptic feedback.

This study presents a database of central blood pressure waveforms according to cardiovascular health conditions, to supplement the lack of clinical data in cardiovascular health research, constructed by a cardiovascular simulator. Blood pressure (BP) is the most frequently measured biomarker, and in addition to systolic and diastolic pressure, its waveform represents the various conditions of cardiovascular health. A BP waveform is formed by overlapping the forward and reflected waves, which are affected by the pulse wave velocity (PWV). The increase in vascular stiffness with aging increases PWV, and the PWV-age distribution curve is called vascular age. For cardiovascular health research, extensive data of central BP waveform is essential, but the clinical data published so far are insufficient and imbalanced in quantity and quality. This study reproduces the central BP waveform using a cardiovascular hardware simulator and artificial aortas, which mimic the physiological structure and properties of the human. The simulator can adjust cardiovascular health conditions to the same level as humans, such as heart rate of 40-100 BPM, stroke volume of 40-100 mL, and peripheral resistance of 12 steps. Also, 6 artificial aortas with vascular ages in the 20-70 were fabricated to reproduce the increase in vascular stiffness due to aging. Vascular age calculated from measured stiffness of artificial aorta and central BP waveform showed an error of less than 3 years from the clinical value. Through this, a total of 636 waveforms were created to construct a central BP waveform database according to controlled various cardiovascular health conditions.

Helmholtz resonators (HR) have been proven to have feasibility for sensor applications with good performance. However, for biosensor application, when the amount of test sample is limited, the operating frequency tends to be very high, which may bring challenges of resonance excitation and frequency measurement. In this paper, a modified HR was proposed for biosensor application at a lower frequency, by designing the neck of the HR as a type of an Archimedes spiral, which can increase the neck length as much as possible without occupying much space. The resonant frequency of the proposed HR was derived by treating it as an HR with a straight neck with the equivalent length. An analytical example was calculated and verified by acoustic finite element analysis, and the results clearly showed that the frequency decreases in comparison to that of the conventional HR. Based on the underlying theory, the measurement principle was proposed and the experiment setup was established using a 3D-printed structure. The experiment was done using test solutions with different glucose concentrations, whose results showed consistency with the analytical results and noticeable frequency increases with the glucose concentration. Prospective results of the proposed HR after miniaturization show an operating frequency around 3 kHz when the amount of test sample is 0.1 mL, which will facilitate use of the common acoustic power source and acoustic pressure sensors in the sensing system.

Background and Objective:Estimation of cardiovascular parameters is essential for the assessment of cardiovascular health. Blood pressure waveforms are usually used for an efficient and continuous cardiovascular evaluation in practical clinical settings. However, previous studies have focused mainly on the independent estimation of vascular age, stroke volume, or peripheral resistance, while joint estimation within a unified framework remains underexplored. Methods:Since cardiovascular parameters jointly influence blood pressure waveform morphology, tasks for estimating cardiovascular parameters can be regarded as related tasks. Therefore, we formulated joint cardiovascular parameter estimation as a multi-task learning problem. However, since cardiovascular parameters effect different temporal regions in the blood pressure waveform, naive multi-task learning architecture can suffer negative transfer. So, we proposed a cross-talk architecture, residual neural dimension reductor with temporal and channel attention (RNDR-TCA), which models nonlinear inter-task relations while preserving task-specific representations through residual connections. Additionally, temporal and channel attention modules enhance intra-task temporal features and inter-task features, respectively. Experimental validation was conducted using a physiologically credible cardiovascular simulator dataset, where the values of independently controlled cardiovascular parameters were treated as classes for estimation based on value classification.Results:RNDR-TCA achieved the highest average Macro F1 score of 0.836, outperforming separate single-task learning models (0.784), multi-class classifier (0.771), shared-trunk architectures (0.781-0.816), and existing cross-talk architectures (0.792-0.824).Conclusions:The proposed RNDR-TCA effectively leverages inter-task relations for joint cardiovascular parameter estimation from blood pressure waveforms. The results suggest that attention-enhanced cross-talk architectures can improve multi-parameter cardiovascular assessment using a unified deep learning framework.