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The purpose of this paper is the application of the computational fluid dynamics model simulating the blood flow within the aorta of an eight-year-old patient with Coarctation of Aorta.

The numerical model, based on commercial code ANSYS Fluent, was built using the multifluid Euler–Euler approach with the interaction between the phases described by the kinetic theory of granular flow (KTGF).

A model of the blood flow in the arches of the main aorta branches has been presented. The model was built using the multifluid Euler–Euler approach with the interaction between the phases described by the KTGF. The flow and pressure patterns, as well as the volumetric concentration of the blood components, were calculated. The lumped parameter model was implemented to couple the interaction of the computational domain with the remaining portion of the vascular bed.

The multiphase model based on the Euler–Euler approach describing blood flow in the branched large vessel with a three-element Windkessel model in the coarcted geometry was not previously described in the literature.

Wrist-cuff oscillometric blood pressure monitors are very popular in the portable medical device market. However, its accuracy has always been controversial. In addition to the oscillatory pressure pulse wave, the finger photoplethysmography (PPG) can provide information on blood pressure changes. A blood pressure measurement system integrating the information of pressure pulse wave and the finger PPG may improve measurement accuracy. Additionally, a neural network can synthesize the information of different types of signals and approximate the complex nonlinear relationship between inputs and outputs. The purpose of this study is to verify the hypothesis that a wrist-cuff device using a neural network for blood pressure estimation from both the oscillatory pressure pulse wave and PPG signal may improve the accuracy.

A PPG sensor was integrated into a wrist blood pressure monitor, so the finger PPG and the oscillatory pressure wave could be detected at the same time during the measurement. After the peak detection, curves were fitted to the data of pressure pulse amplitude and PPG pulse amplitude versus time. A genetic algorithm-back propagation neural network was constructed. Parameters of the curves were inputted into the neural network, the outputs of which were the measurement values of blood pressure. Blood pressure measurements of 145 subjects were obtained using a mercury sphygmomanometer, the developed device with the neural network algorithm and an Omron HEM-6111 blood pressure monitor for comparison.

For the systolic blood pressure (SBP), the difference between the proposed device and the mercury sphygmomanometer is 0.0062 ± 2.55 mmHg (mean ± SD) and the difference between the Omron device and the mercury sphygmomanometer is 1.13 ± 9.48 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and the proposed device was 0.28 ± 2.99 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and Omron HEM-6111 was −3.37 ± 7.53 mmHg.

Although the difference in the SBP error between the proposed device and Omron HEM-6111 was not remarkable, there was a significant difference between the proposed device and Omron HEM-6111 in the diastolic blood pressure error. The developed device showed an improved performance. This study was an attempt to enhance the accuracy of wrist-cuff oscillometric blood pressure monitors by using the finger PPG and the neural network. The hardware framework constructed in this study can improve the conventional wrist oscillometric sphygmomanometer and may be used for continuous measurement of blood pressure.

Studies problems related to modelling, control and monitoring of circulatory systems with an artificial heart. First, presents a pulsatile model of cardiovascular systems. This model has a well‐grounded physiological basis and includes several reflex control systems, such as baroreceptor feedback loops, and nonlinearities of vessel compliance, which are inherent in the body. In addition, develops a model of an artificial heart. Based on these models, proposes a pulse frequency modulated (PFM) control scheme for the circulatory systems with an artificial heart. This scheme enables us to regulate the blood pressure and volume of the circulatory systems properly. To monitor the behaviour of the circulatory systems with an artificial heart an observation scheme is finally presented, with which the blood pressure and stroke volumes of the circulatory system can be estimated by using only the measurements of the driving pressure of the artificial heart.

Predictive models implemented in medical procedures can potentially bring great benefit to patients and represent a step forward in targeted treatments based on a patient’s physiological condition. It is the purpose of this paper to outline such a model.

A multi-scale 0D-3D model based on patient specific geometry combines a 0-dimensional lumped parameter model (LPM) with a 3D computational fluid dynamics (CFD) analysis coupled in time, to obtain physiologically viable flow parameters.

A comparison of physiological data gathered from literature with flow-field measurements in this model shows the viability of this method in relation to potential predictions of pathological flows repercussions and candidate treatments.

A limitation of the model is the absence of compliance in the walls in the CFD fluid domain; however, compliance of the peripheral vasculature is accounted for by the LPM. Currently, an attempt is in progress to extend this multi-scale model to account for the fluid-structure interaction of the ventricular assist device vasculature and hemodynamics.

This work reports on a predictive pulsatile flow model that can be used to investigate surgical alternatives to reduce strokes in LVADs.