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Veno‐venoarterial extracorporeal membrane oxygenation is an upgraded configuration that provides well‐oxygenated blood to the systemic and pulmonary circulations. Sufficient sized cannulas, gas exchange capability, and other equipment should be selected to fulfil the desired extracorporeal supporting flow for temporary life support. Therefore, an accurate prediction model is used to screen the optimal combinations of cannula sizes and determine adequate extracorporeal flow for gas exchange. Prediction models use a substitution‐rate matrix to choose optimal combinations under hemodynamic stability, and a generalised regression neural network to estimate the desired pump speed required to maintain sufficient flow. Experimental studies are used to validate the proposed model and suggest further modifications in the base/reference for the intensive care unit. [ABSTRACT FROM AUTHOR]

Peripheral arterial disease (PAD) is highly prevalent in haemodialysis (HD) patients with type 2 diabetes. Atherosclerosis may occur in both lower and upper peripheral arteries, causing progressive dialysis access stenosis in HD patients. To assess the risk of PAD, non‐invasive bilateral photoplethysmography (PPG) can be used to obtain continuous variations in blood flow volume in in vivo examinations. The authors propose an astable multivibrator to model the peripheral circulation system and to produce PPG oscillation with time constants, duty ratio (rising time), and amplitude ratio of systolic and diastolic pressures. Then, the bilateral differences in the time constant and duty ratio are used to separate the normal condition from PAD. The machine learning decision‐making process utilises a screening method to automatically detect subjects with and without the risk of PAD. The radial‐based function is employed to parameterise the similarity and dissimilarity levels using probability values. Colour relation analysis incorporates the probability values into the perceptual colour relationships for PAD screening. The experimental results indicate that in comparison with bilateral timing parameters, degree of stenosis, and resistive index, the proposed screening method is efficient in preventing complications of PAD and is easily implemented in an embedded system. [ABSTRACT FROM AUTHOR]

In clinical examinations, narrowed vessel accesses will produce unsteady flow and cause changes in blood flow volume. These phenomena result in pressure, velocity and flow resistance increases and induce stress on vessel walls. The vessel wall vibrates and squeezes the low axial blood flow, a lumped resistor (R) representing resistance to blood flow. The compliance is a pressure against blood volume relationship, a lumped capacitor (C) representing the compliance capacity of the blood vessel. Vibration phenomena results on the elastic vessel wall because of transverse vibration pressure. Turbulent and instability flow will induce simple harmonic motions, and the rise time, amplitude and pulse duration of transverse vibrations are determined by the flow resistances and vessel compliances. Hence, an astable multivibrator as detection model employs time constant, τ = (R × C), to evaluate the flow instability and the dysfunction risk in in‐vitro arteriovenous grafts (AVGs). Experimental results show the time constants have linear regression with a positive correlation as the degree of stenosis increases. Positive pole values, s = (−1/τ), are used to validate that the AVG be unstable because the force responses will continuously grow with time because of vibration increases. For comparison with the computerised analysis and auscultation method, in‐vivo examinations of the proposed equivalent model can be further carried out for screening of AVG dysfunction. [ABSTRACT FROM AUTHOR]