These variations is independent of the seizures' lengths and lateralizations.In this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals are measured non-invasively, this method guarantees an individuals comfort while not omitting important ABP information. The proposed framework predicts the ABP values by processing PPG signals with semi-classical signal analysis (SCSA) method, extracting several categories of features, which reflect the PPG signal morphology variations. Then, regression algorithms are selected for the ABP estimation. The proposed method is evaluated based on a virtual dataset with more than four thousand subjects and MIMIC II database with over eight thousand subjects for model training and testing.  https://www.selleckchem.com/products/ipi-549.html  Mean average error (MAE) and standard deviation (STD) are evaluated for different machine learning algorithms during the prediction and estimation process. Multiple linear regression (MLR) meets the AAMI standard in terms of estimation accuracy, which proves that the ABP can be accurately estimated in a nonintrusive fashion. Given the easy implementation of the ABP estimation method, we regard that the proposed features and machine learning algorithms for the cuffless estimation of the ABP can potentially provide the means for mobile healthcare equipment to monitor the ABP continuously.Pulse transit time (PTT) based continuous cuff-less blood pressure (BP) monitoring has attracted wide interests owing to its potential in improving the control and early prevention for cardiovascular diseases. However, it is still impractical in large-scale clinical application due to the concern of BP measurement accuracy. Since such approach strongly relies on the PTT-BP model under certain theoretical assumptions, the accuracy would be affected by the vessel properties alterations induced by cardiovascular disorders. Atrial fibrillation (AF) is one of the most common cardiac diseases which often coexist with hypertension. The present study sought to examine the Impact of AF on the PTT and BP， validate the capability of PTT based cuff-less methods on AF patients. By investigating the PTT and BP on 74 critically ill patients with AF, we found that parameters including PTT, R-R interval and diastolic BP (DBP) were significantly changed when AF occurs, while the systolic BP (SBP) value and photoplethysmography intensity ratio (PIR) changed little. Further, by performing two cuff-less BP estimation method, we found that the estimated accuracy is decreased on PTT based method when AF occurs, but there is little change on PIR based method. The findings demonstrated that the impact of AF on PTT is significant, which would also influence the PTT-BP relationship. But the PIR would still be a predictive factor for BP estimation for AF patients.In the recent years, Bioresorbable Vascular Scaffolds (BVS) for the treatment of atherosclerosis have been introduced. InSilc is a cloud based in silico clinical trial (ISCT) platform for drug-eluting BVS. The platform integrates multidisciplinary and multiscale models predicting the BVS performance. In this study, we present a use case scenario and demonstrate the functioning of the individual modules and of the whole pipeline and the ability to predict BVS short, medium, long-term outcomes.The ongoing advances in the field of cardiovascular modelling during the past years have allowed for the creation of accurate three-dimensional models of the major coronary arteries. The aforementioned 3D models can accurately mimic the human coronary vasculature if they are combined with sophisticated computational fluid dynamics algorithms and shed light to non-trivial issues that concern the clinicians. One of these issues is to define whether a coronary lesion is more dangerous to present with ischemia if it is at a proximal or a distal part of the vessel. In this work, we aim to investigate the aforementioned issue by reconstructing in 3D a coronary arterial model from a healthy subject using Computed Tomography Coronary Angiography images and by editing it to create eight diseased arterial models that contain one or two lesions of different severities. After carrying out the appropriate blood flow simulations using the finite element method, we observed that the distal lesions are more dangerous than the proximal ones in terms of hemodynamic significance. Moreover, the distal severe stenosis (i.e. 70% diameter reduction) present with the highest peak Wall Shear Stress (WSS) values in comparison to the proximal ones.This paper reports an interesting phenomenon that the amplitude of the QRS complex reduces during inhalation and increases during exhalation and the variation can exceed even 100% during very slow breathing rates (BR). The phenomenon has been consistent in all the nine normal male subjects we have studied with age ranging from 23 to 61 years. Further, at very low respiration rates which included breath holds both after inhalation and exhalation, there are highly significant second and third harmonics of the respiration frequency in the heart rate variability spectrum. On the other hand, the R-wave amplitude changes do not have any noticeable higher harmonics of the BR. Thus, the observed changes in the R-wave amplitude are neither connected to the movement of the heart nor changes in its relative position with respect to the recording electrodes nor the fluctuations in the stroke volume.Left ventricular assist devices (LVADs) have increasingly been used clinically to treat heart failure patients. However, hemolysis, pump thrombosis, infection and bleeding still persist as major limitations of LVAD technology. Assessing LVAD hemocompatibility using a blood shear stress device (BSSD) has clear advantages, as the BSSD could provide a better experimental platform to develop reliable, quantifiable blood trauma assays to perform iterative testing of LVAD designs. In this study, a BSSD was proposed with short blood exposure time and no seals or contact bearings to reduce blood trauma caused by the test platform. Enlarged air-gap drive motor in BSSD is essential to avoid high shear stress; however, it would significantly reduce the motor torque, which may result in inadequate force to drive the entire system. In order to evaluate and optimize the drive motor air-gap to ensure adequate motor torque as well as acceptable range for blood exposure time and shear stress, a numerical brushless DC (BLDC) motor model was established using finite element method (FEM) in numerical simulation software COMSOL.