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The advances in cardiovascular modelling over the past two decades have given the opportunity to create accurate three dimensional models of the coronary vasculature which, combined with advanced computational fluid dynamics algorithms can shed light to intriguing matters that concern clinicians. One of these issues is the presence of a stenosis near bifurcations in one of the major coronary vessels. In this work, we try to shed light on the aforementioned matter by creating a healthy arterial bifurcation reconstructed using the fusion of Optical Coherence Tomography and X-Ray angiography images. The healthy model was edited by adding an artificial stenosis of 50% diameter reduction into three different locations after the bifurcation, thus creating three diseased models. After performing the appropriate blood flow simulations, we observed that the location of the stenosis affects the Wall Shear Stress (WSS) distribution but it does not affect the functional significance of the stenosis itself.Cardiac biomechanical modelling is a promising new tool to be used in prognostic medicine and therapy planning for patients suffering from a variety of cardiovascular diseases and injuries. In order to have an accurate biomechanical model, personalized parameters to define loading, boundary conditions and mechanical properties are required. Achieving personalized modelling parameters often requires inverse optimization which is computationally expensive; hence techniques to reduce the multivariable complexity are in need. Presented in this paper is the fundamental blueprint to create a library of scar tissue mechanical properties to be used in modelling the healing mechanics of hearts that have suffered acute myocardial infarction. This library can be used to reduce the number of variables necessary to capture the scar tissue mechanical properties down to 1. This single parameter also carries information pertaining to staging of the scar tissue healing, predict its rate, and predict its collagen density. This information can be potentially used as valuable biomarkers to adjust existing or develop new treatment plans for patients.A system for automated annotation of selected signals from the polysomnogram (PSG) for the presence of apnoea and non-apnoea arousals is presented. Fifty nine time- and frequency-domain features were derived from the PSG for each 15 second epoch and after combining features from adjacent epochs, the feature information was processed with a bank of feed-forward neural networks that provided a probability estimate that each epoch was associated with an apnoea or non-apnoea arousal, or no-arousal. Data from the Physionet Computing in Cardiology Challenge 2018 was used to develop and test the system. Performance of the system was assessed using volume under the receiver operator characteristic surface (VUROS) as well as no-arousal specificity and arousal sensitivities. Using a bank of ten feed-forward neural networks with each network processing ±4 epochs of features and each used a single hidden layer of 20 units, the system achieved a VUROS of 0.73 with a specificity of 70%, a sensitivity of 75% for the apnoea arousals, and a sensitivity of 70% for the non-apnoea arousals.Plant gain quantifies the extent and rapidity with which arterial blood gases change following hypopneic or hyperpneic events. High plant gain, acting in concert with a highly collapsible upper airway and low arousal threshold, may contribute significantly towards increasing the severity of obstructive sleep apnea (OSA), even when controller gain is low. Elevated plant gain may be a manifestation of abnormal gas exchange resulting from ventilation-perfusion mismatch in the lungs. Using a mathematical model, we explore in this paper how ventilation-perfusion mismatch can affect plant gain, as well as the severity of OSA.In this paper, we explored the link between sleep apnoea and cardiovascular disease (CVD) using a time-series statistical measure of sleep apnoea-related oxygen desaturation. We compared the performance of a hypoxic measure derived from the polysomnogram with the Apnoea Hypopnoea Index (AHI) in predicting CVD mortality in patients of the Sleep Heart Health Study.We estimated the relative cumulative time of SpO2 below 90% (Tr90) using pulse oximetry signals from polysomnogram recordings as the hypoxic measure of desaturation patterns. Then, the survival curves for hypoxia quintiles were evaluated for the prediction of CVD mortality and were compared with the results using AHI for prediction. We also calculated the Cox hazard ratios for Tr90 and AHI. selleck compound Our results show that the Tr90 was a better predictor of CVD mortality outcomes than AHI.We present an approach to quantifying nocturnal blood pressure (BP) variations that are elicited by sleep disordered breathing (SDB). A sample-by-sample aggregation of the dynamic BP variations during normal breathing and BP oscillations prompted by apnea episodes is performed. This approach facilitates visualization and analysis of BP oscillations. Preliminary results from analysis of a full night study of 7 SDB subjects (5 Male 2 Female, 52±5.6 yrs., Body Mass Index 36.4±7.4 kg/m2, Apnea-Hypopnea Index 69.1±26.8) are presented. Aggregate trajectory and quantitative values for changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP) concomitant with obstructive apnea episodes are presented. The results show 19.4 mmHg (15.3%) surge in SBP and 9.4 mmHg (13.6%) surge in DBP compared to their respective values during normal breathing (p less then 0.05). Further, the peak of the surge in SBP and DBP occurred about 9s and 7s, respectively, post the end of apnea events. The return of SBP and DBP to baseline values displays a decaying oscillatory pattern.Sleep apnea has a very high prevalence in the general population. Sleep apnea can be the cause for cardiovascular disorders. An increased risk for suffering from hypertension, stroke, and myocardial infarction had been shown in large studies, like the Sleep Heart Health Study. Sleep related breathing disorders and sleep apnea had been diagnosed in sleep laboratories with polysomnography in the past. Today in view of the high prevalence of sleep disordered breathing, home sleep apnea testing (HSAT) has become the accepted test for the diagnosis of sleep apnea, if there are no other comorbidities, and if a high pretest probability was confirmed by a sleep physician. For home sleep apnea testing, the number of sensors needed should be reduced. Some methods use indirect means to derive features to detect sleep apnea and hypopnea events. A very well developed method is peripheral arterial tonometry (PAT). This method records the pulse wave on a finger and derives sleep and sleep apnea feature. The PAT method has been tested under many conditions. As an indirect method, it was long seen as a limitation that obstructive and central sleep apnea events could not be distinguished. A new multicenter trial was set up to develop algorithms, which could distinguish central and obstructive apnea events with sufficient accuracy.This document presents a new complete standalone system for a recognition of sleep apnea using signals from the pressure sensors placed under the mattress. The developed hardware part of the system is tuned to filter and to amplify the signal. Its software part performs more accurate signal filtering and identification of apnea events. The overall achieved accuracy of the recognition of apnea occurrence is 91%, with the average measured recognition delay of about 15 seconds, which confirms the suitability of the proposed method for future employment. The main aim of the presented approach is the support of the healthcare system with the cost-efficient tool for recognition of sleep apnea in the home environment.Usual care regarding vasopressor (VP) initiation is ill-defined. We aimed to further validate a quantitative model for usual care in the Emergency Department (ED) regarding the timing of VP initiation in sepsis. We retrospectively studied a cohort of adult critically-ill ED patients who also received antibiotics in the ED. We applied a multivariable model previously developed from another patient cohort which distinguishes between time points at which patients were or were not subsequently started on a continuous VP infusion. The model has six independently significant predictors (respiratory rate, Glasgow Coma Scale score, systolic blood pressure, SpO2, administered intravenous fluids, and elapsed time). The outcome was initiation of VP infusion, either within the ED or within 6 hours after leaving the ED. We applied the model to all time points, beginning when all model input parameters were first available for a given patient, and ending when either VP were first started, or the patient left the ED. Out of 55,963 adult ED patients during the two-year study interval, we identified 1,629 who met our inclusion criteria. The area under the receiver operating characteristic curve was 0.81 for all patients, and 0.72 for the subset with at least one hypotensive blood pressure measurement. At a model threshold with sensitivity and specificity 0.74 and 0.74, respectively, the median advance detection time was 170.5 minutes (IQR 53 - 363).Septic Shock is a critical pathological state that affects patients entering the intensive care unit (ICU). Many studies have been directed to characterize and predict the onset of the septic shock, both in ICU and in the Emergency Department employing data extracted from the Electronic Health Records. Recently, machine learning algorithms have been successfully employed to help characterize septic shock in a more objective and automatic fashion. Only a few of these studies employ information contained in the continuously recorded vital signs such as electrocardiogram and arterial blood pressure. In particular, we have devised a novel feature estimation procedure able to consider instantaneous dynamics related to cardiovascular control. This work aims at developing a short-term prediction algorithm for identifying patients experiencing septic shock among a population of 100 septic patients extracted from the MIMIC-III clinical and waveform database. Among all the results obtained from several trained machine learning models, the best performance reached an AUC on the test set equal to 0.93 (Accuracy=0.85, Sensitivity=0.89 and Specificity=0.82).Heart diseases are the leading cause of death in developed countries. Ascertaining the etiology of cardiomyopathies is still a challenge. The objective of this study was to classify cardiomyopathy patients through cardio, respiratory and vascular variability analysis, considering the vascular activity as the input and output of the baroreflex response. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM, 24 patients) and dilated (DCM, 17 patients) were analyzed. Thirty-nine elderly control subjects (CON) were used as reference. From the electrocardiographic, respiratory flow, and blood pressure signals, following temporal series were extracted beat-to-beat intervals (BBI), total respiratory cycle time series (TT), and end- systolic (SBP) and diastolic (DBP) blood pressure amplitudes, respectively. Three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices.