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Northborough, MA, United States

Kabinejadian F.,University of Michigan | Su B.,National Heart Center Singapore | Ghista D.N.,University 2020 Foundation | Ismail M.,National University of Singapore | And 2 more authors.
Computer Methods in Biomechanics and Biomedical Engineering | Year: 2016

Arterio-venous grafts (AVGs), the second best option as long-term vascular access for hemodialysis, face major issues of stenosis mainly due to development of intimal hyperplasia at the venous anastomosis which is linked to unfavorable hemodynamic conditions. We have investigated computationally the utility of a coupled sequential venous anastomotic design to replace conventional end-to-side (ETS) venous anastomosis, in order to improve the hemodynamic environment and consequently enhance the patency of AVGs. Two complete vascular access models with the conventional and the proposed venous anastomosis configurations were constructed. Three-dimensional, pulsatile blood flow through the models was simulated, and wall shear stress (WSS)-based hemodynamic parameters were calculated and compared between the two models. Simulation results demonstrated that the proposed anastomotic design provides: (i) a more uniform and smooth flow at the ETS anastomosis, without flow impingement and stagnation point on the artery bed and vortex formation in the heel region of the ETS anastomosis; (ii) more uniform distribution of WSS and substantially lower WSS gradients on the venous wall; and (iii) a spare route for the blood flow to the vein, to avoid re-operation in case of stenosis. The distinctive hemodynamic advantages observed in the proposed anastomotic design can enhance the patency of AVGs. © 2016 Informa UK Limited, trading as Taylor & Francis Group Source


Liu X.,CAS Shenzhen Institutes of Advanced Technology | Gao Z.,CAS Shenzhen Institutes of Advanced Technology | Gao Z.,University of Chinese Academy of Sciences | Xiong H.,Shenzhen University | And 6 more authors.
Biomechanics and Modeling in Mechanobiology | Year: 2016

The hemodynamic alteration in the cerebral circulation caused by the geometric variations in the cerebral circulation arterial network of the circle of Wills (CoW) can lead to fatal ischemic attacks in the brain. The geometric variations due to impairment in the arterial network result in incomplete cerebral arterial structure of CoW and inadequate blood supply to the brain. Therefore, it is of great importance to understand the hemodynamics of the CoW, for efficiently and precisely evaluating the status of blood supply to the brain. In this paper, three-dimensional computational fluid dynamics of the main CoW vasculature coupled with zero-dimensional lumped parameter model boundary condition for the CoW outflow boundaries is developed for analysis of the blood flow distribution in the incomplete CoW cerebral arterial structures. The geometric models in our study cover the arterial segments from the aorta to the cerebral arteries, which can allow us to take into account the innate patient-specific resistance of the arterial trees. Numerical simulations of the governing fluid mechanics are performed to determine the CoW arterial structural hemodynamics, for illustrating the redistribution of the blood flow in CoW due to the structural variations. We have evaluated our coupling methodology in five patient-specific cases that were diagnosed with the absence of efferent vessels or impairment in the connective arteries in their CoWs. The velocity profiles calculated by our approach in the segments of the patient-specific arterial structures are found to be very close to the Doppler ultrasound measurements. The accuracy and consistency of our hemodynamic results have been improved (to (Formula presented.) %) compared to that of the pure-resistance boundary conditions (of 43.5 (Formula presented.) 28 %). Based on our grouping of the five cases according to the occurrence of unilateral occlusion in vertebral arteries, the inter-comparison has shown that (i) the flow reduction in posterior cerebral arteries is the consequence of the unilateral vertebral arterial occlusion, and (ii) the flow rate in the anterior cerebral arteries is correlated with the posterior structural variations. This study shows that our coupling approach is capable of providing comprehensive information of the hemodynamic alterations in the pathological CoW arterial structures. The information generated by our methodology can enable evaluation of both the functional and structural status of the clinically significant symptoms, for assisting the treatment decision-making. © 2016 Springer-Verlag Berlin Heidelberg Source


Rajendra Acharya U.,Ngee Ann Polytechnic | Rajendra Acharya U.,University of Malaya | Vidya K.S.,Ngee Ann Polytechnic | Ghista D.N.,University 2020 Foundation | And 3 more authors.
Knowledge-Based Systems | Year: 2015

Diabetes Mellitus (DM), a chronic lifelong condition, is characterized by increased blood sugar levels. As there is no cure for DM, the major focus lies on controlling the disease. Therefore, DM diagnosis and treatment is of great importance. The most common complications of DM include retinopathy, neuropathy, nephropathy and cardiomyopathy. Diabetes causes cardiovascular autonomic neuropathy that affects the Heart Rate Variability (HRV). Hence, in the absence of other causes, the HRV analysis can be used to diagnose diabetes. The present work aims at developing an automated system for classification of normal and diabetes classes by using the heart rate (HR) information extracted from the Electrocardiogram (ECG) signals. The spectral analysis of HRV recognizes patients with autonomic diabetic neuropathy, and gives an earlier diagnosis of impairment of the Autonomic Nervous System (ANS). Significant correlations with the impaired ANS are observed of the HRV spectral indices obtained by using the Discrete Wavelet Transform (DWT) method. Herein, in order to diagnose and detect DM automatically, we have performed DWT decomposition up to 5 levels, and extracted the energy, sample entropy, approximation entropy, kurtosis and skewness features at various detailed coefficient levels of the DWT. We have extracted relative wavelet energy and entropy features up to the 5th level of DWT coefficients extracted from HR signals. These features are ranked by using various ranking methods, namely, Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC) and entropy. The ranked features are then fed into different classifiers, that include Decision Tree (DT), K-Nearest Neighbor (KNN), Naïve Bayes (NBC) and Support Vector Machine (SVM). Our results have shown maximum diagnostic differentiation performance by using a minimum number of features. With our system, we have obtained an average accuracy of 92.02%, sensitivity of 92.59% and specificity of 91.46%, by using DT classifier with ten-fold cross validation. © 2015 Elsevier B.V. Source


Zhong L.,National Heart Center Singapore | Zhong L.,National University of Singapore | Wang Y.-J.,SIM University | Huang F.-Q.,National Heart Center Singapore | And 3 more authors.
Journal of Mechanics in Medicine and Biology | Year: 2015

This study is aimed to assess (1) Left ventricle (LV) contractile function and ventricular-arterial matching from echocardiography; (2) whether ventricular-arterial matching (VAM) is associated with N-terminal pro B-type natriuretic peptide (NT-proBNP), and stroke output in patients with heart failure. Normal subjects (n = 81) and heart failure patients (n = 80) underwent echocardiography, Doppler echocardiography and tissue Doppler imaging. Only heart failure patients underwent blood test for NT-proBNP. The LV contractility was calculated as dσ∗/dtmax = 3 × (dV/dt)max/2Vm = 3 × Vpeak × (π × D2/4)/(2Vm), and the arterial elastance was calculated as Ea = SBP × 0.9/SV, wherein Vpeak and D are peak velocity and diameter of LV outflow tract, Vm is myocardial volume, SBP is the systolic blood pressure and SV is stroke volume measured from LVOT. The VAM index was expressed as the ratio of LV contractility to arterial elastance (dσ∗/dtmax/Ea). We found that HF patients had (i) decreased dσ∗/dtmax(1.46 ± 0.73 versus 4.06 ± 1.06 s-1), (ii) increased Ea(2.90 ± 0.87 versus 1.81 ± 0.38 mmHg/mL), and (iii) attenuated ventricular-arterial matching index (0.66 ± 0.57 versus 2.38 ± 0.91 mL/mmHg·s) (all p < 0.001) compared with normal subjects. The VAM index was correlated inversely with NT-proBNP (r = -0:32, p < 0.05), but positively with the stroke volume (r = 0.85, p < 0.001). The VAM index of < 1.51 was able to clearly differentiate the failing heart from normal hearts (AUC = 0.959, Sensitivity = 0.911, Specificity = 0.905). Heart failure patients demonstrated impaired ventricular contractility, enhanced arterial stiffening, and attenuated ventricular-arterial matching index. The attenuated ventricular-arterial matching index value was associated with elevated NT-proBNP levels and lower cardiac output. © 2015 World Scientific Publishing Company. Source


Sudarshan V.K.,Nanyang Technological University | Sudarshan V.K.,Ngee Ann Polytechnic | Acharya U.R.,Ngee Ann Polytechnic | Acharya U.R.,University of Malaya | And 4 more authors.
Computers in Biology and Medicine | Year: 2016

Cross-sectional view echocardiography is an efficient non-invasive diagnostic tool for characterizing Myocardial Infarction (MI) and stages of expansion leading to heart failure. An automated computer-aided technique of cross-sectional echocardiography feature assessment can aid clinicians in early and more reliable detection of MI patients before subsequent catastrophic post-MI medical conditions. Therefore, this paper proposes a novel Myocardial Infarction Index (MII) to discriminate infarcted and normal myocardium using features extracted from apical cross-sectional views of echocardiograms. The cross-sectional view of normal and MI echocardiography images are represented as textons using Maximum Responses (MR8) filter banks. Fractal Dimension (FD), Higher-Order Statistics (HOS), Hu's moments, Gabor Transform features, Fuzzy Entropy (FEnt), Energy, Local binary Pattern (LBP), Renyi's Entropy (REnt), Shannon's Entropy (ShEnt), and Kapur's Entropy (KEnt) features are extracted from textons. These features are ranked using t-test and fuzzy Max-Relevancy and Min-Redundancy (mRMR) ranking methods. Then, combinations of highly ranked features are used in the formulation and development of an integrated MII. This calculated novel MII is used to accurately and quickly detect infarcted myocardium by using one numerical value. Also, the highly ranked features are subjected to classification using different classifiers for the characterization of normal and MI LV ultrasound images using a minimum number of features. Our current technique is able to characterize MI with an average accuracy of 94.37%, sensitivity of 91.25% and specificity of 97.50% with 8 apical four chambers view features extracted from only single frame per patient making this a more reliable and accurate classification. © 2016 Elsevier Ltd. Source

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