Faculty Publications
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Item Computational fluid dynamic approach to understand the effect of increasing blockage on wall shear stress and region of rupture in arteries blocked by arthesclerotic plaque(UK Simulation Society Clifton Lane Nottingham NG11 8NS, 2016) Hegde, S.S.; Deb, A.; Nagesh, S.Computational bio-mechanics is developing rapidly as a non-invasive tool to assist the medical fraternity to help in both diagnosis and prognosis of human body related issues such as injuries, cardio-vascular dysfunction, atherosclerotic plaque etc. Any system that would help either properly diagnose such problems or assist prognosis would be a boon to the doctors and medical society in general. This project is an attempt to use numerical analysis techniques; in particular, computational fluid dynamics (CFD) to solve hemodynamics related problems. The mathematical modeling of the blood flow in arteries in the presence of successive blockages has been analyzed using CFD technique. Different cases of blockages in terms of percentages have been modeled to study the effect of blockage on wall shear stress values and also the effect of increase in Reynolds number on wall shear stress values. The concept of fluid structure interaction (FSI) has been used to study the effect of increasing von Mises stress on arteries and to determine the region of rupture in arteries. The simulation results are validated using in vivo measurement data from existing literature. © 2016, UK Simulation Society. All rights reserved.Item A non-invasive approach to investigation of ventricular blood pressure using cardiac sound features(IOP Publishing Ltd, 2017) Tang, H.; Zhang, J.; Chen, H.; Mondal, A.; Park, Y.Heart sounds (HSs) are produced by the interaction of the heart valves, great vessels, and heart wall with blood flow. Previous researchers have demonstrated that blood pressure can be predicted by exploring the features of cardiac sounds. These features include the amplitude of the HSs, the ratio of the amplitude, the systolic time interval, and the spectrum of the HSs. A single feature or combinations of several features have been used for prediction of blood pressure with moderate accuracy. Experiments were conducted with three beagles under various levels of blood pressure induced by different doses of epinephrine. The HSs, blood pressure in the left ventricle and electrocardiograph signals were simultaneously recorded. A total of 31 records (18 262 cardiac beats) were collected. In this paper, 91 features in various domains are extracted and their linear correlations with the measured blood pressures are examined. These features are divided into four groups and applied individually at the input of a neural network to predict the left ventricular blood pressure (LVBP). The analysis shows that non-spectral features can track changes of the LVBP with lower standard deviation. Consequently, the non-spectral feature set gives the best prediction accuracy. The average correlation coefficient between the measured and the predicted blood pressure is 0.92 and the mean absolute error is 6.86 mmHg, even when the systolic blood pressure varies in the large range from 90 mmHg to 282 mmHg. Hence, systolic blood pressure can be accurately predicted even when using fewer HS features. This technique can be used as an alternative to real-time blood pressure monitoring and it has promising applications in home health care environments. © 2017 Institute of Physics and Engineering in Medicine.Item EFFECT of STENOSIS SEVERITY on SHEAR-INDUCED DIFFUSION of RED BLOOD CELLS in CORONARY ARTERIES(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2019) Buradi, A.; Morab, S.; Mahalingam, A.In large blood vessels, migration of red blood cells (RBCs) affects the concentration of platelets and the transport of oxygen to the arterial endothelial cells. In this work, we investigate the locations where hydrodynamic diffusion of RBCs occurs and the effects of stenosis severity on shear-induced diffusion (SID) of RBCs, concentration distribution and wall shear stress (WSS). For the first time, multiphase mixture theory approach with Phillips shear-induced diffusive flux model coupled with Quemada non-Newtonian viscosity model has been applied to numerically simulate the RBCs macroscopic behavior in four different degrees of stenosis (DOS) geometries, viz., 30%, 50%, 70% and 85%. Considering SID of RBCs, the calculated average WSS increased by 77.70% which emphasises the importance of SID in predicting hemodynamic parameters. At the stenosis throat, it was observed that 85% DOS model had the lowest concentration of RBCs near the wall and highest concentration at the center. For the stenosis models with 70% and 85% DOS, the RBC lumen wall concentration at the distal section of stenosis becomes inhomogeneous with the maximum fluctuation of 1.568%. Finally, the wall regions with low WSS and low RBC concentrations correlate well with the atherosclerosis sites observed clinically. © 2019 World Scientific Publishing Company.Item An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images(Springer, 2023) Mayya, V.; Kamath S․, S.K.; Kulkarni, U.; Surya, D.K.; Acharya, U.R.Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F1 scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup. © 2022, The Author(s).