Computational fluid dynamics study of the aortic valve opening on hemodynamics characteristics

In this work, the 3D geometry of patient specific aorta was utilized to carry out CFD studies on the effect of different valve opening (45°,62.5° and fully opening) on the hemodynamic properties. The result shows that the lower valve opening induced jet flow and hampered the flow on the additional carotid arteries. Besides, the leaflets were subjected to extreme stress values having disastrous consequences. Consequently, stenosis which is characterized by weaker leaflets and low valve openings has serious impact on the well being of humans

Non-fiducial based ECG biometric authentication using one-class support vector machine

Identity recognition encounters with several problems especially in feature extraction and pattern classification. Electrocardiogram (ECG) is a quasi-periodic signal which has highly discriminative characteristics in a population for subject recognition. The personal identity verification in a random population using kernel-based binary and one-class Support Vector Machines (SVMs) has been considered by other biometric traits, but has been so far left aside for analysis of ECG signals. This paper investigates the effect of different parameters of data set size, labeling data, configuration of training and testing data sets, feature extraction, different recording sessions, and random partition methods on accuracy and error rates of these SVM classifiers. The experiments were carried out with defining a number of scenarios on ECG data sets designed rely on feature extractors which were modeled based on an autocorrelation in conjunction with linear and nonlinear dimension reduction methods. The experimental results show that Kernel Principal Component Analysis has lower error rate in binary and one-class SVMs on random unknown ECG data sets. Moreover, one-class SVM can be robust recognition algorithm for ECG biometric verification if the sufficient number of biometric samples is available

Multiclass support vector machines for classification of ECG data with missing values

The article presents an experimental study on multiclass Support Vector Machine (SVM) methods over a cardiac arrhythmia dataset that has missing attribute values for electrocardiogram (ECG) diagnostic application. The presence of an incomplete dataset and high data dimensionality can affect the performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values

Numerical analysis using a fixed grid method for cardiovascular flow application

Motivated by the current interest in the numerical simulation of biological flows in the human body, we develop a new method to simulate fluid flow embedded in a solid region. The novelty of this method lies on the use of a fixed grid in the entire computational domain. The formulation is an extension of the multiphase fluid flow that belongs to the category of the penalty method, where high viscosity is imposed on a solid region. A free open source library, namely, OpenFOAM, is used to integrate high order and advanced numerical schemes into these computational formulations. The Monotone Upstream System for Conservation Laws (MUSCL) scheme by van Leer, with a harmonic limiter from the category of the total variation bounded (TVB) scheme, is used for cell face interpolation. The robustness and accuracy of the solver are compared with the benchmark test case, namely, the free fall of a solid sphere. The test case validates that the rigidity of the solid sphere is ensured with the selected high viscosity ratio. The accurate terminal velocity of the falling solid sphere proves the no-slip condition at the solid-liquid interface. As a real application implementation, the flow on a simplified idealized model of heart valve stenosis is presented

ECG biometric authentication based on non-fiducial approach using kernel methods

Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications. Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic. At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). The effects of different dimensionality reduction techniques for use in feature extraction were investigated to evaluate verification performance rates of a multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach. The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA)

Review of numerical methods for simulation of mechanical heart valves and the potential for blood clotting

Even though the mechanical heart valve (MHV) has been used routinely in clinical practice for over 60 years, the occurrence of serious complications such as blood clotting remains to be elucidated. This paper reviews the progress that has been made over the years in terms of numerical simulation method and the contribution of abnormal flow toward blood clotting from MHVs in the aortic position. It is believed that this review would likely be of interest to some readers in various disciplines, such as engineers, scientists, mathematicians and surgeons, to understand the phenomenon of blood clotting in MHVs through computational fluid dynamics

Computational fluid dynamics study of blood flow in aorta using OpenFOAM

Understanding of flow pattern behaviour inside the aorta contributes significantly in diseases treatment artificial design. Objective of present study is to simulate the blood flow in patient specific aorta using open source computational fluid dynamics (CFD) platform OpenFOAM. The real geometry was obtained from real male Malaysian patient. There are not much data available in literature incorporate real geometry of aorta due to complex geometry. The validation is done against existing experimental result of the 90 degree curve tube model. It was shown that our method is able to capture complex flow in the curve tube like secondary and separation flow that responsible for development of wall shear stress at the tube wall. These flow physics could have similarity in aorta blood flow. Finally, we apply our method with anatomy human aorta with pulsatile inlet condition. Further comparison is made with unstructured boundary fitted mesh. The final result shows that the detailed flow physics can be captured in an aorta

Analysis of homocysteine metabolism enzyme gene polymorphisms in non-syndromic congenital heart disease patients among Malaysians

Congenital heart disease (CHD) mainly is caused by the incomplete development of the heart during the first 6 weeks of pregnancy. Chromosomal and genetic abnormalities in the child and high levels of homocysteine in the blood are some of the risk factors related to CHD. Several studies in various populations have been done to determine the candidate genes in the predisposition to CHD with contradictory results, but there have been no studies that had been found in Malaysian CHD patients on homocysteine gene polymorphisms. Hence, this study was conducted to determine the allelic and genotypic analysis of the polymorphisms in candidate genes of the homocysteine enzymes; Methylenetetrahydrofolate Reductase (MTHFR), Cystathionine-b-synthase (CBS), Methionine Synthase (MTR) and Methionine Synthase Reductase (MTRR) genes. Based on the inclusion and exclusion criteria, buccal or blood samples were collected from 150 Malaysian non-syndromic CHD patients and 150 samples from healthy subjects as controls with no matching of age, genders and race between cases and controls. Genomic DNA was extracted from the samples using commercially available kits and the genotyping analysis for C677T MTHFR, A1298C MTHFR, A66G MTRR, A2756G MTR and 844ins68 CBS gene polymorphisms were analyzed using PCR-RFLP analysis. There was a significant difference observed in MTHFR A1298C gene polymorphism between cases and controls (P=0.008). However, there was no significant difference was observed for MTHFR C677T, MTRR A66G, MTR A2756G and CBS 844ins68 gene polymorphism. The association of MTHFR A1298C with the development of CHD in this study emphasis the role of MTHFR gene in the pathogenesis of non-syndromic CHD in Malaysian subjects