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Doost S.N.,Swinburne University of Technology | Ghista D.,University 2020 Foundation | Su B.,5 Hospital Drive | Zhong L.,5 Hospital Drive | And 2 more authors.
BioMedical Engineering Online | Year: 2016

Cardiovascular disease (CVD), the leading cause of death today, incorporates a wide range of cardiovascular system malfunctions that affect heart functionality. It is believed that the hemodynamic loads exerted on the cardiovascular system, the left ventricle (LV) in particular, are the leading cause of CVD initiation and propagation. Moreover, it is believed that the diagnosis and prognosis of CVD at an early stage could reduce its high mortality and morbidity rate. Therefore, a set of robust clinical cardiovascular assessment tools has been introduced to compute the cardiovascular hemodynamics in order to provide useful insights to physicians to recognize indicators leading to CVD and also to aid the diagnosis of CVD. Recently, a combination of computational fluid dynamics (CFD) and different medical imaging tools, image-based CFD (IB-CFD), has been widely employed for cardiovascular functional assessment by providing reliable hemodynamic parameters. Even though the capability of CFD to provide reliable flow dynamics in general fluid mechanics problems has been widely demonstrated for many years, up to now, the clinical implications of the IB-CFD patient-specific LVs have not been applicable due to its limitations and complications. In this paper, we review investigations conducted to numerically simulate patient-specific human LV over the past 15 years using IB-CFD methods. Firstly, we divide different studies according to the different LV types (physiological and different pathological conditions) that have been chosen to reconstruct the geometry, and then discuss their contributions, methodologies, limitations, and findings. In this regard, we have studied CFD simulations of intraventricular flows and related cardiology insights, for (i) Physiological patient-specific LV models, (ii) Pathological heart patient-specific models, including myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy and hypoplastic left heart syndrome. Finally, we discuss the current stage of the IB-CFD LV simulations in order to mimic realistic hemodynamics of patient-specific LVs. We can conclude that heart flow simulation is on the right track for developing into a useful clinical tool for heart function assessment, by (i) incorporating most of heart structures' (such as heart valves) operations, and (ii) providing useful diagnostic indices based hemodynamic parameters, for routine adoption in clinical usage. © 2016 The Author(s).


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


PubMed | University 2020 Foundation, Swinburne University of Technology and 5 Hospital Drive
Type: Journal Article | Journal: Biomedical engineering online | Year: 2016

Cardiovascular disease (CVD), the leading cause of death today, incorporates a wide range of cardiovascular system malfunctions that affect heart functionality. It is believed that the hemodynamic loads exerted on the cardiovascular system, the left ventricle (LV) in particular, are the leading cause of CVD initiation and propagation. Moreover, it is believed that the diagnosis and prognosis of CVD at an early stage could reduce its high mortality and morbidity rate. Therefore, a set of robust clinical cardiovascular assessment tools has been introduced to compute the cardiovascular hemodynamics in order to provide useful insights to physicians to recognize indicators leading to CVD and also to aid the diagnosis of CVD. Recently, a combination of computational fluid dynamics (CFD) and different medical imaging tools, image-based CFD (IB-CFD), has been widely employed for cardiovascular functional assessment by providing reliable hemodynamic parameters. Even though the capability of CFD to provide reliable flow dynamics in general fluid mechanics problems has been widely demonstrated for many years, up to now, the clinical implications of the IB-CFD patient-specific LVs have not been applicable due to its limitations and complications. In this paper, we review investigations conducted to numerically simulate patient-specific human LV over the past 15years using IB-CFD methods. Firstly, we divide different studies according to the different LV types (physiological and different pathological conditions) that have been chosen to reconstruct the geometry, and then discuss their contributions, methodologies, limitations, and findings. In this regard, we have studied CFD simulations of intraventricular flows and related cardiology insights, for (i) Physiological patient-specific LV models, (ii) Pathological heart patient-specific models, including myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy and hypoplastic left heart syndrome. Finally, we discuss the current stage of the IB-CFD LV simulations in order to mimic realistic hemodynamics of patient-specific LVs. We can conclude that heart flow simulation is on the right track for developing into a useful clinical tool for heart function assessment, by (i) incorporating most of heart structures (such as heart valves) operations, and (ii) providing useful diagnostic indices based hemodynamic parameters, for routine adoption in clinical usage.


Fujita H.,Iwate Prefectural University | Acharya U.R.,Ngee Ann Polytechnic | Acharya U.R.,SIM University Singapore | Acharya U.R.,University of Malaya | And 5 more authors.
Applied Soft Computing Journal | Year: 2016

In our previous work, we have developed a sudden cardiac death index (SCDI) using electrocardiogram (ECG) signals that could effectively predict the occurrence of SCD four minutes before the onset. Thus, the prediction of SCD before its onset by using heart rate variability (HRV) signals is a worthwhile task for further investigation. Therefore, in this paper, a new novel methodology to automatically classify the HRV signals of normal and subjects at risk of SCD by using nonlinear techniques has been presented. In this study, we have predicted SCD by analyzing four-minutes of HRV signals (separately for each one-minute interval) prior to SCD occurrence by using nonlinear features such as Renyi entropy (REnt), fuzzy entropy (FE), Hjorth's parameters (activity, mobility and complexity), Tsallis entropy (TEnt), and energy features of discrete wavelet transform (DWT) coefficients. All the clinically significant features obtained are ranked using their t-value and fed to classifiers such as K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). In this work, we have achieved an accuracy of 97.3%, 89.4%, 89.4%, and 94.7% for prediction of SCD one, two, three, and four minutes prior to the SCD onset respectively using SVM classifier. Furthermore, we have also developed a novel SCD Index (SCDI) by using nonlinear HRV signal features to classify the normal and SCD prone HRV signals. Our proposed technique is able to identify the person at risk of developing SCD four minutes earlier, thereby providing sufficient time for the clinicians to respond with treatment in Intensive Care Units (ICU). Thus, this proposed technique can thus serve as a valuable tool for increasing the survival rate of many cardiac patients. © 2016 Elsevier B.V.


Acharya U.R.,Ngee Ann Polytechnic | Acharya U.R.,SIM University Singapore | Fujita H.,Iwate Prefectural University | Sudarshan V.K.,Ngee Ann Polytechnic | And 4 more authors.
Knowledge-Based Systems | Year: 2015

Early prediction of person at risk of Sudden Cardiac Death (SCD) with or without the onset of Ventricular Tachycardia (VT) or Ventricular Fibrillation (VF) still remains a continuing challenge to clinicians. In this work, we have presented a novel integrated index for prediction of SCD with a high level of accuracy by using electrocardiogram (ECG) signals. To achieve this, nonlinear features (Fractal Dimension (FD), Hurst's exponent (H), Detrended Fluctuation Analysis (DFA), Approximate Entropy (ApproxEnt), Sample Entropy (SampEnt), and Correlation Dimension (CD)) are first extracted from the second level Discrete Wavelet Transform (DWT) decomposed ECG signal. The extracted nonlinear features are ranked using t-value and then, a combination of highly ranked features are used in the formulation and employment of an integrated Sudden Cardiac Death Index (SCDI). This calculated novel SCDI can be used to accurately predict SCD (four minutes before the occurrence) by using just one numerical value four minutes before the SCD episode. Also, the nonlinear features are fed to the following classifiers: Decision Tree (DT), k-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The combination of DWT and nonlinear analysis of ECG signals is able to predict SCD with an accuracy of 92.11% (KNN), 98.68% (SVM), 93.42% (KNN) and 92.11% (SVM) for first, second, third and fourth minutes before the occurrence of SCD, respectively. The proposed SCDI will constitute a valuable tool for the medical professionals to enable them in SCD prediction. © 2015 Elsevier B.V. All rights reserved.


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


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.


PubMed | National Heart Center, University 2020 Foundation, Nanyang Technological University and Ngee Ann Polytechnic
Type: | Journal: Computers in biology and medicine | Year: 2016

Early expansion of infarcted zone after Acute Myocardial Infarction (AMI) has serious short and long-term consequences and contributes to increased mortality. Thus, identification of moderate and severe phases of AMI before leading to other catastrophic post-MI medical condition is most important for aggressive treatment and management. Advanced image processing techniques together with robust classifier using two-dimensional (2D) echocardiograms may aid for automated classification of the extent of infarcted myocardium. Therefore, this paper proposes novel algorithms namely Curvelet Transform (CT) and Local Configuration Pattern (LCP) for an automated detection of normal, moderately infarcted and severely infarcted myocardium using 2D echocardiograms. The methodology extracts the LCP features from CT coefficients of echocardiograms. The obtained features are subjected to Marginal Fisher Analysis (MFA) dimensionality reduction technique followed by fuzzy entropy based ranking method. Different classifiers are used to differentiate ranked features into three classes normal, moderate and severely infarcted based on the extent of damage to myocardium. The developed algorithm has achieved an accuracy of 98.99%, sensitivity of 98.48% and specificity of 100% for Support Vector Machine (SVM) classifier using only six features. Furthermore, we have developed an integrated index called Myocardial Infarction Risk Index (MIRI) to detect the normal, moderately and severely infarcted myocardium using a single number. The proposed system may aid the clinicians in faster identification and quantification of the extent of infarcted myocardium using 2D echocardiogram. This system may also aid in identifying the person at risk of developing heart failure based on the extent of infarcted myocardium.


PubMed | National Heart Center, University 2020 Foundation, Nanyang Technological University and Ngee Ann Polytechnic
Type: | Journal: 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), Hus moments, Gabor Transform features, Fuzzy Entropy (FEnt), Energy, Local binary Pattern (LBP), Renyis Entropy (REnt), Shannons Entropy (ShEnt), and Kapurs 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.


PubMed | National Heart Center, University 2020 Foundation, Nanyang Technological University and Ngee Ann Polytechnic
Type: | Journal: Computers in biology and medicine | Year: 2015

Myocardial Infarction (MI) or acute MI (AMI) is one of the leading causes of death worldwide. Precise and timely identification of MI and extent of muscle damage helps in early treatment and reduction in the time taken for further tests. MI diagnosis using 2D echocardiography is prone to inter-/intra-observer variability in the assessment. Therefore, a computerised scheme based on image processing and artificial intelligent techniques can reduce the workload of clinicians and improve the diagnosis accuracy. A Computer-Aided Diagnosis (CAD) of infarcted and normal ultrasound images will be useful for clinicians. In this study, the performance of CAD approach using Discrete Wavelet Transform (DWT), second order statistics calculated from Gray-Level Co-Occurrence Matrix (GLCM) and Higher-Order Spectra (HOS) texture descriptors are compared. The proposed system is validated using 400 MI and 400 normal ultrasound images, obtained from 80 patients with MI and 80 normal subjects. The extracted features are ranked based on t-value and fed to the Support Vector Machine (SVM) classifier to obtain the best performance using minimum number of features. The features extracted from DWT coefficients obtained an accuracy of 99.5%, sensitivity of 99.75% and specificity of 99.25%; GLCM have achieved an accuracy of 85.75%, sensitivity of 90.25% and specificity of 81.25%; and HOS obtained an accuracy of 93.0%, sensitivity of 94.75% and specificity of 91.25%. Among the three techniques presented DWT yielded the highest classification accuracy. Thus, the proposed CAD approach may be used as a complementary tool to assist cardiologists in making a more accurate diagnosis for the presence of MI.

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