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Sudarshan V.,Nanyang Technological University | Acharya U.R.,Ngee Ann Polytechnic | Acharya U.R.,University of Malaya | Ng E.Y.-K.,Nanyang Technological University | And 3 more authors.
IEEE Reviews in Biomedical Engineering | Year: 2015

Myocardial infarction (MI) or acute myocardial infarction commonly known as heart attack is one of the major causes of cardiac death worldwide. It occurs when the blood supply to the portion of the heart muscle is blocked or stopped causing death of heart muscle cells. Early detection of MI will help to prevent the infarct expansion leading to left ventricle (LV) remodeling and further damage to the cardiac muscles. Timely identification of MI and the extent of LV remodeling are crucial to reduce the time taken for further tests, and save the cost due to early treatment. Echocardiography images are widely used to assess the differential diagnosis of normal and infarcted myocardium. The reading of ultrasound images is subjective due to interobserver variability and may lead to inconclusive findings which may increase the anxiety for patients. Hence, a computer-aided diagnostic (CAD) technique which uses echocardiography images of the heart coupled with pattern recognition algorithms can accurately classify normal and infarcted myocardium images. In this review paper, we have discussed the various components that are used to develop a reliable CAD system. © 2015 IEEE. Source

Acharya U.R.,Ngee Ann Polytechnic | Tong J.,Singapore General Hospital | Subbhuraam V.S.,Nanyang Technological University | Chua C.K.,Ngee Ann Polytechnic | And 6 more authors.
Journal of Medical Systems | Year: 2012

Diabetes is a chronic disease that is characterized by an increased blood glucose level due to insulin resistance. Type 2 diabetes is common in middle aged and old people. In this work, we present a technique to analyze dynamic foot pressures images and classify them into normal, diabetes type 2 with and without neuropathy classes. Plantar pressure images were obtained using the F-Scan (Tekscan, USA) in-shoe measurement system. We used Principal Component Analysis (PCA) and extracted the eigenvalues from different regions of the foot image. The features extracted from region 1 of the foot pressure image, which were found to be clinically significant, were fed into the Fuzzy classifier (Sugeno model) for automatic classification. Our results show that the proposed method is able to identify the unknown class with an accuracy of 93.7%, sensitivity of 100%, and specificity of 83.3%. Moreover, in this work, we have proposed an integrated index using the eigenvalues to differentiate the normal subjects from diabetes with and without neuropathy subjects using just one number. This index will help the clinicians in easy and objective daily screening, and it can also be used as an adjunct tool to cross check their diagnosis. © 2011 Springer Science+Business Media, LLC. Source

Acharya U.R.,Ngee Ann Polytechnic | Faust O.,Ngee Ann Polytechnic | Sree S.V.,Nanyang Technological University | Ghista D.N.,Framingham State College | And 5 more authors.
Computer Methods in Biomechanics and Biomedical Engineering | Year: 2013

Electrocardiogram (ECG) signals are difficult to interpret, and clinicians must undertake a long training process to learn to diagnose diabetes from subtle abnormalities in these signals. To facilitate these diagnoses, we have developed a technique based on the heart rate variability signal obtained from ECG signals. This technique uses digital signal processing methods and, therefore, automates the detection of diabetes from ECG signals. In this paper, we describe the signal processing techniques that extract features from heart rate (HR) signals and present an analysis procedure that uses these features to diagnose diabetes. Through statistical analysis, we have identified the correlation dimension, Poincaré geometry properties (SD2), and recurrence plot properties (REC, DET, Lmean) as useful features. These features differentiate the HR data of diabetic patients from those of patients who do not have the illness, and have been validated by using the AdaBoost classifier with the perceptron weak learner (yielding a classification accuracy of 86%). We then developed a novel diabetic integrated index (DII) that is a combination of these nonlinear features. The DII indicates whether a particular HR signal was taken from a person with diabetes. This index aids the automatic detection of diabetes, thereby allowing a more objective assessment and freeing medical professionals for other tasks. © 2013 Copyright Taylor and Francis Group, LLC. Source

Misra D.,Boston University | Booth S.L.,Tufts University | Crosier M.D.,Framingham State College | Ordovas J.M.,Tufts University | And 2 more authors.
Journal of Rheumatology | Year: 2011

Objective. Factors associated with mineralization and osteophyte formation in osteoarthritis (OA) are incompletely understood. Genetic polymorphisms of matrix Gla protein (MGP), a mineralization inhibitor, have been associated clinically with conditions of abnormal calcification. We therefore evaluated the relationship of MGP concentrations and polymorphisms at the MGP locus to hand OA. Methods. Ours was an ancillary study to a 3-year randomized trial assessing the effect of vitamin K supplementation on vascular calcification and bone loss among community-dwelling elders. We studied participants who had serum MGP concentration measured and DNA genotyped for 3 MGP genetic polymorphisms (rs1800802, rs1800801, and rs4236), and who had hand radiographs. We evaluated the cross-sectional associations of serum MGP and the 3 MGP genetic polymorphisms, respectively, with radiographic hand OA using logistic regression with generalized estimating equations, adjusting for potential confounders. Results. Radiographic hand OA in ≥ 1 joint was present in 71% of the 376 participants (mean age 74 years, mean body mass index 28 kg/m 2, 59% women). No significant association between serum MGP concentrations and radiographic hand OA was found [adjusted OR 1.0 (ref), 1.40, 1.21, and 1.21 for quartiles 1-4, respectively]. Homozygosity of the rs1800802 minor allele was associated with 0.56 times lower prevalence of hand OA compared with having ≥ 1 major allele at this locus (95% CI 0.32-0.99, p = 0.046). Conclusion. There may be an association between hand OA and genetic polymorphism at the MGP locus that is not reflected by total MGP serum concentrations. Further studies are warranted to replicate and elucidate potential mechanisms underlying these observed associations. The Journal of Rheumatology Copyright © 2011. All rights reserved. Source

Acharya U.R.,Ngee Ann Polytechnic | Faust O.,Tianjin University | Ghista D.N.,Framingham State College | Sree S.V.,Nanyang Technological University | And 5 more authors.
Journal of Medical Imaging and Health Informatics | Year: 2013

With this paper we explore systems engineering as a systematic way of designing a cardiac health visualization system. From a biomedical perspective, the system is based on the well-known fact that Heart Rate (HR) signals indicate the activity of autonomous nervous system and therefore such signals are used to investigate the cardiac health of patients. HR signals are highly nonlinear and non-stationary in nature. Hence, we have extracted the salient features using nonlinear signal processing techniques. In this work, we have analysed 285 subjects from eight different cardiac classes. The features extracted are: Normalized bispectrum entropies (P1 and P2), Approximate Entropy (ApEn), Sample Entropy (SampEn) and Recurrence Entropy (REN). Furthermore, we propose a Cardiac Integrated Index (CII) using different entropies. This one value CII can be used to differentiate normal and abnormal cardiac classes. During the work on signal analysis we realized that proposing and testing of algorithms is done in the requirements phase of the systems design. This is an important realization, because it puts the work on algorithms in perspective to all the design steps necessary to build a physical solution for the important problem of cardiac monitoring. © 2013 American Scientific Publishers. Source

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