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  • Open Access


    Multifactorial Disease Detection Using Regressive Multi-Array Deep Neural Classifier

    D. Venugopal1, T. Jayasankar2,*, N. Krishnaraj3, S. Venkatraman4, N. B. Prakash5, G. R. Hemalakshmi5

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 27-38, 2021, DOI:10.32604/iasc.2021.015205

    Abstract Comprehensive evaluation of common complex diseases associated with common gene mutations is currently a hot area of human genome research into causative new developments. A multi-fractal analysis of the genome is performed by placing the entire DNA sequence into smaller fragments and using the chaotic game representation and systematic methods to calculate the general dimensional spectrum of each fragment. This is a time consuming process as it uses floating point to represent large data sets and requires processing time. The proposed Regressive Multi-Array Deep Neural Classifier (RMDNC) system is implemented to reduce the computation time,… More >

  • Open Access


    Smart CardioWatch System for Patients with Cardiovascular Diseases Who Live Alone

    Raisa Nazir Ahmed Kazi1,*, Manjur Kolhar2, Faiza Rizwan2

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1237-1250, 2021, DOI:10.32604/cmc.2020.012707

    Abstract The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient’s prognosis. In this study, we propose a framework referred to as smart forecasting CardioWatch (SCW) to measure the heart-rate variation (HRV) for patients with myocardial infarction (MI) who live alone or are outside their homes. In this study, HRV is used as a vital alarming sign for patients with MI. The performance of the proposed framework is measured using machine learning and deep learning techniques, namely, support vector machine, logistic regression, and decision-tree classification techniques. The results More >

  • Open Access


    Preparing adolescents with heart problems for transition to adult care, 2009–2010 National Survey of Children with Special Health Care Needs

    Karrie F. Downing1,2, Matthew E. Oster1,3, Sherry L. Farr1

    Congenital Heart Disease, Vol.12, No.4, pp. 497-506, 2017, DOI:10.1111/chd.12476

    Abstract Objective: A substantial percentage of children with congenital heart disease (CHD) fail to transfer to adult care, resulting in increased risk of morbidity and mortality. Transition planning discussions with a provider may increase rates of transfer, yet little is known about frequency and content of these discussions. We assessed prevalence and predictors of transition-related discussions between providers and parents of children with special healthcare needs (CSHCN) and heart problems, including CHD.
    Design: Using parent-reported data on 12- to 17-year-olds from the 2009–2010 National Survey of CSHCN, we calculated adjusted prevalence ratios (aPR) for associations between demographic factors… More >

  • Open Access


    On the Image-Based Non-Invasive Diagnosis of Cardiovascular Diseases

    Peng Wu1,*, Qi Gao2, Runjie Wei3, Hongping Wang3, Lizhong Wang3

    Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 27-28, 2019, DOI:10.32604/mcb.2019.05711

    Abstract Cardiovascular diseases are the leading cause of human deaths worldwide. Traditional diagnostic tools of cardiovascular diseases are either based on 2D static medical images, or invasive, bringing troubles to both patients and doctors. Our team is committed to the development of image-based non-invasive diagnostic system for cardiovascular diseases. We have made progress mainly in the following areas: 1) 4D flow technology for heart and large blood vessels. According to MRI 4D Flow data, three-dimensional velocity fields within blood vessels were constructed. Divergence-fee smoothing (DFS) was proposed to eliminate the high frequency noise in the hemodynamic… More >

  • Open Access


    Multi-Modality Image-Based Modeling Approach for Cardiovascular Disease: Simulation, Assessment, Prediction, and Virtual Surgery

    Dalin Tang1,2,*

    Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 11-11, 2019, DOI:10.32604/mcb.2019.05170

    Abstract Medical imaging and image-based modeling have made considerable progress in recent years in cardiovascular research, such as identifying atherosclerotic plaque morphological and mechanical risk factors which may be used in developing improved patient screening strategies, and performing virtual heart surgery seeking optimal surgical procedures for best post-surgical outcome. We will report recent progress in using multi-modality image-based models to predict vulnerable plaque progression and vulnerability change. In particular, we will report our recent results using IVUS+OCT data to obtain more accurate stress/strain calculations. Inflammation and cap erosion will affect cap material properties. If OCT image… More >

  • Open Access


    Image-Based Computational Modeling for Cardiovascular Diseases with Potential Clinical Applications

    Dalin Tang1, Chun Yang2, Pedro N. del Nido3, Tal Geva3, Chun Yuan4, Tom Hatsukami5, Fei Liu4, Jie Zheng6, Pamela K. Woodard6

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.1, No.1, pp. 1-6, 2007, DOI:10.3970/icces.2007.001.001

    Abstract Image-based computational models for blood flow in the heart and diseased arteries have been developed for disease assessment and potential clinical applications. Models with fluid-structure interactions for human right ventricle (RV) remodeling surgery design, carotid and coronary atherosclerotic plaques and abdominal aortic aneurysm (AAA) were presented. Organ morphology, material properties, governing equations, proper initial and boundary conditions, controlling factors and research focuses for each model were discussed. More >

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