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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Multiscale Computational Engineering
Импакт фактор: 1.016 5-летний Импакт фактор: 1.194 SJR: 0.554 SNIP: 0.68 CiteScore™: 1.18

ISSN Печать: 1543-1649
ISSN Онлайн: 1940-4352

Выпуски:
Том 17, 2019 Том 16, 2018 Том 15, 2017 Том 14, 2016 Том 13, 2015 Том 12, 2014 Том 11, 2013 Том 10, 2012 Том 9, 2011 Том 8, 2010 Том 7, 2009 Том 6, 2008 Том 5, 2007 Том 4, 2006 Том 3, 2005 Том 2, 2004 Том 1, 2003

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.2018026587
pages 465-486

CLASSIFICATION OF CARDIAC HEART DISEASE USING REDUCED CHAOS FEATURES AND 1-NORM LINEAR PROGRAMMING EXTREME LEARNING MACHINE

Ram Sewak Singh
Department of Electronics and Communication Engineering, IMS Engineering College, Ghaziabad (UP), 201009, India
Barjinder Singh Saini
Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar (Punjab), 144011, India
Ramesh Kumar Sunkaria
Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar (Punjab), 144011, India

Краткое описание

Objective: This article presents a novel method for binary classification of cardiac heart disease using generalized discriminant analysis (GDA) and the 1-norm linear programming extreme learning machine (1-NLPELM). The GDA reduces the dimension of extracted features from heart rate variability (HRV) signals, and can improve the classification accuracy by selecting best discerning features. The 1-NLPELM approach leads to a spares model depiction, in which several components of optimal solution vector will become zero and thus, without scarifying the validation accuracy, the choice of a decision function can be made using a reduced number of hidden nodes. Methodology: For classification of cardiac heart disease, the HRV signals were obtained from a standard database of healthy young (YNG), healthy elderly (ELY), normal sinus rhythm (NSR), congestive heart failure (CHF), and coronary artery disease (CAD) subjects. Initially nine features were extracted from HRV time-series signals by using chaos investigation methods, such as correlation dimension (CD), detrended fluctuation analysis (DFA) as α1 and α2, approximate entropy (ApEn), the results of the Poincare plot as SD1/SD2 ratio, Hurst exponent (HE), permutation entropy (PE), improved multiscale permutation entropy (IMPE) and cumulative bi-correlation (CBC). The nine features were then reduced to one feature by the GDA estimator having radial basis function (RBF), Gaussian and polynomial nonlinear kernel. This reduced feature was applied to the 1-NLPELM classifier. Results: Numerical experiments were performed on the combined database sets as YNG-ELY, NSR-CAD, NSR-CHF, CHF-CAD, YNG-CAD, CHF-YNG, ELY-CAD, and ELY-CHF subjects. The proposed method produced 98.38% validation accuracy for the NSR-CAD dataset and 100% validation accuracy for remaining datasets. Results were compared with support vector machine (SVM), extreme learning machine (ELM), differential evolution extreme learning machine (DE-ELM), online sequential extreme learning machine (OS-ELM), linear programming extreme learning machine (LPELM), 1-NLPELM, and linear discriminant analysis (LDA) + 1-NLPELM.