An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals

Classification of normal and CAD subjects is proposed using HRV signals.FAWT is used to decompose the HRV signal.K-NN entropy estimator and fuzzy entropy are used for feature extraction.Obtained classification accuracy of 100%. Coronary Artery Disease (CAD) causes maximum death among all types of heart disorders. An early detection of CAD can save many human lives. Therefore, we have developed a new technique which is capable of detecting CAD using the Heart Rate Variability (HRV) signals. These HRV signals are decomposed to sub-band signals using Flexible Analytic Wavelet Transform (FAWT). Then, two nonlinear parameters namely; K-Nearest Neighbour (K-NN) entropy estimator and Fuzzy Entropy (FzEn) are extracted from the decomposed sub-band signals. Ranking methods namely Wilcoxon, entropy, Receiver Operating Characteristic (ROC) and Bhattacharya space algorithm are implemented to optimize the performance of the designed system. The proposed methodology has shown better performance using entropy ranking technique. The Least Squares-Support Vector Machine (LS-SVM) with Morlet wavelet and Radial Basis Function (RBF) kernels obtained the highest classification accuracy of 100% for the diagnosis of CAD. The developed novel algorithm can be used to design an expert system for the diagnosis of CAD automatically using Heart Rate (HR) signals. Our system can be used in hospitals, polyclinics and community screening to aid the cardiologists in their regular diagnosis.

[1]  Jung-Wook Bang,et al.  A metabolic entropy approach for measurements of systemic metabolic disruptions in patho-physiological States. , 2010, Journal of proteome research.

[2]  U. Rajendra Acharya,et al.  Application of entropies for automated diagnosis of epilepsy using EEG signals: A review , 2015, Knowl. Based Syst..

[3]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[4]  Alfonsas Vainoras,et al.  Nonlinear dynamics analysis of electrocardiograms for detection of coronary artery disease , 2008, Comput. Methods Programs Biomed..

[5]  C. M. Lim,et al.  Cardiac state diagnosis using higher order spectra of heart rate variability , 2008, Journal of medical engineering & technology.

[6]  Wangxin Yu,et al.  Characterization of Surface EMG Signal Based on Fuzzy Entropy , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Ram Bilas Pachori,et al.  APPLICATION OF EMPIRICAL MODE DECOMPOSITION–BASED FEATURES FOR ANALYSIS OF NORMAL AND CAD HEART RATE SIGNALS , 2016 .

[8]  U. Rajendra Acharya,et al.  Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index , 2016, Appl. Soft Comput..

[9]  Saeed Rahati Quchani,et al.  Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection , 2011, Expert Syst. Appl..

[10]  C. M. Lim,et al.  Analysis of cardiac health using fractal dimension and wavelet transformation , 2005 .

[11]  U. Rajendra Acharya,et al.  Automated identification of normal and diabetes heart rate signals using nonlinear measures , 2013, Comput. Biol. Medicine.

[12]  U. Rajendra Acharya,et al.  Automatic identification of cardiac health using modeling techniques: A comparative study , 2008, Inf. Sci..

[13]  U. Rajendra Acharya,et al.  Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals , 2015, Expert Syst. Appl..

[14]  Rassoul Amirfattahi,et al.  Noninvasive detection and classification of coronary artery occlusions using wavelet analysis of heart sounds with neural networks , 2005 .

[15]  Bing Li,et al.  Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform , 2015 .

[16]  F. Enders,et al.  Calibrated Peer Review for Interpreting Linear Regression Parameters: Results from a Graduate Course , 2010 .

[17]  Ilker Bayram,et al.  An Analytic Wavelet Transform With a Flexible Time-Frequency Covering , 2013, IEEE Transactions on Signal Processing.

[18]  W. J. Conover,et al.  Teaching Rank-Based Tests by Emphasizing Structural Similarities to Corresponding Parametric Tests , 2010 .

[19]  S. Sitharama Iyengar,et al.  Classification of heart rate data using artificial neural network and fuzzy equivalence relation , 2003, Pattern Recognit..

[20]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[21]  R. Acharya U,et al.  Comprehensive analysis of cardiac health using heart rate signals , 2004, Physiological measurement.

[22]  U. Rajendra Acharya,et al.  Linear and nonlinear analysis of normal and CAD-affected heart rate signals , 2014, Comput. Methods Programs Biomed..

[23]  Oguz Findik,et al.  Effects of principle component analysis on assessment of coronary artery diseases using support vector machine , 2010, Expert Syst. Appl..

[24]  Nathan D. Wong,et al.  Epidemiological studies of CHD and the evolution of preventive cardiology , 2014, Nature Reviews Cardiology.

[25]  Ahmad Taher Azar,et al.  Performance analysis of support vector machines classifiers in breast cancer mammography recognition , 2013, Neural Computing and Applications.

[26]  Changchun Liu,et al.  Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects , 2016, Entropy.

[27]  U. Rajendra Acharya,et al.  Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals , 2015, Knowl. Based Syst..

[28]  Sumeet Dua,et al.  NOVEL CLASSIFICATION OF CORONARY ARTERY DISEASE USING HEART RATE VARIABILITY ANALYSIS , 2012 .

[29]  U. Rajendra Acharya,et al.  Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method , 2015, Knowl. Based Syst..

[30]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[31]  U. Rajendra Acharya,et al.  An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures , 2015, Entropy.

[32]  Ram Bilas Pachori,et al.  AN IMPROVED ONLINE PARADIGM FOR SCREENING OF DIABETIC PATIENTS USING RR-INTERVAL SIGNALS , 2016 .

[33]  J. Fleiss,et al.  RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. , 1995, Circulation.

[34]  R. Warlar,et al.  Integer coefficient bandpass filter for the simultaneous removal of baseline wander, 50 and 100 Hz interference from the ECG , 1991, Medical and Biological Engineering and Computing.

[35]  Kostas Karamanos,et al.  Block Entropy Analysis of Heart Rate Variability Signals , 2006, Int. J. Bifurc. Chaos.

[36]  Ivan W. Selesnick,et al.  Frequency-Domain Design of Overcomplete Rational-Dilation Wavelet Transforms , 2009, IEEE Transactions on Signal Processing.

[37]  M. G. Poddar,et al.  Automated diagnosis of coronary artery diseased patients by heart rate variability analysis using linear and non-linear methods , 2015, Journal of medical engineering & technology.

[38]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[39]  A.H. Khandoker,et al.  Wavelet-Based Feature Extraction for Support Vector Machines for Screening Balance Impairments in the Elderly , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[40]  David G. Stork,et al.  Pattern Classification , 1973 .

[41]  Patrick E. McKight,et al.  Kruskal-Wallis Test , 2010 .

[42]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  J. Hayano,et al.  Decreased magnitude of heart rate spectral components in coronary artery disease. Its relation to angiographic severity. , 1990, Circulation.

[44]  Rajeev Sharma,et al.  Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions , 2015, Expert Syst. Appl..

[45]  Oguz Findik,et al.  A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine , 2010, Expert Syst. Appl..

[46]  Ram Bilas Pachori,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012, IEEE Transactions on Information Technology in Biomedicine.

[47]  Zhidong Zhao,et al.  An Intelligent System for Noninvasive Diagnosis of Coronary Artery Disease with EMD-TEO and BP Neural Network , 2008, 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing.

[48]  Keun Ho Ryu,et al.  Mining Biosignal Data: Coronary Artery Disease Diagnosis Using Linear and Nonlinear Features of HRV , 2007, PAKDD Workshops.

[49]  U. Rajendra Acharya,et al.  Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform , 2013, Knowl. Based Syst..