Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

Knowledge-Based Systems

Volumn 37, Issue , 2013, Pages 274-282

  Giri, Donna ^a   Rajendra Acharya, U ^b,c   Martis, Roshan Joy ^b   Vinitha Sree, S ^d   Lim, Teik Cheng ^a   Ahamed, Thajudin ^e   Suri, Jasjit S ^f  

^a SINGAPORE UNIVERSITY OF SOCIAL SCIENCES   (Singapore)

^b DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^c UNIVERSITY OF MALAYA   (Malaysia)

^d Global Biomedical Technologies Inc   (United States)

^e Government Engineering College   (India)

^f IDAHO STATE UNIVERSITY   (United States)

Author keywords

Classifiers; Coronary Artery Disease; Discrete Wavelet Transform; Electrocardiogram; Heart rate signal; Independent Component Analysis; Linear Discriminant Analysis; Principle Component Analysis

Indexed keywords

AUTOMATED DIAGNOSIS; AUTOMATIC DETECTION; CARDIAC CELL; CARDIAC HEALTH; CARDIAC SIGNALS; CLASSIFIER COMBINATION; CORONARY ARTERY DISEASE; DATA DIMENSIONS; DWT COEFFICIENTS; ECG SIGNALS; GAUSSIAN MIXTURE MODEL; HEART RATE SIGNAL; K-NEAREST NEIGHBORS; LINEAR DISCRIMINANT ANALYSIS; PRINCIPLE COMPONENT ANALYSIS; PROBABILISTIC NEURAL NETWORKS; REDUCTION TECHNIQUES; STATE OF HEALTH;

BLOOD VESSELS; CLASSIFIERS; DATA REDUCTION; DIAGNOSIS; DISCRETE WAVELET TRANSFORMS; DISEASES; ELECTROCARDIOGRAPHY; HEART; IMAGE RETRIEVAL; INDEPENDENT COMPONENT ANALYSIS; NEURAL NETWORKS; OXYGEN SUPPLY; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84870060821     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2012.08.011     Document Type: Article

References (46)

1. U.R. Acharya, N. Kannathal, and S.M. Krishnan Comprehensive analysis of cardiac health using heart rate signals Physiol. Meas. 25 2004 1130 1151
2. U.R. Acharya, P.S. Bhat, N. Kannathal, A. Rao, and C.M. Lim Analysis of cardiac health using fractal dimension and wavelet transformation Innov. Tech. Biol. Med. 26 2005 133 139
3. U.R. Acharya, K.P. Joseph, N. Kannathal, C. Lim, and J.S. Suri Heart rate variability: a review Med. Biol. Eng. Comput. 44 2006 1031 1051
4. U.R. Acharya, M. Sankaranarayanan, J. Nayak, C. Xiang, and T. Tamura Automatic identification of cardiac health using modeling techniques: a comparative study Inform. Sci. 178 2008 4571 4582
5. U.R. Acharya, S.V. Sree, S. Chattopadhyay, W. Yu, and A.P.C. Alvin Application of recurrence quantification analysis for the automatic identification of epileptic EEG signals International Journal of Neural Systems 21 3 2011 199 211
6. S. Arafat, M. Dohrmann, and M. Skubic Classification of coronary artery disease stress ECGs using uncertainty modeling Congr. Comput. Intell. Methods Appl. 2005
7. I. Babaoglu, O. Findik, and E. Ülker A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine Exp. Syst. Appl. 37 2010 3177 3183
8. I. Babaoglu, O. Findik, and M. Bayrak Effects of principle component analysis on assessment of coronary artery diseases using support vector machine Exp. Syst. Appl. 37 2010 2182 2185
9. J.T. Bigger Jr., R.C. Steinman, L.M. Rolnitzky, J.L. Fleiss, P. Albrecht, and R.J. Cohen Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants Circulation 93 1996 2142 2151
10. TM. Information available at < http://www.biopac.com/ > (accessed 24.02.12).
11. P.H. Brubaker Coronary Artery Disease: Essentials of Prevention and Rehabilitation Programs 2002 Human Kinetics Publishers
12. CAD. Information available at < http://www.nhlbi.nih.gov/health/ health-topics/topics/cad/ > (accessed 24.02.12).
13. L. Cao, J. Chua, K.S. Chong, W.K. Lee, and Q.M. Gu A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine Neurocomputing 55 2003 321 336
14. C.K. Chua, V. Chandran, U.R. Acharya, and C.M. Lim Cardiac health diagnosis using higher order spectra and support vector machine Open Med. Inform. J. 3 2009 1 8
15. D. Cysarz, H. Bettermann, and P. van Leeuwen Entropies of short binary sequences in heart period dynamics (vol. 728) Am. J. Physiol. Heart Circ. Physiol. 278 2000 H2163 H2172
16. V. David, and A. Sanchez Advanced support vector machines and kernel methods Neurocomputing 55 2003 5 20
17. S. Dua, U.R. Acharya, P. Chowriappa, and S. Vinitha Sree Wavelet-based energy features for glaucomatous image classification IEEE Trans. Inf. Technol. Biomed. 16 2012 80 87
18. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification second ed. 2001 Wiley
19. European Heart Journal, Task Force of the European Society of Cardiology and North American Society of Pacing and electrophysiology Heart Rate Variability: Standards of measurement, physiological interpretation and clinical use 17 (1996) 354-381.
20. K. Fukunaga Introduction to Statistical Pattern Recognition second ed. 1990 Academic Press
21. J. Han, M. Kamber, and J. Pei Data Mining: Concepts and Techniques second ed. 2005 Morgan Kaufmann
22. S. Haykin Neural Networks and Learning Machines third ed. 2009 Prentice Hall
23. K.K.L. Ho, G.B. Moody, C.K. Peng, J.E. Mietus, M.G. Larson, D. Levy, and A.L. Goldberger Predicting survival in heart failure cases and controls using fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics Circulation 96 1997 842 848
24. C.W. Hsu, C.C. Chang, C.J. Lin, A Practical Guide to Support Vector Classification, Technical Report, Department of Computer Science, National Taiwan University, 2003.
25. A. Hyvärinen, and E. Oja Independent component analysis: algorithms and applications Neural Networks 13 4-5 2000 411 430
26. Jinchang, Ren, Ann Vs. Svm: Which One Performs Better in Classification of Mccs in Mammogram Imaging, Knowledge-Based Systems (in press).
27. M.V. Kamath, D.N. Ghista, E.L. Fallen, D. Fitchett, D. Miller, and R. McKelvie Heart rate variability power spectrogram as potential noninvasive signature of cardiac regulatory system response, mechanisms, and disorders Heart Vessels 3 1987 33 41
28. K. Karamanos, S. Nikolopoulos, G. Manis, A. Alexandridi, K. Hizanidis, and S. Nikolakeas Block entropy analysis of heart rate variability signals Int. J. Bifurcat. Chaos. 16 2006 2093 2101
29. M. Karimi, R. Amirfattahi, S. Sadri, S.A. Marvasti, Non-invasive detection and classification of coronary artery occlusions using wavelet analysis of heart sounds with neural networks, in: 3rd IEE International Seminar on Medical Applications of, Signal Processing, 2005, pp. 117-120.
30. W. Kim, S. Jin, Y. Park, and H. Choi A study on development of multi-parametric measure of heart rate variability diagnosing cardiovascular disease IFMBE Proc. 14 2007 3480 3483
31. M. Kobayashi, and T. Musha 1/f fluctuations of heart beat period IEEE Trans. Biomed. Eng. 29 1982 456 457
32. T.T. Laitio, H.V. Huikuri, T.H. Makikallio, J. Jalonen, E.S.H. Kentala, H. Helenius, O. Pullisaar, J. Hartiala, and H. Seheinin The breakdown of fractal heart rate dynamics predicts prolonged postoperative myocardial ischemia Anesth. Analg. 98 2004 1239 1244
33. H.G. Lee, K.Y. Noh, and K.H. Ryu Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV PAKDD Workshops 2007 2007 218 228
34. H.G. Lee, K.Y. Noh, and K.H. Ryu A data mining approach for coronary heart disease prediction using HRV features and carotid arterial wall thickness Int. Conf. Biomed. Eng. Inform. 1 2008 200 206
35. T.H. Makikallio, T. Seppanen, M. Niemela, K.E.J. Airaksinen, J.M. Koistinen, M. Tulppo, and H.V. Huikuri Abnormalities in beat to be at complexity of heart rate dynamics in patients with a previous myocardial infarction J. Am. Coll. Cardiol. 28 1996 1005 1011
36. T.H. Makikallio, T. Seppanen, K.E.J. Airaksinen, J.M. Koistinen, M.P. Tulppo, C.K. Peng, A.L. Goldberger, and H.V. Huikuri Dynamic analysis of heart rate may predict subsequent ventricular tachycardia after myocardial infarction Am. J. Cardiol. 80 1997 779 783
37. G. Manis, S. Nikolopoulos, A. Alexandridi, and C. Davos Assessment of the classification capability of prediction and approximation methods for HRV analysis Comput. Biol. Med. 37 2007 642 654
38. Ping-Feng Pai, Hsu Ming-Fu, and Wang Ming-Chieh A support vector machine-based model for detecting top management fraud Knowl.-Based Syst. 24 2011 314 321
39. C.K. Peng, S. Havlin, J.M. Hausdorf, J.E. Mietus, H.E. Stanley, and A.L. Goldberger Fractal mechanisms and heart rate dynamics J. Electrocardiol. 28 1996 59 64
40. P.B. Persson, and C.D. Wagner General principles of chaotic dynamics Cardiovasc. Res. 31 1996 332 341
41. S.M. Pincus Approximate entropy as a measure of system complexity Proc. Natl. Acad. Sci. USA 88 1991 2297 2301
42. K. Qtsuka, G.F. Cornélissen, and F. Halberg Circadian rhythmic fractal scaling of heart rate variability in health and coronary artery disease Clin. Cardiol. 20 2009 631 638
43. D. Reynolds Gaussian Mixture Models 1991 MIT Lincoln Laboratory
44. L.I. Smith, A Tutorial on Principal Components Analysis. Information. < http://www.cs.otago.ac.nz/cosc453/student-tutorials/principal-components. pdf > (accessed 24.02.12).
45. A. Subasi, and M.I. Gursoy EEG signal classification using PCA, ICA, LDA and support vector machines Exp. Syst. Appl. 37 2010 8659 8666
46. Z. Zhao, and C. Ma An intelligent system for noninvasive diagnosis of coronary artery disease with EMD-TEO and BP neural network International Workshop on Education Technology and Training and International Workshop on Geoscience and Remote Sensing 2 2008 631 635