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

Expert Systems with Applications

Volumn 63, Issue , 2016, Pages 165-172

  Kumar, Mohit ^a   Pachori, Ram Bilas ^a   Rajendra Acharya, U ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY INDORE   (India)

^b DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

Author keywords

CAD; FAWT; HRV; LS SVM; Nonlinear features

Indexed keywords

ALGORITHMS; COMPUTER AIDED DESIGN; DIAGNOSIS; DISEASES; ENTROPY; EXPERT SYSTEMS; HEART; NEAREST NEIGHBOR SEARCH; RADIAL BASIS FUNCTION NETWORKS; SIGNAL DETECTION; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES; WAVELET TRANSFORMS;

ANALYTIC WAVELET TRANSFORM; FAWT; K NEAREST NEIGHBOURS (K-NN); LEAST SQUARES SUPPORT VECTOR MACHINES; LS-SVM; NONLINEAR FEATURES; RADIAL BASIS FUNCTION(RBF); RECEIVER OPERATING CHARACTERISTICS;

COMPUTER AIDED DIAGNOSIS;

EID: 84977641982     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2016.06.038     Document Type: Article

References (51)

1. Acharya, U.R., Bhat, P.S., Iyengar, S.S., Rao, A., Dua, S., Classification of heart rate data using artificial neural network and fuzzy equivalence relation. Pattern Recognition 36:1 (2003), 61–68.
2. Acharya, U.R., Bhat, P.S., Kannathal, N., Rao, A., Lim, C.M., Analysis of cardiac health using fractal dimension and wavelet transformation. ITBM-RBM 26:2 (2005), 133–139.
3. Acharya, U.R., Faust, O., Kadri, N.A., Suri, J.S., Yu, W., Automated identification of normal and diabetes heart rate signals using nonlinear measures. Computers in Biology and Medicine 43:10 (2013), 1523–1529.
4. Acharya, U.R., Faust, O., Sree, V., Swapna, G., Martis, R.J., Kadri, N.A., Suri, J.S., Linear and nonlinear analysis of normal and CAD-affected heart rate signals. Computer Methods and Programs in Biomedicine 113:1 (2014), 55–68.
5. Acharya, U.R., Fujita, H., Sudarshan, V.K., Bhat, S., Koh, J.E.W., Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowledge-Based Systems 88 (2015), 85–96.
6. Acharya, U.R., Kannathal, N., Krishnan, S.M., Comprehensive analysis of cardiac health using heart rate signals. Physiological Measurement 25:5 (2004), 1139–1151.
7. Acharya, U.R., Sankaranarayanan, M., Nayak, J., Xiang, C., Tamura, T., Automatic identification of cardiac health using modeling techniques: A comparative study. Information Sciences 178:23 (2008), 4571–4582.
8. Acharya, U.R., Vidya, K.S., Ghista, D.N., Lim, W.J.E., Molinari, F., Sankaranarayanan, M., Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. Knowledge-Based Systems 81 (2015), 56–64.
9. Antanavičius, K., Bastys, A., Blužas, J., Gargasas, L., Kaminskienė, S., Urbonavičienė, G., Vainoras, A., Nonlinear dynamics analysis of electrocardiograms for detection of coronary artery disease. Computer Methods and Programs in Biomedicine 92:2 (2008), 198–204.
10. Azar, A.T., El-Said, S.A., Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Computing and Applications 24:5 (2014), 1163–1177.
11. Babaoglu, I., Findik, O., Ülker, E., A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine. Expert Systems with Applications 37:4 (2010), 3177–3183.
12. Babaoǧlu, I., Findik, O., Bayrak, M., Effects of principle component analysis on assessment of coronary artery diseases using support vector machine. Expert Systems with Applications 37:3 (2010), 2182–2185.
13. Bajaj, V., Pachori, R.B., Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Transactions on Information Technology in Biomedicine 16:6 (2012), 1135–1142.
14. Bayram, I., An analytic wavelet transform with a flexible time-frequency covering. IEEE Transactions on Signal Processing 61:5 (2013), 1131–1142.
15. Bayram, I., Selesnick, I.W., Frequency-domain design of overcomplete rational delation wavelet transforms. IEEE Transactions on Signal Processing 57:8 (2009), 2957–2972.
16. Bigger, J.T., Fleiss, J.L., Steinman, R.C., Rolnitzky, L.M., Schneider, W.J., Stein, P.K., RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. Circulation 91:7 (1995), 1936–1943.
17. BIOPAC Systems Canada, Inc. (2010). Acqknowledge 4.1, BIOPAC Systems, Inc. http://www.biopac.ca/Acqknowledge_40.htm.
18. Chen, W., Wang, Z., Xie, H., Yu, W., Characterization of surface EMG signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15:2 (2007), 266–272.
19. Chua, K.C., Chandran, V., Acharya, U.R., Lim, C.M., Cardiac state diagnosis using higher order spectra of heart rate variability. Journal of Medical Engineering & Technology 32:2 (2008), 145–155.
20. Derryberry, D.R., Schou, S.B., Conover, W.J., Teaching rank-based tests by emphasizing structural similarities to corresponding parametric tests. Journal of Statistics Education 18:1 (2010), 1–19.
21. Dua, S., Du, X., Sree, S.V., V. I., T.A., Novel classification of coronary artery disease using heart rate variability analysis. Journal of Mechanics in Medicine and Biology, 12(04), 2012, 1240017.
22. Duda, R.O., Hart, P.E., Stork, D.G., Pattern classification. 2nd ed., 2000, John Willey & Sons.
23. Fujita, H., Acharya, U.R., Sudarshan, V.K., Ghista, D.N., Sree, S.V., Eugene, L.W.J., Koh, J.E.W., Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index. Applied Soft Computing 43 (2016), 510–519.
24. Giri, D., Acharya, U.R., Martis, R.J., Sree, S.V., Lim, T.-C., VI, T.A., Suri, J.S., Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowledge-Based Systems 37 (2013), 274–282.
25. Hayano, J., Sakakibara, Y., Yamada, M., Ohte, N., Fujinami, T., Yokoyama, K., Takata, K., Decreased magnitude of heart rate spectral components in coronary artery disease. Its relation to angiographic severity. Circulation 81:4 (1990), 1217–1224.
26. Ji, L., Li, P., Liu, C., Wang, X., Yang, J., Liu, C., Measuring electromechanical coupling in patients with coronary artery disease and healthy subjects. Entropy, 18(4), 2016, 153.
27. Karamanos, K., Nikolopoulos, S., Hizanidis, K., Manis, G., Alexandridi, A., Nikolakeas, S., Block entropy analysis of heart rate variability signals. International Journal of Bifurcation and Chaos 16:7 (2006), 2093–2101.
28. Karimi, M., Amirfattahi, R., Sadri, S., Marvasti, S.A., Noninvasive detection and classification of coronary artery occlusions using wavelet analysis of heart sounds with neural networks. The 3rd iee international seminar on medical applications of signal processing, 2005, 117–120.
29. Khandoker, A.H., Lai, D.T.H., Begg, R.K., Palaniswami, M., Wavelet-based feature extraction for support vector machines for screening balance impairments in the elderly. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15:4 (2007), 587–597.
30. Kohavi, R., A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th international joint conference on artificial intelligence, 1995, 1137–1143 San Francisco, CA, USA.
31. Kraskov, A., Stögbauer, H., Grassberger, P., Estimating mutual information. Physical Review E, 69(6), 2004, 066138.
32. Lee, H.G., Noh, K.Y., Ryu, K.H., Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV. Washio, T., Zhou, Z.-H., Huang, J.Z., Hu, X., Li, J., Xie, C., He, J., Zou, D., Li, K.-C., Freire, M.M., (eds.) Emerging technologies in knowledge discovery and data mining Lecture notes in computer science, Vol. 4819, 2007, Springer Berlin Heidelberg, 218–228.
33. Maheshwari, S., Pachori, R.B., Acharya, U.R., Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images. IEEE Journal of Biomedical and Health Informatics, 2016, 10.1109/JBHI.2016.2544961 In Press.
34. McKight, P.E., Najab, J., Kruskal-Wallis test. Corsini Encyclopedia of Psychology, 2010.
35. National Heart, Lung and Blood Institute (2015). What is coronary heart disease?Accessed 2015-11-25 http://www.nhlbi.nih.gov/health/health-topics/topics/cad/.
36. Pachori, R.B., Avinash, P., Shashank, K., Sharma, R., Acharya, U.R., Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals. Expert Systems with Applications 42:9 (2015), 4567–4581.
37. Pachori, R.B., Kumar, M., Avinash, P., Shashank, K., Acharya, U.R., An improved online paradigm for screening of diabetic patients using RR-interval signals. Journal of Mechanics in Medicine and Biology, 16(1), 2016, 1640003.
38. Pan, J., Tompkins, W.J., A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering 32:3 (1985), 230–236.
39. Patidar, S., Pachori, R.B., Acharya, U.R., Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowledge-Based Systems 82 (2015), 1–10.
40. Poddar, M.G., Kumar, V., Sharma, Y.P., Automated diagnosis of coronary artery diseased patients by heart rate variability analysis using linear and non-linear methods. Journal of Medical Engineering & Technology 39:6 (2015), 331–341.
41. Sharma, R., Pachori, R.B., Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Systems with Applications 42:3 (2015), 1106–1117.
42. Sharma, R., Pachori, R.B., Acharya, U.R., An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 17:8 (2015), 5218–5240.
43. Sood, S., Kumar, M., Pachori, R.B., Acharya, U.R., Application of empirical mode decomposition-based features for analysis of normal and CAD heart rate signals. Journal of Mechanics in Medicine and Biology, 16(1), 2016, 1640002.
44. Suykens, J.A.K., Vandewalle, J., Least squares support vector machine classifiers. Neural Processing Letters 9:3 (1999), 293–300.
45. Theodoridis, S., Koutroumbas, K., Feature selection. Pattern recognition, 2nd ed., 2003, Academic Press, 163–205 San Diego, USA.
46. Veselkov, K.A., Pahomov, V.I., Lindon, J.C., Volynkin, V.S., Crockford, D., Osipenko, G.S., Nicholson, J.K., A metabolic entropy approach for measurements of systemic metabolic disruptions in patho-physiological states. Journal of Proteome Research 9:7 (2010), 3537–3544.
47. Warlar, R., Eswaran, C., Integer coefficient bandpass filter for the simultaneous removal of baseline wander, 50 and 100 Hz interference from the ECG. Medical and Biological Engineering and Computing 29:3 (1991), 333–336.
48. Wong, N.D., Epidemiological studies of CHD and the evolution of preventive cardiology. Nature Reviews Cardiology 11 (2014), 276–289.
49. Zavar, M., Rahati, S., Akbarzadeh-T, M.-R., Ghasemifard, H., Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection. Expert Systems with Applications 38:9 (2011), 10751–10758.
50. Zhang, C., Li, B., Chen, B., Cao, H., Zi, Y., He, Z., Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. Mechanical Systems and Signal Processing 64–65 (2015), 162–187.
51. Zhao, Z., Ma, C., 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., Vol. 2, 2008, 631–635.