Characterization of Cardiovascular Diseases Using Wavelet Packet Decomposition and Nonlinear Measures of Electrocardiogram Signal

  • Hamido FujitaEmail author
  • Vidya K. Sudarshan
  • Muhammad Adam
  • Shu Lih Oh
  • Jen Hong Tan
  • Yuki Hagiwara
  • Kuang Chua Chua
  • Kok Poo Chua
  • U. Rajendra Acharya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10350)


Cardiovascular diseases (CVDs) remain as the primary causes of disability and mortality worldwide and are predicted to continue rise in the future due to inadequate preventive actions. Electrocardiogram (ECG) signal contains vital clinical information that assists significantly in the diagnosis of CVDs. Assessment of subtle ECG parameters that indicate the presence of CVDs are extremely difficult and requires long hours of manual examination for accurate diagnosis. Hence, automated computer-aided diagnosis systems might help in overcoming these limitations. In this study, a novel algorithm is proposed based on the combination of wavelet packet decomposition (WPD) and nonlinear features. The proposed method achieved classification results of 97.98% accuracy, 99.61% sensitivity and 94.84% specificity with 8 reliefF ranked features. The proposed methodology is highly efficient in helping clinical staff to detect cardiac abnormalities using a single algorithm.


Coronary artery disease Myocardial infarction Congestive heart failure Electrocardiogram Entropies Wavelet packet decomposition 



First author appreciates the support given by Japan Society for Promotion of Science (JSPS) KAKENHI Grant Number: 15K00439.


  1. 1.
    Acharya, U.R., Fujita, H., Sudarshan, V.K., Oh, S.L., Adam, M., Koh, J.E.W., Tan, J.H., Ghista, D.N., Martis, R.J., Chua, C.K., Poo, C.K., Tan, R.S.: Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl.-Based Syst. 99, 146–156 (2016a)Google Scholar
  2. 2.
    Acharya, U.R., Fujita, H., Adam, M., Lih, O.S., Sudarshan, V.K., Hong, T.J., Koh, E.W., Hagiwara, Y., Chua, C.K., Poo, C.K., San, T.R.: Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. Inf. Sci. 377, 17–29 (2016b)Google Scholar
  3. 3.
    Acharya, U.R., Kannathal, N., Krishnan, S.M.: Comprehensive analysis of cardiac health using heart rate signals. Physiol. Meas. J. 25, 1130–1151 (2004)Google Scholar
  4. 4.
    Acharya, U.R., Sudarshan, V.K., Koh, E.W., Martis, R.J., Tan, J.H., Oh, S.L., Adam, M., Hagiwara, Y., Mookiah, M.R.K., Chua, K.P., Chua, K.C., Tan, R.S.: Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals. Biomed. Sig. Process. Control 31, 31–43 (2017)CrossRefGoogle Scholar
  5. 5.
    Arafat, S., Dohrmann, M., Skubic, M.: Classification of coronary artery disease stress ECGs using uncertainty modeling, 1-4244-0020-1. IEEE (2005)Google Scholar
  6. 6.
    Arif, M., Malagore, I.A., Afsar, F.A.: Detection and localization of myocardial infarction using k-nearest neighbor classifier. J. Med. Syst. 36, 279–289 (2012)CrossRefGoogle Scholar
  7. 7.
    Babaoglu, I., Findik, O., Bayrak, M.: Effects of principle component analysis on assessment of coronary artery diseases using support vector machine. Expert Syst. Appl. 37, 2182–2185 (2010a). ElsevierGoogle Scholar
  8. 8.
    Babaoglu, I., Findik, O., Ulker, 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 Syst. Appl. 37, 3177–3183 (2010b). ElsevierGoogle Scholar
  9. 9.
    Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. Rev. Lett. 88, 174102 (2002)CrossRefGoogle Scholar
  10. 10.
    Bezerianos, A., Tong, S., Thankor, N.: Time dependent entropy of the EEG rhythm changes following brain ischemia. Ann. Biomed. Eng. 31, 221–232 (2003)CrossRefGoogle Scholar
  11. 11.
    Bui, A.L., Horwich, T.B., Fonarow, G.C.: Epidemiology and risk profile of heart failure. Nat. Rev. Cardiol. 8, 3041 (2011)CrossRefGoogle Scholar
  12. 12.
    Buja, L.M., Willerson, J.T.: The role of coronary artery lesions in ischemic heart disease: insights from recent clinicopathologic, coronary arteriographic, and experimental studies. Hum. Pathol. 18, 451–461 (1987)CrossRefGoogle Scholar
  13. 13.
    Buja, L.M., McAllister Jr., H.A.: Coronary artery disease: pathological anatomy and pathogenesis. In: Willerson, J.T., Cohn, J.N., Wellens, H.J.J., Holmes Jr., D.R. (eds.) Cardiovascular medicine, 3rd edn, pp. 593–610. Springer, London (2007)CrossRefGoogle Scholar
  14. 14.
    Chee, J., Seow, S.C.: The electrocardiogram. In: Acharya, U.R., Suri, J.S., Spaan, J.A.E., Krishnan, S.M. (eds.) Advances in Cardiac Signal Processing, pp. 1–53. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Deedwania, P.C., Carbajal, E.V.: Congestive heart failure. In: Shanahan, J., Lebowitz, H. (eds.) Current diagnosis & treatment, Cardiology, pp. 203–232. McGraw-Hill Companies, USA (2009)Google Scholar
  16. 16.
    Eckmann, J.P., Kamphorst, S.O., Ruelle, D.: Recurrence plots of dynamical systems. Europhys. Lett. 5, 973–977 (1987)CrossRefGoogle Scholar
  17. 17.
    Farmer, J.D.: Information dimension and the probabilistic structure of chaos. Naturforsch. Z. 37, 1304–1325 (1982)MathSciNetGoogle Scholar
  18. 18.
    Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, PCh., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
  19. 19.
    Guyton, A.C., Hall, J.E.: Text Book of Medical Physiology, 11th edn. Elsevier, New York (2006)Google Scholar
  20. 20.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  21. 21.
    Jayachandran, E.S., Joseph, K.P., Acharya, U.R.: Analysis of myocardial infarction using discrete wavelet transform. J. Med. Syst. 34, 985–992 (2010)CrossRefGoogle Scholar
  22. 22.
    Kamath, C.: A new approach to detect congestive heart failure using detrended fluctuation analysis of electrocardiogram signals. J. Eng. Sci. Technol. 10(2), 145–159 (2015)Google Scholar
  23. 23.
    Kaveh, A., Chung, W.: Automated classification of coronary atherosclerosis using single lead ECG. In: IEEE Conference on Wireless Sensors, Kuching, Sarawak (2013)Google Scholar
  24. 24.
    Kosko, B.: Fuzzy entropy and conditioning. Inf. Sci. 40, 165–174 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Lewenstein, K.: Radial basis function neural network approach for the diagnosis of coronary artery disease based on the standard electrocardiogram exercise test. Med. Biol. Eng. Comput. 39, 1–6 (2001)CrossRefGoogle Scholar
  26. 26.
    Liu, B., Liu, J., Wang, G., Huang, K., Li, F., Zheng, Y., Luo, Y., Zhou, F.: A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput. Biol. Med. 61, 178–184 (2015)CrossRefGoogle Scholar
  27. 27.
    Mandelbrot, B.B.: The Fractal Geometry of Nature. W.H. Freeman and Company (1982)Google Scholar
  28. 28.
    Masetic, Z., Subasi, A.: Detection of congestive heart failures using C4.5 decision tree. Southeast Eur. J. Soft Comput. 2, 74–77 (2013). ISSN 2233-1859Google Scholar
  29. 29.
    Masetic, Z., Subasi, A.: Congestive heart failure detection using random forest classifier. Comput. Methods Programs Biomed. 130, 54–64 (2016)CrossRefGoogle Scholar
  30. 30.
    Masiti, M., Masiti, Y., Oppenheim, G., Poggi, J.M.: Wavelet toolbox for use with Matlab, User’s Guide, Ver. 3. The MathWorks, Inc. (2004)Google Scholar
  31. 31.
    Marcano-Cedeno, A., Quintanilla-Dominguez, J., Cortina-Januchs, M.G., Andina, D.: Feature selection using sequential forward selection and classification applying artificial metaplasticity neural network. In: IEEE, IECON 2010, 36th Annual Conference on IEEE Industrial Electronics Society (2010)Google Scholar
  32. 32.
    Marko, R.S., Igor, K.: Theoretical and Empirical Analysis of ReliefF and RReliefF. Mach. Learn. J. 53, 23–69 (2003). doi: 10.1023/A:1025667309714 CrossRefzbMATHGoogle Scholar
  33. 33.
    Martis, R.J., Acharya, U.R., Lim, C.M.: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Sig. Process. Control 8(5), 437–448 (2013a)Google Scholar
  34. 34.
    Mendis, S., et al.: Global Status Report on Non-communicable Diseases 2014. World Health Organization (2014)Google Scholar
  35. 35.
    Mookiah, M.R.K., Acharya, U.R., Lim, C.M., Petznick, A., Suri, J.S.: Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowl. Based Syst. 33, 73–82 (2012)CrossRefGoogle Scholar
  36. 36.
    NIkias, C.I., Raghuveer, M.R.: Bispectrum estimation: a digital signal processing framework. Proc. IEEE 75, 869–891 (1987)Google Scholar
  37. 37.
    Pan, J., Tompkins, W.J.: A Real Time QRS Detection Algorithm, 11th edn. WB Saunders Co, Philadelphia (2006)Google Scholar
  38. 38.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88, 2297–2301 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Renyi, A.: On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, pp. 547–561 (1961)Google Scholar
  40. 40.
    Richman, J.S., Mooran, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, 2039–2049 (2000)Google Scholar
  41. 41.
    Rosso, O.A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schurmann, M., Basar, E.: Wavelet entropy: a new tool for analysis of short duration electrical signals. J. Neurosci. Methods 105, 65–67 (2001)CrossRefGoogle Scholar
  42. 42.
    Safdarian, N., Dabanloo, N.J., Attarodi, G.: A new pattern recognition method for detection and localization of myocardial infarction using T-wave integral and total integral as extracted features from one cycle of ECG signal. J. Biomed. Sci. Eng. 7, 818–824 (2014)CrossRefGoogle Scholar
  43. 43.
    Sharma, L.N., Tripathy, R.K., Dandapat, S.: Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans. Biomed. Eng. 62(7), 1827–1837 (2015)CrossRefGoogle Scholar
  44. 44.
    Sun, L., Lu, Y., Yang, K., Li, S.: ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans. Biomed. Eng. 59(12), 3348–3356 (2012)CrossRefGoogle Scholar
  45. 45.
    Thuraisingham, R.A.: A classification to detect congestive heart failure using second-order difference plot of RR intervals. SAGE-Hindawi access to research Cardiology Research and Practice, article id 807379 (2009)Google Scholar
  46. 46.
    Townsend, N., Wickramasinghe, K., Bhatnagar, P., Smolina, K., Nichols, M., Leal, J., Luengo-Fernandez, R., Rayner, M.: Coronary Heart Disease Statistics, a Compendium of Health Statistics, 2012th edn. British Heart Foundation, London (2012)Google Scholar
  47. 47.
    Willerson, J.T., Hillis, L.D., Buja, L.M.: Ischemic Heart Disease Clinical and Pathophysiological Aspects. Raven, New York (1982)Google Scholar
  48. 48.
    World Health Organization (WHO). Disease and injury country estimates, Geneva, Switzerland (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hamido Fujita
    • 1
    Email author
  • Vidya K. Sudarshan
    • 2
  • Muhammad Adam
    • 2
  • Shu Lih Oh
    • 2
  • Jen Hong Tan
    • 2
  • Yuki Hagiwara
    • 2
  • Kuang Chua Chua
    • 2
  • Kok Poo Chua
    • 2
  • U. Rajendra Acharya
    • 2
    • 3
    • 4
  1. 1.Faculty of Software and Information ScienceIwate Prefectural University (IPU)TakizawaJapan
  2. 2.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  3. 3.Department of Biomedical Engineering, School of Science and TechnologySIM UniversitySingaporeSingapore
  4. 4.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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