Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features

  • R. K. TripathyEmail author
  • S. Dandapat
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement


The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.


Cardiac abnormalities Multilead ECG Multiscale phase alternation Feature selection Classifiers Accuracy 



The authors are grateful to editor-in-chief and associate editor of journal of medical systems for encouragement and would like to thank reviewers for their valuable suggestions to for revising this manuscript.


  1. 1.
    Drezner, J. A., Ashley, E., Baggish, A. L., Börjesson, M., Corrado, D., Owens, D. S., Patel, A., Pelliccia, A., Vetter, V. L., Ackerman, M. J., et al. Abnormal electrocardiographic findings in athletes: recognising changes suggestive of cardiomyopathy. Br. J. Sports Med. 47(3):137–152, 2013.Google Scholar
  2. 2.
    Goldberger, A. L. Clinical electrocardiography: a simplified approach: Elsevier Health Sciences, 2012.Google Scholar
  3. 3.
    Thygesen, K., Alpert, J. S., Jaffe, A. S., White, H. D., Simoons, M. L., Chaitman, B. R., Katus, H. A., Apple, F. S., Lindahl, B., Morrow, D. A., et al., Third universal definition of myocardial infarction. J. Am. Coll. Cardiol. 60(16):1581–1598, 2012.Google Scholar
  4. 4.
    Sharma, L., Tripathy, R., and Dandapat, S., Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans. Biomed. Eng. 62(7):1827–1837, 2015.CrossRefPubMedGoogle Scholar
  5. 5.
    Martis, R. J., Acharya, U. R., and Adeli, H., Current methods in electrocardiogram characterization. Comput. Biol. Med. 48:133–149, 2014.CrossRefPubMedGoogle Scholar
  6. 6.
    Lin, B. -S., Wong, A. M., and Tseng, K. C., Community-based ecg monitoring system for patients with cardiovascular diseases. J. Med. Syst. 40(4):1–12, 2016.Google Scholar
  7. 7.
    Alshraideh, H., Otoom, M., Al-Araida, A., Bawaneh, H., and Bravo, J., A web based cardiovascular disease detection system. J. Med. Syst. 39(10):1–6, 2015.CrossRefGoogle Scholar
  8. 8.
    Rahman, Q. A., Tereshchenko, L. G., Kongkatong, M., Abraham, T., Abraham, M. R., and Shatkay, H., Utilizing ecg-based heartbeat classification for hypertrophic cardiomyopathy identification. IEEE Trans. NanoBioscience 14(5):505–512, 2015.CrossRefPubMedGoogle Scholar
  9. 9.
    Arif, M., Malagore, I. A., and Afsar, F. A., Detection and localization of myocardial infarction using k-nearest neighbor classifier. J. Med. Syst. 36(1):279–289, 2012.CrossRefPubMedGoogle Scholar
  10. 10.
    Lu, H., Ong, K., and Chia, P., An automated ecg classification system based on a neuro-fuzzy system. In: Computers in Cardiology 2000, pp. 387–390: IEEE (2000)Google Scholar
  11. 11.
    Sun, L., Lu, Y., Yang, K., and Li, S., Ecg analysis using multiple instance learning for myocardial infarction detection. IEEE Trans. Biomed. Eng. 59(12):3348–3356, 2012.CrossRefPubMedGoogle Scholar
  12. 12.
    Liu, B., Liu, J., Wang, G., Huang, K., Li, F., Zheng, Y., Luo, Y., and Zhou, F., A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput. Biol. Med. 61:178–184, 2015.CrossRefPubMedGoogle Scholar
  13. 13.
    Alickovic, E., and Subasi, A., Medical decision support system for diagnosis of heart arrhythmia using dwt and random forests classifier. J. Med. Syst. 40(4):1–12, 2016.CrossRefGoogle Scholar
  14. 14.
    Jayachandran, E. et al., Analysis of myocardial infarction using discrete wavelet transform. J. Med. Syst. 34(6):985–992, 2010.CrossRefPubMedGoogle Scholar
  15. 15.
    Haraldsson, H., Edenbrandt, L., and Ohlsson, M., Detecting acute myocardial infarction in the 12-lead ecg using hermite expansions and neural networks. Artif. Intell. Med. 32(2):127–136, 2004.CrossRefPubMedGoogle Scholar
  16. 16.
    Acharya, U. R., Fujita, H., Sudarshan, V. K., Oh, S. L., Adam, M., Koh, J. E., Tan, J. H., Ghista, D. N., Martis, R. J., Chua, C. K., et al., Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl.-Based Syst. 99:146–156, 2016.Google Scholar
  17. 17.
    Lahiri, T., Kumar, U., Mishra, H., Sarkar, S., and Das Roy, A., Analysis of ecg signal by chaos principle to help automatic diagnosis of myocardial infarction. J. Sci. Ind. Res. 68(10):866, 2009.Google Scholar
  18. 18.
    Safdarian, N., Dabanloo, N. J., and 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(10):818, 2014.CrossRefGoogle Scholar
  19. 19.
    Tripathy, R., Sharma, L., and Dandapat, S., A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification. Healthcare Technol. Lett. 1(4):98, 2014.CrossRefGoogle Scholar
  20. 20.
    Martis, R. J., Acharya, U. R., Mandana, K., Ray, A., and Chakraborty, C., Application of principal component analysis to {ECG} signals for automated diagnosis of cardiac health. Expert Syst. Appl. 39(14):11792–11800, 2012. [Online]. Available: Scholar
  21. 21.
    Martis, R. J., Acharya, U. R., Mandana, K., Ray, A. K., and Chakraborty, C., Cardiac decision making using higher order spectra. Biomedical Signal Process. Control 8(2):193–203, 2013.CrossRefGoogle Scholar
  22. 22.
    Huang, K., and Zhang, L., Cardiology knowledge free ecg feature extraction using generalized tensor rank one discriminant analysis. EURASIP J. Adv. Signal Process. 2014(1):1–15, 2014.CrossRefGoogle Scholar
  23. 23.
    Oppenheim, A. V., and Lim, J. S., The importance of phase in signals. Proc. IEEE 69(5):529–541, 1981.CrossRefGoogle Scholar
  24. 24.
    Thomas, M., Das, M. K., and Ari, S., Automatic ecg arrhythmia classification using dual tree complex wavelet based features. AEU-Int. J. Electron. Commun. 69(4):715–721, 2015.CrossRefGoogle Scholar
  25. 25.
    Selesnick, I. W., Baraniuk, R. G., and Kingsbury, N. G., The dual-tree complex wavelet transform. IEEE Signal Proc. Mag. 22(6):123–151, 2005.CrossRefGoogle Scholar
  26. 26.
    Rangayyan, R. M. Biomedical signal analysis. Vol. 33: Wiley, 2015.Google Scholar
  27. 27.
    Kingsbury, N., A dual-tree complex wavelet transform with improved orthogonality and symmetry properties. In: Image Processing, 2000 International Conference on Proceedings, Vol. 2, pp. 375–378: IEEE, 2000.Google Scholar
  28. 28.
    Takla, G., Loparo, K. A., and Nair, B.: System for artifact detection and elimination in an electrocardiogram signal recorded from a patient monitor. May 7 2008, uS Patent App. 12/116, 235Google Scholar
  29. 29.
    Selesnick, I. W., Hilbert transform pairs of wavelet bases. IEEE Signal Process. Lett. 8(6):170–173, 2001.CrossRefGoogle Scholar
  30. 30.
    Zhang, J., Jiang, W., Wang, R., and Wang, L., Brain mr image segmentation with spatial constrained k-mean algorithm and dual-tree complex wavelet transform. J. Med. Syst. 38(9):1–6 , 2014.CrossRefGoogle Scholar
  31. 31.
    Tripathy, R., Sharma, L., and Dandapat, S., Detection of shockable ventricular arrhythmia using variational mode decomposition. J. Med. Syst. 40(4):1–13, 2016.CrossRefGoogle Scholar
  32. 32.
    Pohjalainen, J., Räsänen, O., and Kadioglu, S., Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput. Speech Lang. 29(1): 145–171, 2015.CrossRefGoogle Scholar
  33. 33.
    Bejani, M., Gharavian, D., and Charkari, N. M., Audiovisual emotion recognition using anova feature selection method and multi-classifier neural networks. Neural Comput. & Applic. 24(2):399–412, 2014.CrossRefGoogle Scholar
  34. 34.
    Bishop, C. M., Pattern recognition. Mach. Learn., 2006.Google Scholar
  35. 35.
    Keller, J. M., Gray, M. R., and Givens, J. A., A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 4:580–585, 1985.CrossRefGoogle Scholar
  36. 36.
    Arif, M., Akram, M. U., et al., Pruned fuzzy k-nearest neighbor classifier for beat classification. J. Biomed. Sci. Eng. 3(04):380, 2010.CrossRefGoogle Scholar
  37. 37.
    Sokolova, M., and Lapalme, G., A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4):427–437, 2009.CrossRefGoogle Scholar
  38. 38.
    Oeff, M., Koch, H., Bousseljot, R., and Kreiseler, D.: The ptb diagnostic ecg database. National Metrology Institute of Germany,, 2012.
  39. 39.
    Heiberger, R. M., and Neuwirth, E., One-way anova. In: R through excel, pp. 165–191: Springer 2009.Google Scholar
  40. 40.
    Tsutsumi, T., Okamoto, Y., Kubota-Takano, N., Wakatsuki, D., Suzuki, H., Sezaki, K., Iwasawa, K., and Nakajima, T., Time–frequency analysis of the qrs complex in patients with ischemic cardiomyopathy and myocardial infarction. IJC Heart Vessel. 4:177–187, 2014.CrossRefGoogle Scholar
  41. 41.
    Dandapat, S., Sharma, L., and Tripathy, R., Quantification of diagnostic information from electrocardiogram signal: A review. In: Advances in communication and computing, pp. 17–39: Springer (2015)Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

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