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

  • Systems-Level Quality Improvement
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Abstract

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.

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References

  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.

  2. Goldberger, A. L. Clinical electrocardiography: a simplified approach: Elsevier Health Sciences, 2012.

  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.

  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.

    Article  CAS  PubMed  Google Scholar 

  5. Martis, R. J., Acharya, U. R., and Adeli, H., Current methods in electrocardiogram characterization. Comput. Biol. Med. 48:133–149, 2014.

    Article  PubMed  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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)

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  14. Jayachandran, E. et al., Analysis of myocardial infarction using discrete wavelet transform. J. Med. Syst. 34(6):985–992, 2010.

    Article  CAS  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

  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. 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.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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: http://www.sciencedirect.com/science/article/pii/S0957417412006690.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  23. Oppenheim, A. V., and Lim, J. S., The importance of phase in signals. Proc. IEEE 69(5):529–541, 1981.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  26. Rangayyan, R. M. Biomedical signal analysis. Vol. 33: Wiley, 2015.

  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.

  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, 235

  29. Selesnick, I. W., Hilbert transform pairs of wavelet bases. IEEE Signal Process. Lett. 8(6):170–173, 2001.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  34. Bishop, C. M., Pattern recognition. Mach. Learn., 2006.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  37. Sokolova, M., and Lapalme, G., A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4):427–437, 2009.

    Article  Google Scholar 

  38. Oeff, M., Koch, H., Bousseljot, R., and Kreiseler, D.: The ptb diagnostic ecg database. National Metrology Institute of Germany, http://www.physionet.org/physiobank/database/ptbdb, 2012.

  39. Heiberger, R. M., and Neuwirth, E., One-way anova. In: R through excel, pp. 165–191: Springer 2009.

  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.

    Article  Google Scholar 

  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)

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Acknowledgments

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.

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Authors and Affiliations

  1. Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    R. K. Tripathy & S. Dandapat

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  1. R. K. Tripathy
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  2. S. Dandapat
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Correspondence to R. K. Tripathy.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Tripathy, R.K., Dandapat, S. Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features. J Med Syst 40, 143 (2016). https://doi.org/10.1007/s10916-016-0505-6

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