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Automated detection of myocardial infarction using robust features extracted from 12-lead ECG

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Abstract

Electrocardiography is a useful diagnostic tool for various cardiovascular diseases, such as myocardial infarction (MI). An electrocardiograph (ECG) records the electrical activity of the heart, which can reflect any abnormal activity. MI recognition by visual examination of an ECG requires an expert’s interpretation and is difficult because of the short duration and small amplitude of the changes in ECG signals associated with MI. Therefore, we propose a new method for the automatic detection of MI using ECG signals. In this study, we used maximal overlap discrete wavelet transform to decompose the data, extracted the variance, inter-quartile range, Pearson correlation coefficient, Hoeffding’s D correlation coefficient and Shannon entropy of the wavelet coefficients and used the k-nearest neighbor model to detect MI. The accuracy, sensitivity and specificity of the model were 99.57%, 99.82% and 98.79%, respectively. Therefore, the system can be used in clinics to help diagnose MI.

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References

  1. Vos, T., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388(10053), 1545–1602 (2016)

    Article  Google Scholar 

  2. Stuart, R., et al.: Davidson’s Principles and Practice of Medicine, 21st edn, pp. 588–599. Churchill Livingstone, London (2018)

    Google Scholar 

  3. Xingyu, Z., et al.: Atlas-based quantification of cardiac remodeling due to myocardial infarction. PLoS ONE 9(10), 1 (2014)

    Google Scholar 

  4. Guven, G., Gurkan, H., Guz, U.: Biometric identification using fingertip electrocardiogram signals. Signal Image Video Process. 12, 1–8 (2018)

    Article  Google Scholar 

  5. Lankford, J.: The Minnesota code for ECG classification. Adaptation to CR (CH) leads and modification of the code for ECGs recorded during and after exercise. J. Intern. Med. 183(481), 13–17 (2010)

    Google Scholar 

  6. Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)

    Article  Google Scholar 

  7. Bousseljot, R., Kreiseler, D., Schnabel, A.: Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomed. Tech./Biomed. Eng. 40(1), 317–318 (1995)

    Google Scholar 

  8. Ingrid, D.: Ten Lectures on Wavelets, vol. 194. SIAM, Philadelphia (1992)

    MATH  Google Scholar 

  9. Singh, B.N., Tiwari, A.K.: Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process. 16(3), 275–287 (2006)

    Article  Google Scholar 

  10. Pan, J., Tompkins, W.J., et al.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)

    Article  Google Scholar 

  11. Percival, D.B., Mofjeld, H.O.: Analysis of subtidal coastal sea level fluctuations using wavelets. J. Am. Stat. Assoc. 92(439), 868–880 (1997)

    Article  MATH  Google Scholar 

  12. Martis, R.J., Acharya, U.R., Min, L.C.: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Signal Process. 8(5), 437–448 (2013)

    Article  Google Scholar 

  13. Hoeffding, W., Robbins, H.: The central limit theorem for dependent random variables. Duke Math. J. 15(3), 773–780 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  14. Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27(4), 623–656 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  15. Maharaj, E.A., Andrses, M.A.: Discriminant analysis of multivariate time series: application to diagnosis based on ECG signals. Comput. Stat. Data Anal. 70, 67–87 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  16. Duda, R.O., Peter, E.H., David, G.S.: Pattern Classification, pp. 177–191. Wiley, Hoboken (2007)

    Google Scholar 

  17. Hoeffding, W.: A non-parametric test of independence. Ann. Math. Stat. 19(4), 546–557 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  18. Sun, L., et al.: ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans. Biomed. Eng. 59(12), 3348–3356 (2012)

    Article  Google Scholar 

  19. Correa, R., Arini, P.D., Correa, L.S., Valentinuzzi, M.E., Laciar, E.: New VCG and ECG indexes for early identification of acute myocardial infarction patients. In: VI Latin American Congress on Biomedical Engineering CLAIB 2014 (2014)

  20. Banerjee, S., Mitra, M.: Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Trans. Instrum. Meas. 63(2), 326–333 (2014)

    Article  Google Scholar 

  21. Bhaskar, N.A.: Performance analysis of support vector machine and neural networks in detection of myocardial infarction. Procedia Comput. Sci. 46, 20–30 (2015)

    Article  Google Scholar 

  22. Bin, L., et al.: A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput. Biol. Med. 61, 178–184 (2015)

    Article  Google Scholar 

  23. Sharma, L.N., et al.: Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans. Biomed. Eng. 62(7), 1827–2837 (2015)

    Article  Google Scholar 

  24. Remya, R.S., et al.: Classification of myocardial infarction using multi resolution wavelet analysis of ECG. Procedia Technol. 24, 949–956 (2016)

    Article  Google Scholar 

  25. Acharya, U.R., et al.: Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl. Based Syst. 99, 146–156 (2016)

    Article  Google Scholar 

  26. Acharya, U.R., et al.: Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. Inf. Sci. 377, 17–29 (2017)

    Article  Google Scholar 

  27. Acharya, U.R., et al.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415–416, 190–198 (2017)

    Article  Google Scholar 

  28. Mohit, K., et al.: Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework. Entropy-Switz 19(9), 488 (2017)

    Article  Google Scholar 

  29. Reasat, T., Shahnaz, C.: Detection of inferior myocardial infarction using shallow convolutional neural networks. In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 718–721 (2017)

  30. Liu, W., et al.: Real-time multilead convolutional neural network for myocardial infarction detection. IEEE J. Biomed. Health Inform. 22(5), 1434–1444 (2017)

    Article  Google Scholar 

  31. Sharma, L.D., Sunkaria, R.K.: Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process. 12(2), 199–206 (2017)

    Article  Google Scholar 

  32. Sharma, M., Tan, R.S., Acharya, U.R.: A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput. Biol. Med. 102, 341–356 (2018)

    Article  Google Scholar 

  33. Sadhukhan, D., Pal, S., Mitra, M.: Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data. IEEE Trans. Instrum. Meas. 67(10), 2303–2313 (2018)

    Article  Google Scholar 

  34. Dionisije, S., et al.: Real-time event-driven classification technique for early detection and prevention of myocardial infarction on wearable systems. IEEE Trans. Biomed. Circuits Syst. 12(5), 982–991 (2018)

    Article  Google Scholar 

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Acknowledgements

The work described in this paper is supported by the National Natural Science Foundation of China (NSFC, 81773545).

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Authors and Affiliations

  1. School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China

    Zhuochen Lin, Yongxiang Gao, Yimin Chen, Qi Ge, Gehendra Mahara & Jinxin Zhang

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  1. Zhuochen Lin
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  2. Yongxiang Gao
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  3. Yimin Chen
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  4. Qi Ge
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  5. Gehendra Mahara
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  6. Jinxin Zhang
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Contributions

ZL wrote the paper and performed experiments. JZ, YG, YC, QG and GM offered useful suggestions for the paper preparation and writing. All authors have read and approved the final manuscript.

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Correspondence to Jinxin Zhang.

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Lin, Z., Gao, Y., Chen, Y. et al. Automated detection of myocardial infarction using robust features extracted from 12-lead ECG. SIViP 14, 857–865 (2020). https://doi.org/10.1007/s11760-019-01617-y

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  • DOI: https://doi.org/10.1007/s11760-019-01617-y

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