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ECG arrhythmia classification using time frequency distribution techniques

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Abstract

In this paper, we focus on classifying cardiac arrhythmias. The MIT-BIH database is used with 14 original classes of labeling which is then mapped into 5 more general classes, using the Association for the Advancement of Medical Instrumentation standard. Three types of features were selected with a focus on the time–frequency aspects of ECG signal. After using the Wigner–Ville distribution the time–frequency plane is split into 9 windows considering the frequency bandwidth and time duration of ECG segments and peaks. The summation over these windows are employed as pseudo-energy features in classification. The “subject-oriented” scheme is used in classification, meaning the train and test sets include samples from different subjects. The subject-oriented method avoids the possible overfitting issues and guaranties the authenticity of the classification. The overall sensitivity and positive predictivity of classification is 99.67 and 98.92%, respectively, which shows a significant improvement over previous studies.

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References

  1. Jenkins D, Gerred S. Normal ECG. http://www.ecglibrary.com/norm.php. Accessed Febr 2015.

  2. de Chazal P, O’Dwyer M, Reilly R. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng. 2004;51(7):1196–206.

    Article  Google Scholar 

  3. de Oliveira L, Andreao R, Sarcinelli-Filho M. Premature ventricular beat classification using a dynamic Bayesian network. In: Annual international conference of the IEEE engineering in medicine and biology society, EMBC, Boston, MA; 2011.

  4. Zeng XD, Chao S, Wong F. Ensemble learning on heartbeat type classification. In: International conference on system science and engineering (ICSSE), Macao; 2011.

  5. Cohen L. Time-frequency distributions—a review. Proc IEEE. 2002;77(7):941–81.

    Article  Google Scholar 

  6. Ince T, Kiranyaz S, Gabbouj M. A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans Biomed Eng. 2009;56(5):1415–26.

    Article  Google Scholar 

  7. Jiang X, Zhang L, Zhao Q, Albayrak S. ECG arrhythmias recognition system based on independent component analysis feature extraction. In: TENCON 2006 IEEE Region 10 Conference, Hong Kong; 2006.

  8. Yang S, ShenH. Heartbeat classification using discrete wavelet transform and kernel principal component analysis. In: TENCON Spring Conference, 2013 IEEE, Sydney, NSW; 2013.

  9. Thomas M, Das MKAS. Automatic ECG arrhythmia classification using dual tree complex wavelet based features. Int J Electron Commun (AEÜ). 2015;69(4):715–21.

    Article  Google Scholar 

  10. Osowski S, Hoai LT, Markiewicz T. Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans Biomed Eng. 2004;51(4):582–9.

    Article  Google Scholar 

  11. Ye C, Kumar B, Coimbra M. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng. 2012;59(10):2930–41.

    Article  Google Scholar 

  12. Lagerholm M, Peterson C, Braccini G, Edenbrandt L. Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans Biomed Eng. 2002;47(7):838–48.

    Article  Google Scholar 

  13. Jiang W, Kong S. Block-based neural networks for personalized ECG signal classification. IEEE Trans Neural Netw. 2007;18(6):1750–61.

    Article  Google Scholar 

  14. Llamedo M, Martinez J. Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans Biomed Eng. 2011;58(3):616–25.

    Article  Google Scholar 

  15. de Lannoy G, Francois D, Delbeke J, Verleysen M. Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans Biomed Eng. 2012;59(1):241–7.

    Article  Google Scholar 

  16. Ghorbani Afkhami R, Azarnia G, Tinati MA. Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recognit Lett. 2016;70:45–51.

    Article  Google Scholar 

  17. Ghorbani Afkhami R, Tinati MA. ECG based detection of left ventricular hypertrophy using higher order statistics. In: 23rd Iranian Conference on Electrical Engineering (ICEE), Tehran; 2015.

  18. Pachori RB, Nishad A. Cross-terms reduction in the Wigner–Ville distribution using tunable-Q wavelet transform. Sig Process. 2016;120:288–304.

    Article  Google Scholar 

  19. Breiman L, Friedman J, Stone CJ, Olshen R. Classification and regression trees. London: Chapman and Hall; 1984.

    MATH  Google Scholar 

  20. Zaunseder S, Huhle R, Malberg H. CinC challenge—assessing the usability of ECG by ensemble decision trees. In: Computing in cardiology, Hangzhou; 2011.

  21. Maimon O, Rokach L. Data mining and knowledge discovery handbook. Berlin: Springer; 2010.

    Book  MATH  Google Scholar 

  22. Opitz D, Maclin R. Popular ensemble methods: an empirical study. J Artif Intell Res. 1999;11:169–98.

    MATH  Google Scholar 

  23. Mark R, Moody G. MIT-BIH database and software catalog. http://ecg.mit.edu/dbinfo.html (1997). Accessed Feb 2015.

  24. “Recommended practice for testing and reporting performance results of ventricular arrhythmia detection algorithms,” Association for the Advancement of Medical Instrumentation, 1987.

  25. “Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms,” Association for the Advancement of Medical Instrumentation, 1998.

  26. Rodriguez J, Goñi A, Illarramendi A. Real-time classification of ECGs on a PDA. IEEE Trans Inf Technol Biomed. 2005;9(1):23–34.

    Article  Google Scholar 

  27. Awodeyi A, Alty S, Ghavami M. Median filter approach for removal of baseline wander in photoplethysmography signals. In: European Modelling Symposium (EMS), Manchester; 2013.

  28. Prasad G, Sahambi J. Classification of ECG arrhythmias using multi-resolution analysis and neural networks. In: Conference on Convergent Technologies for the Asia-Pacific Region, TENCON, vol. 1, p. 227–231, 2003.

  29. Martis R, Acharya R, Ray A. Application of higher order cumulants to ECG signals for the cardiac health diagnosis. In: International Conference of the IEEE EMBS, Boston; 2011.

  30. Ebrahimzadeh A, Khazaee A. Higher order statistics for automated classification of ECG beats. In: International conference on electrical and control engineering (ICECE), Yichang; 2011.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, 29 Bahman Blvd., Tabriz, Iran

    Safa Sultan Qurraie

  2. Faculty of Engineering and Built Environment, University of Newcastle, Callaghan, NSW, 2308, Australia

    Rashid Ghorbani Afkhami

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  1. Safa Sultan Qurraie
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  2. Rashid Ghorbani Afkhami
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Correspondence to Rashid Ghorbani Afkhami.

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Sultan Qurraie, S., Ghorbani Afkhami, R. ECG arrhythmia classification using time frequency distribution techniques. Biomed. Eng. Lett. 7, 325–332 (2017). https://doi.org/10.1007/s13534-017-0043-2

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  • DOI: https://doi.org/10.1007/s13534-017-0043-2

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