Journal of Medical Systems

, 41:11 | Cite as

Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection

  • Juyoung Park
  • Mingon Kang
  • Jean Gao
  • Younghoon Kim
  • Kyungtae KangEmail author
Mobile & Wireless Health
Part of the following topical collections:
  1. Advances in Big-Data based mHealth Theories and Applications


Detecting arrhythmia from ECG data is now feasible on mobile devices, but in this environment it is necessary to trade computational efficiency against accuracy. We propose an adaptive strategy for feature extraction that only considers normalized beat morphology features when running in a resource-constrained environment; but in a high-performance environment it takes account of a wider range of ECG features. This process is augmented by a cascaded random forest classifier. Experiments on data from the MIT-BIH Arrhythmia Database showed classification accuracies from 96.59% to 98.51%, which are comparable to state-of-the art methods.


ECG Heartbeat classification Heartbeat morphology features Cascaded classifiers Adaptive feature extraction 



This work was supported by the Ministry of Science, ICT and Future Planning (MSIP), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2016-H8501-16-1018) supervised by the Institute for Information & communications Technology Promotion (IITP), and by an IITP grant funded by the Korea government (MSIP; No. B0101-15-0557, Resilient Cyber-Physical Systems Research).


  1. 1.
    Minami, K., Nakajima, H., and Toyoshima, T., Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans. Biomed. Eng. 46(2):179–185, 1999.CrossRefPubMedGoogle Scholar
  2. 2.
    Evans, S.J., Hastings, H., and Bodenheimer, M.M., Differentiation of beats of ventricular and sinus origin using a self-training neural network. Pacing Clin. Electrophysiol. 17(4):611–626, 1994.CrossRefPubMedGoogle Scholar
  3. 3.
    Clayton, R.H., Murray, A., and Campbell, R.W., Recognition of ventricular fibrillation using neural networks. Med. Biol. Soc. Comput. 32(2):217–220, 1994.CrossRefGoogle Scholar
  4. 4.
    Barro, S., Ruiz, R., Cabello, D., and Mira, J., Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: A diagnostic system. J. Biomed. Eng. 11(4):320–328, 1989.Google Scholar
  5. 5.
    Kastor, J.A., Arrhythmias, 2nd ed. Saunders, London, U.K., 1994.Google Scholar
  6. 6.
    Ye, C., Kumar, B.V., and Coimbra, M.T., Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans. Biomed. Eng. 59(10):2930–2941, 2012.CrossRefPubMedGoogle Scholar
  7. 7.
    de Lannoy, G., Francois, D., Delbeke, J., and Verleysen, M., Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans. Biomed. Eng. 59(1):241–247, 2012.CrossRefPubMedGoogle Scholar
  8. 8.
    Silva, G., Moody, B., and Celi, L., Improving the quality of ECGs collected using mobile phones: The PhysioNet/Computing in Cardiology Challenge 2011. Proc. Computing in Cardiology, Hangzhou, China. 273–276, 2011.Google Scholar
  9. 9.
    Oresko, J.J., Jin, Z., Cheng, J., Huang, S., Sun, Y., Duschl, H., and Cheng, A.C., A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Trans. Inform. Tech. in Biomedicine. 14(3):734–740, 2010.CrossRefGoogle Scholar
  10. 10.
    Yen, T.-H., Chang, C.-Y., and Yu, S.-N., A portable real-time ECG recognition system based on smartphone. Proc. IEEE Conf. Eng. Med. Biol. Soc., Osaka, Japan. 7262–7265, 2013.Google Scholar
  11. 11.
    Leutheuser, H., Gradl, S., Kugler, P., Anneken, L., Arnold, M., Achenbach, S., and Eskofier, B.M., Comparison of real-time classification system for arrhythmia detection on Android-based mobile device. Proc. IEEE Int. Conf. Eng. Med. Biol. Soc., Chicago, Illinois, USA. 2690–2693, 2014.Google Scholar
  12. 12.
    Park, J., Lee, K., and Kang, K., Pit-a-pat: A smart electrocardiogram system for detecting arrhythmia. Telemed. J. E. Health. 21(10):814–821, 2015.Google Scholar
  13. 13.
    Pan, J. and Tompkins, W. J., A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME-32(3):230–236, 1985.Google Scholar
  14. 14.
    Moody, G.B., and Mark, R.G., The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3):45–50, 2001.CrossRefPubMedGoogle Scholar
  15. 15.
    Bhagwat, P., Bluetooth: Technology for short-range wireless apps. IEEE Internet Comput. 5(3):96–103, 2001.CrossRefGoogle Scholar
  16. 16.
    Park, J., Kang, M., Kim, Y., and Kang, K., Heartbeat classification for detecting arrhythmia using normalized beat morphology features. Proc. IEEE Int. Conf. Bioinformatics. Biomed., Washington D.C., USA:1743–1744, 2015.Google Scholar
  17. 17.
    Park, J., Lee, S., and Kang, K., Arrhythmia detection using amplitude difference features based on random forest. Proc. IEEE Int. Conf. Eng. Med. Biol. Soc. Milan, Italy. 5191–5194, 2015.Google Scholar
  18. 18.
    Laguna, P., Jane, R., and Caminal, P., Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE database. Comput. Biomed. Res. 27(1):45–60, 1994.Google Scholar
  19. 19.
    Afonso, V.X., Tompkins, W.J., Nguyen, T.Q., and Luo, S., ECG beat detection using filter banks. IEEE Trans. Biomed. Eng. 46(2):192–202, 1999.CrossRefPubMedGoogle Scholar
  20. 20.
    Kadambe, S., Murray, R., and Boudreaux-Bartels, G.F., Wavelet transform-based QRS complex detection. IEEE Trans. Biomed. Eng. 46(7):838–848, 1999.CrossRefPubMedGoogle Scholar
  21. 21.
    Benali, R., Bereksi Reguig, F., and Hadj Slimane, Z., Automatic classification of heartbeats using wavelet neural network. J. Med. Syst. 36(2):883–892, 2012.CrossRefPubMedGoogle Scholar
  22. 22.
    Huang, H.F., Hu, G.S., and Zhu, L., Sparse representation-based heartbeat classification using independent component analysis. J. Med. Syst. 36(3):1235–1247, 2012.CrossRefPubMedGoogle Scholar
  23. 23.
    Salman, O.H., Rasid, M.F., Saripan, M.I., and Subramaniam, S.K., Multi-sources data fusion framework for remote triage prioritization in telehealth. J. Med. Syst. 38(9):103–126, 2014.CrossRefPubMedGoogle Scholar
  24. 24.
    de Chazal, P., O’Dwyer, M., and Reilly, R.B., Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7):1196–1206, 2004.CrossRefPubMedGoogle Scholar
  25. 25.
    Llamedo, M., and Martinez, J.P., Heartbeat classification using features selection driven by database generalization criteria. IEEE Trans. Biomed. Eng. 58(3):616–625, 2011.CrossRefPubMedGoogle Scholar
  26. 26.
    Gama, J., and Brazdil, P., Cascade generalization. Mach. Learn. 41(3):315–343, 2000.CrossRefGoogle Scholar
  27. 27.
    Breiman, L., Random forests. Mach. Learn. 45(1):5–32, 2001.CrossRefGoogle Scholar
  28. 28.
    Kohavi, R., A study of cross-validation and bootstrap for accuracy estimation and model selection. Proc. International Joint Conference on Artificial Intelligence, Quebec, Canada. 1137–1143, 1995.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Juyoung Park
    • 1
  • Mingon Kang
    • 2
  • Jean Gao
    • 3
  • Younghoon Kim
    • 1
  • Kyungtae Kang
    • 1
    Email author
  1. 1.Department of Computer Science & EngineeringHanyang UniversityAnsanRepublic of Korea
  2. 2.Department of Computer ScienceKennesaw State UniversityKennesawUSA
  3. 3.Department of Computer Science & EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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