Advertisement

Comparison of Atrial Fibrillation Detection Performance Using Decision Trees, SVM and Artificial Neural Network

  • Szymon SiecińskiEmail author
  • Paweł S. Kostka
  • Ewaryst J. Tkacz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)

Abstract

Atrial fibrillation (AFib) is a supraventricular tachyarrhythmia characterized by uncoordinated atrial activation and ineffective atrial contraction. AFib affects 1–2% of the general population, its prevalence increases with age and may remain long undiagnosed. Due to costs of hospitalization and treatment related to AFib and increasing prevalence, effective methods of detecting atrial fibrillation are needed.

In this study we compared AFib classification using support vector machine (SVM), artificial neural network (ANN) and binary decision trees on 10 ECG signals. We considered 8 parameters associated with RR intervals: mean RR, SDNN, RMSSD, PLF, PHF, LF/HF, SD1 and SD2. In this comparison the best performing AFib classifier was binary decision tree with maximum number of splits equal to 100 and the worst case was SVM classifier with medium Gaussian kernel and using only one feature. Achieved result should encourage further studies using decision trees.

Keywords

Atrial fibrillation Classification SVM Classification trees ANN 

References

  1. 1.
    Brennan, M., Palaniswami, M., Kamen, P.: Do existing measure of poincaré plot geometry reflect nonlinear features of heart rate variability? IEEE Trans. Biomed. Eng. 48, 1342–1347 (2001)CrossRefGoogle Scholar
  2. 2.
    Kirchhof, P., et al.: ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur. Heart J. 37, 2893–2962 (2016).  https://doi.org/10.1093/eurheartj/ehw210CrossRefGoogle Scholar
  3. 3.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn 20, 273 (1995).  https://doi.org/10.1007/BF00994018CrossRefzbMATHGoogle Scholar
  4. 4.
    Dash, S., Raeder, E., Merchant, S., Chon, K.: A statistical approach for accurate detection of atrial fibrillation and flutter. Comput. Cardiol. 36, 137–140 (2009)Google Scholar
  5. 5.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)CrossRefGoogle Scholar
  6. 6.
    Fuster, V., et al.: ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Revise the 2001 Guidelines for the Management of Patients With Atrial Fibrillation). Circulation 114, e257–e354 (2006)CrossRefGoogle Scholar
  7. 7.
    Camm, A.J., Malik, M., Bigger, J.T., Breithardt, G., Cerutti, S., Cohen, R., Coumel, P., Fallen, E., Kennedy, H., Kleiger, R.E., Lombardi, F.: Heart rate variability standards of measurement, physiological interpretation, and clinical use. Task force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Circulation 93, 1043–1065 (1996).  https://doi.org/10.1161/01.CIR.93.5.1043CrossRefGoogle Scholar
  8. 8.
    Huang, C., Ye, S., Chen, H., Li, D., He, F., Tu, Y.: A novel method for detection of the transition between atrial fibrillation and sinus rhythm. IEEE Trans. Biomed. Eng. 58(4), 1113–1119 (2011)CrossRefGoogle Scholar
  9. 9.
    Hulley, S.B., et al.: Designing Clinical Research, 3rd edn, pp. 189–190. Lippincott Williams & Wilkins, Philadelphia (2007)Google Scholar
  10. 10.
    January, C.T., Wann, L.S., Alpert, J.S., Calkins, H., Cigarroa, J.E., Cleveland Jr, J.C., Conti, J.B., Ellinor, P.T., Ezekowitz, M.D., Field, M.E., Murray, K.T., Sacco, R.L., Stevenson, W.G., Tchou, P.J., Tracy, C.M., Yancy, C.W.: 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American college of cardiology/American heart association task force on practice guidelines and the heart rhythm society. Circulation 130, e199–e267 (2014)Google Scholar
  11. 11.
    Karmakar, C.K., Gubbi, J., Khandoker, A.H., Palaniswami, M.: Analyzing temporal variability of standard descriptors of poincaré plots. J. Electrocardiol. 43, 719–724 (2010)CrossRefGoogle Scholar
  12. 12.
    Kostka, P.S., Tkacz, E.J.: Feature based, extraction, on time-frequency and independent component analysis for improvement of separation ability in atrial fibrillation detector. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, pp. 2960–2963 (2008)Google Scholar
  13. 13.
    Kostka, P.S., Tkacz, E.J.: Feature extraction in time-frequency signal analysis by means of matched wavelets as a feature generator. In: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30–3 August–September 2011, pp. 4996–4999 (2011)Google Scholar
  14. 14.
    Larburu, N., Lopetegi, T., Romero, I.: Comparative study of algorithms for atrial fibrillation detection. In: 2011 Computing in Cardiology, Hangzhou, pp. 265–268. IEEE (2011)Google Scholar
  15. 15.
    Markides, V., Schilling, R.J.: Atrial fibrillation: classification, pathophysiology, mechanisms and drug treatment. Heart 89, 939–943 (2003)CrossRefGoogle Scholar
  16. 16.
    Mohebbi, M., Ghassemian, H.: Detection of atrial fibrillation episodes using SVM. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, pp. 177–180 (2008)Google Scholar
  17. 17.
    Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol 20(3), 45–50 (2001). (PMID: 11446209)CrossRefGoogle Scholar
  18. 18.
    Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of CVPR 1997, Puerto Rico (1997)Google Scholar
  19. 19.
    Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. (BME) 32(3), 230–236 (1985).  https://doi.org/10.1109/TBME.1985.325532CrossRefGoogle Scholar
  20. 20.
    Goldberger, A.L., Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C.H., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). http://circ.ahajournals.org/content/101/23/e215.fullCrossRefGoogle Scholar
  21. 21.
    Ruan, X., Liu, C., Wang, X., Li, P.: Automatic detection of atrial fibrillation using R-R interval signal. In: 4th International Conference on Biomedical Engineering and Informatics, vol. 2, pp. 644–647 (2011)Google Scholar
  22. 22.
    Schölkopf, B., Burges, C., Vapnik, V.: Extracting support data for a given task. In: Proceedings of First International Conference on Knowledge Discovery & Data Mining, Menlo Park, pp. 252–257 (1995)Google Scholar
  23. 23.
    Tateno, K., Glass, L.: A method for detection of atrial fibrillation using RR intervals. Comput. Cardiol. 27, 391–394 (2000)Google Scholar
  24. 24.
    Tulppo, M.P., Makikallio, T.H., Takala, T.E., Seppanen, T., Huikuri, H.V.: Quantitative beat-to-beat analysis of heart rate dynamics during exercise. Am. J. Physiol. 271, H244–H252 (1996)Google Scholar
  25. 25.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefGoogle Scholar
  26. 26.
    Wedekind, D.: qrsdetector. TU Dresden, Institute for Biomedical Engineering, Biosignal Processing Group (2014). https://github.com/danielwedekind/qrsdetector
  27. 27.
    Wiesel, J., Fitzig, L., Herschman, Y., Messineo, F.C.: Detection of atrial fibrillation using a modified microlife blood pressure monitor. Am. J. Hypertens. 22, 848–52 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Szymon Sieciński
    • 1
    Email author
  • Paweł S. Kostka
    • 1
  • Ewaryst J. Tkacz
    • 1
  1. 1.Department of Biosensors and Biomedical Signal ProcessingSilesian University of TechnologyZabrzePoland

Personalised recommendations