Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
권호 표지
DOI QR코드

DOI QR Code

Prediction of Paroxysmal Atrial Fibrillation using Time-domain Analysis and Random Forest

  • Lee, Seung-Hwan (Department of Biomedical Engineering, College of Health Science, Yonsei University) ;
  • Kang, Dong-Won (Department of Biomedical Engineering, College of Health Science, Yonsei University) ;
  • Lee, Kyoung-Joung (Department of Biomedical Engineering, College of Health Science, Yonsei University)
  • Received : 2018.02.28
  • Accepted : 2018.04.11
  • Published : 2018.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The present study proposes an algorithm that can discriminate between normal subjects and paroxysmal atrial fibrillation (PAF) patients, which is conducted using electrocardiogram (ECG) without PAF events. For this, time-domain features and random forest classifier are used. Time-domain features are obtained from Poincare plot, Lorenz plot of ${\delta}RR$ interval, and morphology analysis. Afterward, three features are selected in total through feature selection. PAF patients and normal subjects are classified using random forest. The classification result showed that sensitivity and specificity were 81.82% and 95.24% respectively, the positive predictive value and negative predictive value were 96.43% and 76.92% respectively, and accuracy was 87.04%. The proposed algorithm had an advantage in terms of the computation requirement compared to existing algorithm, so it has suggested applicability in the more efficient prediction of PAF.

Keywords

References

  1. C. T. January, L. S. Wann, J. S. Alpert, H. Calkins, J. E. Cigarroa, J. C. Cleveland, ... and K. T. Murray, "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," Journal of the American College of Cardiology, vol. 64, no. 21, pp. e1-e76, 2014. https://doi.org/10.1016/j.jacc.2014.03.022
  2. A. J. Camm, P. Kirchhof, G. Y. Lip, U. Schotten, I. Savelieva, S. Ernst, ... and H. Heidbuchel, "Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC)," European heart journal, vol. 31, no. 19, pp. 2369-2429, 2010. https://doi.org/10.1093/eurheartj/ehq278
  3. G. Moody, A. Goldberger, S. McClennen, and S. Swiryn, "Predicting the onset of paroxysmal atrial fibrillation: The Computers in Cardiology Challenge 2001," Computers in Cardiology, Rotterdam, Netherlands, 2001, pp. 113-116.
  4. T. Thong, J. McNames, M. Aboy, and B. Goldstein, "Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes," IEEE Transactions on Biomedical Engineering, vol. 51, no. 4, pp. 561-569, 2004. https://doi.org/10.1109/TBME.2003.821030
  5. E. Ros, S. Mota, F. J. Fernandez, F. J. Toro, and J. L. Bernier, "ECG Characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm," Computers in biology and medicine, vol. 34, no. 8 pp. 679-696, 2004. https://doi.org/10.1016/j.compbiomed.2003.10.002
  6. B. Pourbabaee, and C. Lucas, "Automatic detection and prediction of paroxysmal atrial fibrillation based on analyzing ecg signal feature classification methods," 2008 Cairo International Biomedical Engineering Conference, Cairo, Egypt, 2008, pp. 1-4.
  7. M. Panusittikorn, N. Uchaipichat, C. Tantibundhit, and A. Buakamsri, "Prediction of paroxysmal atrial fibrillation occurrence with wavelet-based markers," Electrical Engineering/ Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on, Chiang Mai, Thailand, 2010, pp. 342-345.
  8. H. Costin, C. ROTARIU, "A New Method for Paroxysmal Atrial Fibrillation Automatic Prediction," Buletinul Institutului Politehnic din Iasi Tome, vol. 59, no. 1, pp. 71-83, 2013.
  9. Y. V. Chesnokov, "Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods," Artificial intelligence in medicine, vol. 43, no. 2, pp. 151-165, 2008. https://doi.org/10.1016/j.artmed.2008.03.009
  10. M. Mohebbi, and H. Ghassemian, "Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal,"Physiological measurement, vol. 32, no. 8, pp. 1147, 2011. https://doi.org/10.1088/0967-3334/32/8/010
  11. M. Mohebbi, and H. Ghassemian, "Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal," Computer methods and programs in biomedicine, vol. 105, no. 1, pp. 40-49, 2012. https://doi.org/10.1016/j.cmpb.2010.07.011
  12. K. H. Boon, Khalil-Hani, M., M. B. Malarvili, and C. W. Sia, "Paroxysmal atrial fibrillation prediction method with shorter HRV sequences," Computer Methods and Programs in Biomedicine, vol. 134, pp. 187-196, 2016. https://doi.org/10.1016/j.cmpb.2016.07.016
  13. A. Martinez, D. Abasolo, R. Alcaraz, and J. J. Rieta, "Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation," Medical engineering & physics, vol. 37, no. 7, pp. 692-697, 2015. https://doi.org/10.1016/j.medengphy.2015.03.021
  14. R. Alcaraz, A. Martinez, and J. J. Rieta, "The P Wave Time?Frequency Variability Reflects Atrial Conduction Defects before Paroxysmal Atrial Fibrillation," Annals of Noninvasive Electrocardiology, vol. 20, no. 5, pp. 433-445, 2015. https://doi.org/10.1111/anec.12240
  15. S. M. Kallenberger, C. Schmid, F. Wiedmann, D. Mereles, H. A. Katus, "A Simple, Non-Invasive Score to Predict Paroxysmal Atrial Fibrillation," PloS one, vol. 11, no. 9, pp. e0163621, 2016. https://doi.org/10.1371/journal.pone.0163621
  16. M. C. Wiggins, H. A. Firpi, R. R. Blanco, M. Amer, and S. C. Dudley, "Prediction of atrial fibrillation following cardiac surgery using rough set derived rules," Engineering in Medicine and Biology Society 2006. EMBS'06. 28th Annual International Conference of the IEEE, New York, USA, 2006.
  17. H. Gonna, M. M. Gallagher, X. H. Guo, Y. G. Yap, K. Hnatkova, and A. J. Camm, "P-wave abnormality predicts recurrence of atrial fibrillation after electrical cardioversion: a prospective study," Annals of Noninvasive Electrocardiology, vol. 19, no. 1, pp. 57-62, 2014. https://doi.org/10.1111/anec.12087
  18. C. Blanche, N. Tran, F. Rigamonti, H. Burri, and M. Zimmermann, "Value of P-wave signal averaging to predict atrial fibrillation recurrences after pulmonary vein isolation," Europace, vol. 15, no. 2, pp. 198-204, 2013. https://doi.org/10.1093/europace/eus251
  19. A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, ... and H. E. Stanley, "PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals," Circulation, vol. 101, no. 23, pp. e215-e220, 2000. https://doi.org/10.1161/01.CIR.101.23.e215
  20. J. Pan, and W. J. Tompkins, "A real-time QRS detection algorithm," IEEE transactions on biomedical engineering, vol. 3, pp. 230-236, 1985.
  21. M. Adnane, Z. Jiang, and S. Choi, "Development of QRS detection algorithm designed for wearable cardiorespiratory system," Computer methods and programs in biomedicine, vol. 93, no. 1, pp. 20-31, 2009. https://doi.org/10.1016/j.cmpb.2008.07.010
  22. M. S. Kim, Y. C. Cho, S. T. Seo, C. S. Son, and Y. N. Kim, "Auto-detection of R wave in ECG (electrocardiography) for patch-type ECG remote monitoring system," Biomedical Engineering Letters, vol. 1, no. 3, pp. 180-187, 2011. https://doi.org/10.1007/s13534-011-0029-4
  23. M. H. Song, S. P. Cho, W. Kim, and K. J. Lee, "New real-time heartbeat detection method using the angle of a single-lead electrocardiogram," Computers in biology and medicine, vol. 59, pp. 73-79, 2015. https://doi.org/10.1016/j.compbiomed.2015.01.015
  24. U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim, and J. S. Suri, "Heart rate variability: a review," Medical and biological engineering and computing, vol. 44, no. 12, pp. 1031-1051, 2006. https://doi.org/10.1007/s11517-006-0119-0
  25. M. V. Perez, F. E. Dewey, R. Marcus, E. A. Ashley, A. A. Al- Ahmad, P. J. Wang, and V. F. Froelicher, "Electrocardiographic predictors of atrial fibrillation," American heart journal, vol. 158, no. 4, pp. 622-628, 2009. https://doi.org/10.1016/j.ahj.2009.08.002
  26. S. Sarkar, D. Ritscher, and R. Mehra, "A detector for a chronic implantable atrial tachyarrhythmia monitor," IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 1219-1224, 2008. https://doi.org/10.1109/TBME.2007.903707
  27. G. B. Moody, H. Koch, and U. Steinhoff, "The physionet/ computers in cardiology challenge 2006: Qt interval measurement," 2006 Computers in Cardiology, Valencia, Spain, pp. 313-316, 2006.
  28. J. Kittler, Handbook of pattern recognition and image processing, Academic Press, 1986, pp. 59-83.
  29. I. Guyon, and A. Elisseeff, "An introduction to variable and feature selection," Journal of machine learning research, vol. 3, no. Mar, pp. 1157-1182, 2003.
  30. P. N. Tan, Introduction to data mining, Pearson Education India, 2006, pp. 145-195.
  31. L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001. https://doi.org/10.1023/A:1010933404324
  32. T. G. Dietterich, and E. B. Kong, "Machine learning bias, statistical bias, and statistical variance of decision tree algorithms," Technical report, Department of Computer Science, Oregon State University, 1995.
  33. T. H. Cormen, Introduction to algorithms, MIT press, 2009, pp. 1-145.
  34. S. Theodoridis, and K. Koutroumbas, Pattern Recognition, Academic Press, 2009, pp. 13-89
  35. L. Rokach, and O. Maimon, Data Mining and Knowledge Discovery Handbook, Springer, 2010, pp. 149-174
  36. S. Nattel, B. Burstein, and D. Dobrev, "Atrial remodeling and atrial fibrillation mechanisms and implications," Circulation: Arrhythmia and Electrophysiology, vol. 1, no. 1, pp. 62-73, 2008. https://doi.org/10.1161/CIRCEP.107.754564
  37. S. Poli, V. Barbaro, P. Bartolini, G. Calcagnini, and F. Censi, "Prediction of atrial fibrillation from surface ECG: review of methods and algorithms," Annali dell'Istituto superiore di sanita, vol. 39, no. 2, pp. 195-203, 2002.
  38. P. E. Dilaveris, E. J. Gialafos, S. K. Sideris, A. M. Theopistou, G. K. Andrikopoulos, M. Kyriakidis, ... and P. K. Toutouzas, "Simple electrocardiographic markers for the prediction of paroxysmal idiopathic atrial fibrillation," American heart journa, vol. 135, no. 5, pp. 733-738, 1998. https://doi.org/10.1016/S0002-8703(98)70030-4
  39. K. Ishida, H. Hayashi, A. Miyamoto, Y. Sugimoto, M. Ito, Y. Murakami, and M. Horie, "P wave and the development of atrial fibrillation," Heart Rhythm, vol. 7, no. 3, pp. 289-294, 2010. https://doi.org/10.1016/j.hrthm.2009.11.012
  40. A. A. Euan, N. Josef, Cardiology Explained, Remedica, 2004, pp. 19-22.