Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation

Diana Batista, Ana Fred

2015

Abstract

Atrial fibrillation (AF) is the most common type of arrhythmia. This work presents a pattern analysis approach to automatically classify electrocardiographic (ECG) records as normal sinus rhythm or AF. Both spectral and time domain features were extracted and their discrimination capability was assessed individually and in combination. Spectral features were based on the wavelet decomposition of the signal and time parameters translated heart rate characteristics. The performance of three classifiers was evaluated: k-nearest neighbour (kNN), artificial neural network (ANN) and support vector machine (SVM). The MITBIH arrhythmia database was used for validation. The best results were obtained when a combination of spectral and time domain features was used. An overall accuracy of 99.08 % was achieved with the SVM classifier.

References

  1. Al-Fahoum, A. and Howitt, I. (1999). Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias. Medical & biological engineering & computing, 37(5), pp.566--573.
  2. Artis, S., Mark, R. and Moody, G. (1991). Detection of atrial fibrillation using artificial neural networks. pp.173--176.
  3. Beale, R. and Fiesler, E. (1997). Handbook of neural computation. 1st ed. Bristol: Institute of Physics Pub.
  4. Clayton, R., Murray, A. and Campbell, R. (1994). Recognition of ventricular fibrillation using neural networks. Medical and Biological Engineering and Computing, 32(2), pp.217--220.
  5. Dash, S., Chon, K., Lu, S. and Raeder, E. (2009). Automatic Real Time Detection of Atrial Fibrillation. Ann Biomed Eng, 37(9), pp.1701-1709.
  6. De Chazal, P., O'Dwyer, M. and Reilly, R. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. Biomedical Engineering, IEEE Transactions on, 51(7), pp.1196--1206.
  7. Fletcher, T. (2009). Support vector machines explained. Tutorial paper. [online] Available at: http://www.tristanfletcher.co.uk/. [Accessed 5 Nov. 2014]
  8. Fowler, J. (2005). The redundant discrete wavelet transform and additive noise. Signal Processing Letters, IEEE, 12(9), pp.629--632.
  9. Güler, I. (2005). ECG beat classifier designed by combined neural network model. Pattern recognition, 38(2), pp.199--208.
  10. Huang, C., Ye, S., Chen, H., Li, D., He, F. and Tu, Y. (2011). A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm. IEEE Transactions on Biomedical Engineering, 58(4), pp.1113-1119.
  11. Huff, J. (2006). ECG workout. 1st ed. Ambler, PA: Lippincott Williams & Wilkins.
  12. Iliev, I., Krasteva, V. and Tabakov, S. (2007). Real-time detection of pathological cardiac events in the electrocardiogram. Physiological measurement, 28(3), p.259.
  13. Inan, O., Giovangrandi, L. and Kovacs, G. (2006). Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. Biomedical Engineering, IEEE Transactions on, 53(12), pp.2507--2515.
  14. Kaiser, S., Kirst, M. and Kunze, C. (2010). Automatic Detection of Atrial Fibrillation for Mobile Devices. Springer, pp.258--270.
  15. Kara, S. and Okandan, M. (2007). Atrial fibrillation classification with artificial neural networks. Pattern Recognition, 40(11), pp.2967--2973.
  16. Khadra, L., Al-Fahoum, A. and Al-Nashash, H. (1997). Detection of life-threatening cardiac arrhythmias using the wavelet transformation. Medical and Biological Engineering and Computing, 35(6), pp.626--632.
  17. Langley, P., Dewhurst, M., Di Marco, L., Adams, P., Dewhurst, F., Mwita, J., Walker, R. and Murray, A. (2012). Accuracy of algorithms for detection of atrial fibrillation from short duration beat interval recordings. Medical Engineering & Physics, 34(10), pp.1441-1447.
  18. Mallat, S. and Zhong, S. (1992). Characterization of signals from multiscale edges. IEEE Transactions on pattern analysis and machine intelligence, 14(7), pp.710--732.
  19. Martis, R., Krishnan, M., Chakraborty, C., Pal, S., Sarkar, D., Mandana, K. and Ray, A. (2012). Automated screening of arrhythmia using wavelet based machine learning techniques. Journal of medical systems, 36(2), pp.677--688.
  20. Moody, G. and Mark, R. (1983). A new method for detecting atrial fibrillation using RR intervals. Computers in Cardiology, 10, pp.227-230.
  21. Moody, G. and Mark, R. (2001). The impact of the MITBIH arrhythmia database. Engineering in Medicine and Biology Magazine, IEEE, 20(3), pp.45--50.
  22. Park, J., Lee, S. and Jeon, M. (2009). Atrial fibrillation detection by heart rate variability in Poincare plot. BioMed Eng OnLine, 8(1), p.38.
  23. Prasad, G. and Sahambi, J. (2003). Classification of ECG arrhythmias using multi-resolution analysis and neural networks. 1, pp.227--231.
  24. Shen, C., Kao, W., Yang, Y., Hsu, M., Wu, Y. and Lai, F. (2012). Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines. Expert Systems with Applications, 39(9), pp.7845--7852.
  25. Silipo, R. and Marchesi, C. (1998). Artificial neural networks for automatic ECG analysis. Signal Processing, IEEE Transactions on, 46(5), pp.1417-- 1425.
  26. Tateno, K. and Glass, L. (2001). Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ?RR intervals. Med. Biol. Eng. Comput., 39(6), pp.664-671.
  27. Welch, P. (1967). The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics, 15(2), pp.70--73.
  28. Yang, T., Devine, B. and Macfarlane, P. (1994). Artificial neural networks for the diagnosis of atrial fibrillation. Medical and Biological Engineering and Computing, 32(6), pp.615--619.
  29. Ye, C., Kumar, B. and Coimbra, M. (2012). Heartbeat classification using morphological and dynamic features of ECG signals. Biomedical Engineering, IEEE Transactions on, 59(10), pp.2930--2941.
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Paper Citation


in Harvard Style

Batista D. and Fred A. (2015). Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 329-337. DOI: 10.5220/0005283403290337


in Bibtex Style

@conference{biosignals15,
author={Diana Batista and Ana Fred},
title={Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={329-337},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005283403290337},
isbn={978-989-758-069-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Spectral and Time Domain Parameters for The Classification of Atrial Fibrillation
SN - 978-989-758-069-7
AU - Batista D.
AU - Fred A.
PY - 2015
SP - 329
EP - 337
DO - 10.5220/0005283403290337