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Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals

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DOI: 10.4236/jbise.2011.411087    4,911 Downloads   8,789 Views   Citations

ABSTRACT

Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate variability signal through the classical and time-frequency methods. At first, one minute of ECG signals, just before the cardiac death event are extracted and used to compute heart rate variability (HRV) signal. Five features in time domain and four features in frequency domain are extracted from the HRV signal and used as classical linear features. Then the Wigner Ville transform is applied to the HRV signal, and 11 extra features in the time-frequency (TF) domain are obtained. In order to improve the performance of classification, the principal component analysis (PCA) is applied to the obtained features vector. Finally a neural network classifier is applied to the reduced features. The obtained results show that the TF method can classify normal and SCD subjects, more efficiently than the classical methods. A MIT-BIH ECG database was used to evaluate the proposed method. The proposed method was implemented using MLP classifier and had 74.36% and 99.16% correct detection rate (accuracy) for classical features and TF method, respectively. Also, the accuracy of the KNN classifier were 73.87% and 96.04%.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Ebrahimzadeh, E. and Pooyan, M. (2011) Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals. Journal of Biomedical Science and Engineering, 4, 699-706. doi: 10.4236/jbise.2011.411087.

References

[1] Lin, J., et al. (2002) A few evidence in patient’s ECG shown high risk existed inducing syncope or SCD. Journal of Electrocardiolog, 21, 95-198.
[2] Chen, X. (2000) The patient with high risk of sudden cardiac death, The Chinese Journal of Arrhythmia, 4, 307-308.
[3] Smith, W.M. (1997) Cardiac Defibrillation. IEEE-EMBC and CMBEC, Montreal, 20-23 September, 249-250.
[4] Jones, J.L. and Tovar, O.H. (1996) The Mechanism of Defibrillation and Cardioversion. Proceedings of the IEEE, 84, 392-403. doi:10.1109/5.486742
[5] Shen, T.W., Shen, H.P, Lin, C. and Ou, Y. (2007) Detection and prediction of Sudden Cardiac Death (SCD) for personal healthcare. 29th Annual International Conference of the IEEE, Buenos Aires, 22-26 August 2007, 2575-2578.
[6] Fang, Z., Lai, D., Ge, X. and Wu, X. (2009) Successive ECG telemetry monitoring for preventing sudden cardiac death. IEEE Engineering in Medicine and Biology Society, 1738-1741.
[7] Ichimaru, Y., et al, (1988) Circadian changes of heart rate variability. Proceedings of Computers in Cardiology, Washington, DC, 25-28 September 1988, 315-318.
[8] VanHoogenhuyze, D., Martin, G., et al., (1989) Spectrum of heart rate variability. Proceedings of Computers in Cardiology, Chigaco, 13-16 September 1998, 65.
[9] Mrowka, R., Theres, H., et al., (1998) Alternans-like phenomena due to filtering of electrocardiographic data. Proceedings of Computers in Cardiology, Chigaco, 13-16 September 1998, 725-727.
[10] Meeting Report, Non-invasive test to predict sudden cardiac death proven effective. American Heart Association (AHA), 2006. http://www.heart.org/presenter,jhtml:identifier=3043452.
[11] Acharya, R., Kumar, A., Bhat, S., Lim, M., Lyengar, S., Kannathal, N. and Krishnan, S. M. (2004) Classification of cardiac abnormalities using heart rate signals. IEEE Engineering in Medicine and Biology Magazine, 42, 288- 293.
[12] Pan, J. and Tompkins, W.J. (1985) A real time QRS detection algorithm. IEEE Transaction on Biomedical Engineering, 32, 230-236. doi:10.1109/TBME.1985.325532
[13] Obayya, M. and Chadi, F.A. (2008) Data fusion for heart diseases classification using multi-layer feed forward neural Network, ICCES 2008 International Conference on Computer Engineering & Systems, Cairo, 25-27 November 2008, 67-70.
[14] Heart rate variability: Standards of measurements, physiological interpretation, and clinical use. Task force of the European society of cardiology and the North American society of pacing and electrophysiology, Circulation, 93, 354-381.
[15] Mohebbi, M. and Ghassemian, H. (2008) Detection of atrial fibrillation episodes using SVM. Conference Proceedings of the International Conference of IEEE Engineering in Medicine and Biology Society, 2008, 177-180.
[16] Martinm?ki, K., Rusko, H., Saalasti, S. and Kettunen, J. (2006) Ability of short-time Fourier transform method to detect transient changes invagal effects on hearts: a pharmacological blocking study. American Journal of Physiology, Heart and Circulatory Physiology, 290, H2582-H2589. doi:10.1152/ajpheart.00058.2005
[17] Keselbrener, L. andAkselrod, S. (1996) Selective discrete Fourier transform algorithm for time-frequency analysis: method and application on simulated and cardiovascular signals. IEEE Transactions on Biomedical Engineering, 43, 789-802. doi:10.1109/10.508542
[18] Toledo, E., Gurevitz, O., Hod, H., Eldar, M. and Akselrod, S. (2003) Wavelet analysis of instantaneous heart rate: A study of autonomic control during thrombolysis. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 284, R1079-R1091.
[19] Clari, F. Vallverdd, M., Baranowski, R., Chonowska, L., Martinez, P. and Caminal, P. (2000) Time-frequency representation of the HRV: A tool to characterize sudden cardiac death in hypertrophy cardiomyopathy patients. Proceedings of the 22nd Annual International Conference of the IEEE, 1, 71-73.
[20] Novak P. and Novak, V. (1993) Time/frequency mapping of the heart rate, blood pressure and respiratory signals. Medical and Biological Engineering and Computing, 31, 103-110. doi:10.1007/BF02446667
[21] Pola, S., Macerata, A., Emdin, M. and Marchesi, C. (1996) Estimation of the power spectral density in non-stationary cardiovascular time series: Assessing the role of the time-frequency representations (TFR). IEEE Transactions on Biomedical Engineering, 43, 46-59. doi:10.1109/10.477700
[22] Mainardi, L.T., Montano, N. and Cerutti, S. (2004) Automatic decomposition of Wigner distribution and its application to heart rate variability. Methods of Information in Medicine, 43, 17-21.
[23] Jasson, S., Medigue, C., Maison-Blanche, P., Montano, N., Meyer, L., Vermeiren, C., Mansier, P., Coumel, P., Malliani, A. and Swynghedauw, B. (1997) Instant power spectrum analysis of heart rate variability during orthostatic tilt using a time-frequency-domain method. Circulation. 96, 3521-3526.
[24] Bianchi, A.M., Mainardi, L.T., Meloni, C., Chierchia, S. and Cerutti, S. (1997) “Continuousmonitoring of the sympathovagal balance through spectral analysis. Recursive autoregressive techniques for tracking transient events in heart rate signals. IEEE Engineering in Medicine and Biology Magazine, 16, 64-73. doi:10.1109/51.620497
[25] Martin W. and Flandrin, P. (1985) Wigner-Ville spectral analysis of nonstationary processes. IEEE Transactions on Acoustics, Speech and Signal Processing, 33, 1461- 1470. doi:10.1109/TASSP.1985.1164760
[26] Baudat, G. and Anouar, F. (2000) Generalized discriminant analysis using a kernel approach. Neural Computation, 12, 2385-2404. doi:10.1162/089976600300014980

  
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