Early Detection of Sudden Cardiac Death in Electrocardiogram Signals Using Extended Kalman Filter

Kahroba, Farin; Mohebbi, Maryam; Danandeh Hesar, Hamed

doi:10.22041/ijbme.2018.82463.1332

Document Type : Full Research Paper

Authors

¹ Msc Student, Department of Biomedical Engineering, K. N. Toosi University of Technology, Tehran, Iran

² Assistant Professor, Department of Biomedical Engineering, K. N. Toosi University of Technology, Tehran, Iran

³ Phd Student, Department of Biomedical Engineering, K. N. Toosi University of Technology, Tehran, Iran

https://doi.org/10.22041/ijbme.2018.82463.1332

Abstract

Sudden cardiac death (SCD) is one of the most significant and common causes of heart related deaths around the world. It is believed that SCD can be predicted using signatures and features extracted from ECG signal. These signatures may be seen as arrhythmia or abnormalities in the ECG signal. In this paper, a monitoring index is introduced for early detection of SCD. This index is acquired by filtering the ECG signal using a nonlinear ECG dynamical model and extended Kalman filter (EKF). The nonlinear dynamical model was a modified version of polar ECG dynamical model proposed by Mc. Sharry et.al. In our algorithm, first the ECG dynamical model is extracted. Then an EKF is applied on the signal. Using the fidelity index extracted from the innovation signal yielded by EKF, a novel algorithm detects the SCD related arrhythmias and abnormalities. The proposed method was evaluated on Physionet Sudden Cardiac Death Holter database. Twenty records corresponding to patients having SCD and eighteen records corresponding to healthy patients were extracted from this database. The evaluation results showed that our proposed monitoring index correctly detected 17 SCDs out of 20 (85% accuracy).

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

References

[1] G. I. Fishman, S. S. Chugh, J. P. DiMarco, C. M. Albert, M. E. Anderson, R. O. Bonow, et al., "Sudden cardiac death prediction and prevention report from a National Heart, Lung, and Blood Institute and Heart Rhythm Society workshop," Circulation, vol. 122, pp. 2335-2348, 2010.

[2] C. Sandroni, G. Ferro, S. Santangelo, F. Tortora, L. Mistura, F. Cavallaro,A. Caricato, and M. Antonelli, "In-hospital cardiac arrest: survival depends mainly on the effectiveness of the emergency response", Resuscitation, vol. 62, pp.291-297, 2004.

[3] S. Suraseranivongse, T. Chawaruechai, P. Saengsung, and C. Komoltri, "Outcome of cardiopulmonary resuscitation in a 2300-bed hospital in a developing country", Resuscitation, Vol.71, pp.188–193, 2006.

[4] Shen, Tsu-Wang, and et al. "A personal Sudden Cardiac Death (SCD) detector based on ECG biometric technology." World Congress on Medical Physics and Biomedical Engineering 2006. Springer Berlin Heidelberg, 2007.

[5] T.-W. Shen, H.-P. Shen, C.-H. Lin, and Y.-L. Ou, "Detection and prediction of sudden cardiac death (SCD) for personal healthcare," in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2575-2578,2007.

[6] H. Yu, F. Pi-hua, W. Yuan, L. Xiao-feng, L. Jun, L. Zhi, et al., "Prediction of sudden cardiac death in patients after acute myocardial infarction using T-wave alternans: a prospective study," Journal of electrocardiology, vol. 45, pp. 60-65, 2012.

[7] Fujita, Hamido, et al. "Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index." Applied Soft Computing, vol. 43, pp. 510-519, 2016.

[8] P. E. McSharry, G. D. Clifford, L. Tarassenko, and L. A. Smith, "A dynamical model for generating synthetic electrocardiogram signals," IEEE Transactions on Biomedical Engineering, vol. 50, pp. 289-294, 2003.

[9] R. Sameni, M. B. Shamsollahi, C. Jutten, and G. D. Clifford, "A nonlinear Bayesian filtering framework for ECG denoising," IEEE Transactions on Biomedical Engineering, vol. 54, pp. 2172-2185, 2007.

[10] O. Sayadi and M. B. Shamsollahi, "ECG denoising and compression using a modified extended Kalman filter structure," IEEE Transactions on Biomedical Engineering, vol. 55, pp. 2240-2248, 2008.

[11] O. Sayadi and M. Shamsollahi, "A model-based Bayesian framework for ECG beat segmentation," Physiological measurement, vol. 30, p. 335, 2009.

[12] O. Sayadi, M. B. Shamsollahi, and G. D. Clifford, "Robust detection of premature ventricular contractions using a wave-based Bayesian framework," IEEE Transactions on Biomedical Engineering, vol. 57, pp. 353-362, 2010.

[13] Sameni, Reza, et al. "Filtering noisy ECG signals using the extended Kalman filter based on a modified dynamic ECG model." Computers in Cardiology, 2005. IEEE, 2005.

[14] J. Pan, W. J. Tompkins, "A real-time qrs detection algorithm,"

[15] IEEE transactions on biomedical engineeringو vol. 32, no. 3, pp. 230-6, 1985.

[16] Akhbari, Mahsa, Mohammad B. Shamsollahi, and Christian Jutten. "ECG fiducial points extraction by extended kalman filtering." Telecommunications and Signal Processing (TSP), 2013 36th International Conference on. IEEE, 2013.‏

[17] O. Sayadi, " Model-Based ECG Processing (Denoising, Compression and Classification)" ,MS Thesis,Sharif University of Technology, Tehran Iran, 200

Iranian Journal of Biomedical Engineering

Early Detection of Sudden Cardiac Death in Electrocardiogram Signals Using Extended Kalman Filter

References

References

Volume 11, Issue 2
June 2017
Pages 187-199

Early Detection of Sudden Cardiac Death in Electrocardiogram Signals Using Extended Kalman Filter

References

References

Volume 11, Issue 2June 2017Pages 187-199

Volume 11, Issue 2
June 2017
Pages 187-199