نوع مقاله: مقاله کامل پژوهشی
نویسندگان
1 دانشجوی دکتری مهندسی پزشکی، گروه بیوالکتریک، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه صنعتی خواجه‌نصیرالدین طوسی، تهران
2 استادیار، گروه بیوالکتریک، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه صنعتی خواجه‌نصیرالدین طوسی، تهران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Marginalized particle extended Kalman filter (MP-EKF) takes advantage of both extended Kalman filter and particle filter frameworks to estimate nonlinear ECG dynamic models (EDMs) with reduced number of calculations in comparison to typical particle filters. However, due to existence of Kalman filter framework inside MP-EKF, some limitations are introduced in implementation of MP-EKF especially in embedded systems with finite numerical accuracies. In this paper, for the first time, we propose a square root filtering strategy for MP-EKF which alleviates these restrictions using factorization. Typical or other square-root Kalman filters cannot be employed inside MP-EKF due to presence of minus operations in some equations of MP-EKF. However, our method can be implemented in MP-EKF structure. The proposed method can be used in any EDM previously used by EKF based frameworks in the field of ECG processing.
کلیدواژه‌ها [English]
[1] R. Sameni, M. B. Shamsollahi, and C. Jutten, "Model-based Bayesian filtering of cardiac contaminants from biomedical recordings," Physiological Measurement, vol. 29, no. 5, pp. 595-613, May 2008.
[2] 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, no. 12, pp. 2172-2185, 2007.
[3] O. Sayadi and M. B. Shamsollahi, "ECG denoising and compression using a modified extended Kalman filter structure," IEEE Transactions on Biomedical Engineering, vol. 55, no. 9, pp. 2240-2248, 2008.
[4] 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, no. 3, pp. 289-294, 2003.
[5] H. Hesar and M. Mohebbi, "ECG Denoising Using Marginalized Particle Extended Kalman Filter with an Automatic Particle Weighting Strategy," IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 3, pp. 635-644, 2016.
[6] D. Simon, Optimal state estimation: Kalman, H infinity, and nonlinear approaches: John Wiley & Sons, 2006.
[7] G. Girija, J. Raol, R. A. Raj, and S. Kashyap, "Tracking filter and multi-sensor data fusion," SADHANA-BANGALORE-, vol. 25, no. 2, pp. 159-168, 2000.
[8] T. Kailath, A. H. Sayed, and B. Hassibi, Linear estimation vol. 1: Prentice Hall Upper Saddle River, NJ, 2000.
[9] M. L. Psiaki, "Square-root information filtering and fixed-interval smoothing with singularities," in Proc. American Control Conference, 1998. Proceedings of the 1998, 1998, pp. 2744-2748.
[10] H. D. Hesar and M. Mohebbi, "An Adaptive Particle Weighting Strategy for ECG Denoising Using Marginalized Particle Extended Kalman Filter: an Evaluation in Arrhythmia Contexts," IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 6, pp. 1581-1592, 2017.
[11] R. Sameni, M. B. Shamsollahi, C. Jutten, and G. D. Clifford, "A nonlinear Bayesian filtering framework for ECG denoising," IEEE Trans. Biomedical Engineering, vol. 54, no. 12, pp. 2172-2185, 2007.
[12] M. Akhbari, M. B. Shamsollahi, C. Jutten, A. A. Armoundas, and O. Sayadi, "ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations," Physiological measurement, vol. 37, no. 2, p. 203, 2016.
[13] G. Clifford, A. Shoeb, P. McSharry, and B. Janz, "Model-based filtering, compression and classification of the ECG," International Journal of Bioelectromagnetism, vol. 7, no. 1, pp. 158-161, 2005.
[14] O. Sayadi and M. Shamsollahi, "A model-based Bayesian framework for ECG beat segmentation," Physiological Measurement, vol. 30, no. 3, pp. 335-352, 2009.
[15] T. Schon, F. Gustafsson, and P.-J. Nordlund, "Marginalized particle filters for mixed linear/nonlinear state-space models," IEEE Transactions on Signal Processing, vol. 53, no. 7, pp. 2279-2289, 2005.
[16] G. H. Golub and C. F. Van Loan, Matrix computations vol. 3: JHU Press, 2012.
[17] S. Gibson and B. Ninness, "Robust maximum-likelihood estimation of multivariable dynamic systems," Automatica, vol. 41, no. 10, pp. 1667-1682, 2005.
[18] The MIT-BIH Normal Sinus Rhythm Database. PhysioNet,Cambridge,MA[Online]. Available:http://www.physionet.org/physiobank/data-base/nsrdb/
[19] The MT-BIH Noise Stress Test Database. PhysioNet, Cambridge, MA [Online]. Available:http://www.physionet.org/physiobank/data-base/nstdb/
[20] M. S. Manikandan and S. Dandapat, "Multiscale entropy-based weighted distortion measure for ECG coding," Signal Processing Letters, IEEE, vol. 15, pp. 829-832, 2008.
[21] M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, "Image coding using wavelet transform," IEEE Transactions on Image Processing, vol. 1, no. 2, pp. 205-220, 1992.
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