Bayesian and empirical Bayesian approach to weighted averaging of ECG signal

• Autorzy: Mamot A. , Mamot M. , Łęski J.
• Języki publikacji: EN

Abstrakty

EN

One of the prime tool in non-invasive cardiac electrophysiology is the recording of an electrocardiographic signal (ECG) which analysis is greatly useful in the screening and diagnosis of cardiovascular diseases. However, one of the greatest problems is that usually recording an electrical activity of the heart is performed in the presence of noise. The paper presents Bayesian and empirical Bayesian approach to problem of weighted signal averaging in time domain which is commonly used to extract a useful signal distorted by a noise. The averaging is especially useful for biomedical signal such as ECG signal, where the spectra of the signal and noise significantly overlap. Using the methods of weighted averaging are motivated by variability of noise power from cycle to cycle, often observed in reality. It is demonstrated that exploiting a probabilistic Bayesian learning framework leads to accurate prediction models. Additionally, even in the presence of nuisance parameters the empirical Bayesian approach offers the method of theirs automatic estimation which reduces number of preset parameters. Performance of the new method is experimentally compared to the traditional averaging by using arithmetic mean and weighted averaging method based on criterion function minimization.

Słowa kluczowe

EN

ECG signal; weighted averaging; Bayesian inference

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2007
• Tom: Vol. 55, nr 4
• Strony: 341--350
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Mamot A.

autor

Mamot M.

autor

Łęski J.

• Silesian University of Technology, Institute of Computer Science, 16 Akademicka St., 44-101 Gliwice, Poland,

Bibliografia

• [1] B. Car1in and T. Louis, Bayes and Empirical Bayes Methods for Data Analysis, Chapman & Hall, New York, 1996.
• [2] A. Gelman, J. Carlin, H. Stern, and D. Rubin, Bayesian Data Analysis, Chapman & Hall, New York, 2004.
• [3] R. Duda, P. Hart, and D. Stork, Pattern Classification, John Wi1ey & Sons, Inc, New York, 2001.
• [4] J. Łęski, "Application of time domain signal averaging and Ka1man filtering for ECG noise reduction", PhD Thesis, Silesian University of Technology, G1iwice, 1989.
• [5] J. Łęski, "Robust weighted averaging", IEEE Transactions on Biomedical Engineering 49 (8), 796-804 (2002).
• [6] M. Figueiredo, "Adaptive sparseness for supervised learning", IEEE Transaction on Pattern Analysis and Machine Learning 25 (9), 1150-1159 (2003).
• [7] H. White, Estimation, Inference and Specification Analysis, Cambridge University Press, Cambridge, 1996.
• [8] A. Momot, M. Momot, and J. Łęski, "Empirical Bayesian averaging of biomedical signals", Proc. XI International Conference MIT 2006, 176-181 (2006).
• [9] R. Adler, R. Feldman, and M. Taqqu, A Practical Guide to Heavy Tails, Birkhauser, Boston, 1998.
• [10] P. Augustyniak, "Time-frequency modelling and discrimination of noise in the electrocardiogram", Physiological Measurement 24, 1-15 (2003).