Empirical Bayesian averaging method and its application to noise reduction in ECG signal

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

Abstrakty

EN

An electrocardiogram (ECG) is the prime tool in non-invasive cardiac electrophysiology and has a prime function 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 empirical Bayesian approach to problem of signal averaging 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. In reality the variability of noise can be observed, with power from cycle to cycle, which is motivation for weighted averaging methods usage. It is demonstrated that by exploiting a probabilistic Bayesian learning framework, it can be derived accurate prediction models offering significant additional advantage, namely automatic estimation of 'nuisance' 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

PL

sygnał EKG; średnia ważona; wnioskowanie bayesowskie

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2006
• Tom: Vol. 10
• Strony: 93--101
• Opis fizyczny: Bibliogr. 9 poz., fig., tab.

Twórcy

autor

Momot A.

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

autor

Momot M.

autor

Łęski J.

Bibliografia

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• [3] CARLIN B., LOUIS T., Bayes and Empirical Bayes Methods for Data Analysis, Chapman & Hall, New York, 1996.
• [4] DUDA R., HART P., STORK D., Pattern Classification, John Wiley & Sons, Inc, New York, 2001.
• [5] FIGUEIREDO M., Adaptive Sparseness for Supervised Learning, IEEE Transaction on Pattern Analysis and Machine Learning, 25(9), pp. 1150-1159, 2003.
• [6] GELMAN A., CARLIN J., STERN H., RUBIN D., Bayesian Data Analysis, Chapman & Hall, New York, 2004.
• [7] ŁĘSKI J., Application of time domain signal averaging and Kalman filtering for ECG noise reduction, Ph.D. dissertation, Silesian University of Technology, Gliwice, 1989.
• [8] ŁĘSKI J., Robust Weighted Averaging, IEEE Transactions on Biomedical Engineering 49(8), pp. 796-804, 2002.
• [9] WHITE H., Estimation, Inference and Specification Analysis, Cambridge University Press, Cambridge, 1996.