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Bayesian and empirical Bayesian approach to weighted averaging of ECG signal

Mamot A., Mamot M., Łęski J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2007

Vol. 55, nr 4

341-350

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.

Zastosowanie transformaty falkowej w klasyfikacji sygnałów EKG

Mamot A., Mamot M., Owczarek A.

Studia Informatica

2003

Vol. 24, nr 3

113-122

W artykule przedstawiono podstawowe informacje związane z dyskretną transformatą falkową oraz opisano jej zastosowanie w przypadku klasyfikacji patologicznych cykli w analizie sygnału EKG. Artykuł zawiera porównanie wyników klasyfikacji przeprowadzonych na tych samych próbkach z użyciem różnych funkcji falkowych oraz różnych klasyfikatorów.

The article presents the basic information about the discrete wavelet transformation and describes application of wavelet transformation to ECG signals classification into physiological and pathological cycles. The paper contains a comparison of results of classifications, taken on the same samples using different wavelet functions as well as different classificators.