Characteristic points detection in ECG signal using Bayesian learning and fuzzy system

• Autorzy: Momot M. , Momot A.
• Języki publikacji: PL

Abstrakty

EN

Characteristic points detection such as beginnings and ends of P-wave, T-wave or QRS complex is one of primary aims in automated analysis of ECG signal. The paper presents one possible approach based on Bayesian inference to design of kernel based classifier. The classification function is constructed using the probability distribution function of standard normal distribution and independent Gaussian random variables. The parameters of such variables are computed using iterative Expectation-Maximization algorithm. This approach is used to calculate parameters of classification function to modelling Takagi-Sugeno-Kang fuzzy systems. Numerical experiment of characteristic points detection in ECG signal using CTS database is also presented.

Słowa kluczowe

PL

sygnał EKG; systemy rozmyte

EN

ECG signal; fuzzy systems; Bayesian learning

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2007
• Tom: Vol. 11
• Strony: 171--176
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Momot M.

• Institute of Medical Technology and Equipment, 118 Roosevelt St., 41-800 Zabrze, Poland

autor

Momot A.

Bibliografia

• [1] AUGUSTYNIAK P., Time–frequency modelling and discrimination of noise in the electrocardiogram, Physiological Measurement, 24, pp. 1–15, 2003.
• [2] FIGUEIREDO M., Adaptive Sparseness for Supervised Learning, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 25, No. 9, pp. 1150-1159, 2003.
• [3] FIGUEIREDO M., JAIN A.K., Bayesian Learning of Sparse Classifiers, IEEE Computer Society Conference on Computer Vision and Pattern Recognition - CVPR'2001, Hawaii, 2001.
• [4] SCHAMROTH L., An introduction to electrocardiography. Oxford Press, 1986.
• [5] SUGENO M., KANG G.T., Structure identification of fuzzy model, Fuzzy Sets and Systems, Vol. 28, pp. 15-33, 1988.
• [6] TAKAGI T., SUGENO M., Fuzzy identification of systems and its application to modelling and control, IEEE Trans. on System, Man and Cybernetics, Vol. 15, No. 1, pp. 116-132, 1985.
• [7] VAPNIK V.N., The nature of statistical learning theory, Springer, New York, 1995.
• [8] International Electrotechnical Commission Standard 60601-3-2, 1999.