Granular representation of biomedical signals using numerical differentiation methods

• Autorzy: Momot M. , Momot A. , Gacek A.
• Języki publikacji: EN

Abstrakty

EN

This work presents the general idea of granular description of temporal signal, particularly biomedical signal sampled with constant frequency. The main idea of presented method is based on using triangular fuzzy numbers as information granules in temporal and amplitude spaces. The amplitude space contains values of first few derivatives of underlying signal. The construction of data granules is performed using the optimization method according to some objective function, which balances the high coverage ability and the low support of fuzzy numbers. The granules (descriptors) undergo the clustering process, namely fuzzy c-means. The centroids of created clusters form a granular vocabulary and the quality of description is quantitatively assessed by reconstruction criterion. There are presented results of experiments with the electrocardiographic signal, digitally sampled and stored in MIT-BIH database. The method of numerical differentiation of function based on finite set of its values is employed, which incorporates polynomial interpolation. The paper presents results of numerical experiments which show the impact of method parameters, such as temporal window length, degree of polynomial, fuzzification parameter, on the reconstruction ability of presented method.

Słowa kluczowe

EN

clustering; fuzzy number; information granulation; interpolation; reconstruction criterion

PL

grupowanie; liczby rozmyte; granulacja informacji; interpolacja; kryterium odbudowy

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2010
• Tom: Vol. 16
• Strony: 43--49
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Momot M.

• Institute of Medical Technology and Equipment ITAM, Roosevelt'a 118 Street, 41-800 Zabrze

autor

Momot A.

autor

Gacek A.

Bibliografia

• [1] BARGIELA A., PEDRYCZ W., Granular Computing: An Introduction, Kluwer Academic Publishers, Dordercht, 2003.
• [2] BEZDEK J., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
• [3] BRACI M., DIOP S., On numerical differentiation algorithms for nonlinear estimation, Proc. 42nd IEEE Conf. on Decision and Control, Maui, Hawaii USA, 2003, pp. 2896-2901.
• [4] BURDEN R.L., FAIRES J.D., Numerical Analysis, Brooks/Cole, 2000.
• [5] GACEK A., PEDRYCZ W., A Granular Description of ECG Signals, IEEE Trans. Biomed. Eng., Vol. 53, No. 10, 2006, pp. 1972-1982.
• [6] GARLAND M. et al., Parallel Computing Experiences with CUDA, IEEE Micro, Vol. 28, No. 4, 2008, pp. 13–27.
• [7] HATHAWAY R., BEZDEK J., HU Y., Generalized fuzzy c-means clustering strategies using Lp norm distances, IEEE Trans. Fuzzy Syst., Vol. 8, No. 5, 2000, pp. 576-582.
• [8] MARK R., MOODY G., MIT-BIH Arrhythmia Database Directory, MIT, Cambridge, MA, 1988.
• [9] MOODY G. B., MARK R. G., The MIT-BIH arrhythmia database on CD-ROM and software for use with it, Proc. Conf. Computers in Cardiology, San Diego, CA, 1990, pp. 185-188.
• [10] ORTOLANI M., HOFER H., et al., Fuzzy Information Granules in Time Series Data, Proc. IEEE Int. Conf. on Fuzzy Systems, 2002, pp. 695-699.
• [11] PEDRYCZ W., Fuzzy Sets as a User-Centric Processing Framework of Granular Computing. In: Pedrycz W., Skowron A. and Kreinovich V (eds), Handbook of Granular Computing, John Wiley & Sons, Chichester, 2008, pp. 97-139.
• [12] ZHANG Y., Advanced Differential Quadrature Methods, CRC Press, Boca Raton, 2009.