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Representation of ECG signals using the segmentation technique

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, we are concerned with the segmentation of ECG signals and the use of the resulting constructs (segments) in the development of a vocabulary of generic signal descriptors. The resulting space of the segmentation parameters is discussed in detail. It is shown how their representatives (prototypes) are constructed via fuzzy clustering. Numerical examples using the MIT-BIH database ECG signals are provided.
Twórcy
autor
  • Institute of Medical Technology and Equipment ITAM, Zabrze, Poland
autor
  • Institute of Medical Technology and Equipment ITAM, Zabrze, Poland
Bibliografia
  • [1] Bezdek J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, N. York 1981.
  • [2] Bortolan G„ Willems J.L.: Diagnostic ECG classification based on neural networks, Journal of Electrocardiology, 26, 1993, 75-79.
  • [3] Devine B. and Macfarlane P.W.: Detection of Electrocardiographic left ventricular strain using neural nets, Med. Biol. Eng. Comput, 31, 1993, 343-348.
  • [4] Edenbrandt L., Heden B„ Pahlm O.: Neural networks for analysis of ECG complexes, Journal of Electrocardiology 26, 1993, 74.
  • [5] Everitt B.S.: Cluster Analysis, Heinemann, Berlin, 1974.
  • [6] Ham F.M. and Han S.: Classification of cardiac arrhythmia using Fuzzy ARTMAP, IEEE Trans, on Biomedical Engineering, 43,4, 1996, 425-430.
  • [7] Horowitz S.L.: A syntactic algorithm for peak detection in waveforms with applications to cardiography, CACM, 18, 5, 1975, 281-285.
  • [8] Hoppner F, Klawonn F, Kruse R., Runkler T.: Fuzzy Cluster Analysis, J. Wiley, Chicester, 1999.
  • [9] Hu Y.H., Tompkins W.J., Urristi J.L. and Valtino X.A.: Application of artificial neural networks for ECG signal detection and classification, Journal of Electrocardiology, 26, 1993, 66-73.
  • [10] Kundu M., Nasipuri M., Basu D.K.: Knowledge-based ECG interpretation: a critical review, Pattern Recognition, 33, 2000, 351-373.
  • [11] Linnenbank A.C., Groenewegen A.S., Grimbergen C.A.: Artificial neural networks applied in multiple lead electrocardiograpy for rapid quantitative classification of ventricular tachycardia QRS integral patterns, In: Proc. of the Annual International Conference of the 12. IEEE Eng. of Med. and Biology Society, 12, pp. 1461,1990.
  • [12] MIT-BIH ECG Arrhythmia Database, Available Beth Israel Hospital, Biomedical Engineering Division Room, KB-26, 330 Brookline Ave., Boston MA 02215.
  • [13] Papakonstantinou G., Skordolakis E„ Grtazali F: A attribute grammar for QRS detection, Pattern Recognition, 19, 4, 1986, 297-303.
  • [14] Papakonstantinou G.: An interpreter of attribute grammars and its application to waveform analysis, IEEE Trans. Software Engineering, SE-7, 3, 1981, 279-283.
  • [15] Skordolakis E.: Syntactic ECG pattern processing: a review, Pattern Recognition, 19(4), 1986, 305-313.
  • [16] Trahanias P., Skordolakis E.: Syntactic pattern recognition of the ECG, IEEE Trans, on Pattern Analysis and Machine Intelligence, PAMI-12,7, 1990, 648-657.
  • [17] Udupa K„ Murphy I.S.N.: Syntactic approach to ECG rhythm analysis, IEEE Trans, on Biomedical Eng., BME-27, 7, 1980.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0003-0061
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