Abstract
This paper describes a system for the analysis of the diagnostic electrocardiogram (ECG). The main emphasis is placed on the problems of ECG description, medical knowledge representation, clinical decision. In all these fields the use of fuzzy sets is a powerful tool to treat imprecision and vagueness. Electrocardiographic features are represented by fuzzy linguistic expressions; pathological classes are defined in terms of fuzzy descriptors; diagnostic classification is obtained by means of the composition of fuzzy relations. The result is (for all possible pathologies) a group of three fuzzy sets defined in the interval [0,1], each one representing the relative (fuzzy) degree of membership of the ECG under examination in one of the three classes: Normal, Abnormal, Borderline.
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References
Bailey, J.J., Itscoitz, S.B., Hirshfeld, Jr., J.W., Grauer, L.E., and Horton, M.R. (1974 ). A method for evaluating computer programs for electrocardiographic interpretation. I. Application to the experimental IBM program of 1971. Circulation 50, 73–79.
Bailey, J.J., Itscoitz, S.B., Hirshfeld, Jr., J.W., Grauer, L.E., and Horton, M.R. (1974). A method for evaluating computer programs for electrocardiographic interpretation. II. Application to version D of the PHS program and the Mayo Clinic program of 1968. Circulation 50, 80–87.
Bailey, J.J., Horton. M. and Itscoitz, S.B. (19T6). The importance of reproducibility testing of computer programs for electrocardiographic interpretation: application to the automatic vectorcardiographic analysis program (AVA 3.4). Comput. Biomed. Res. 9, 307–316.
Degani, R., and Pacini, G. (1978). Linguistic pattern recognition algorithms for computer analysis of ECG. Proc. BIOSIGMA 78, Paris, pp.18–26.
Dubois, D. and Prade, H. (1978). Operations on fuzzy numbers. Int. J. Systems Sci. 9, 613–626.
Dubois, D. and Prade, H. (1983). Unfair coins and necessity measures: towards a possibilistic interpretation of histograms. Fuzzy Sets and Systems 10, 15–20.
Jenkins, J.M. (1981). Computerized electrocardiography. CRC Critical Rev. Bioeng. 6, 307–350.
Macfarlane, P.W. (1979). A critical appraisal of computer assisted ECG interpretation. J. Med. Eng. & Technol. 3, 281–287.
Pipberger, H.V., McCaughan, D., Littmann, D., Pipberger, H.A., Cornfield J., Dunn, R.A., Batchlor, C.D., and Berson, A.S. (1975). Clinical application of a second generation ECG computer program. Am. J. Cardiol. 35, 597–608.
Taback, L., Marden, E., Mason, H.L. and Pipberger, H.V. (1959) Digital recording of ECG data for analysis by a digital computer. IRE Trans. Med. Elect. ME-6, 167–171.
Van Bemmel, J.H., and Willems, J.L., editors (1977). Trends in computer processed electrocardiograms. Amsterdam, North Holland.
Wartak, J. (1970). Computers in electrocardiography. C.C. Thomas, Springfield, 111.
Willems, J.L. et al. (the CSE European Working Party) (1985). Assessment of the performance of ECG computer programs with the use of a reference data base. Circulation 71, 523–534.
Zadeh, L.A. (1979). A theory of approximate reasoning. In: Machine Intelligence 9 (J.E. Hayes, D. Michie and L.I. Mikulich, eds.), pp. 149–194. Elsevier, New York.
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© 1986 D. Reidel Publishing Company
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Degani, R., Bortolan, G. (1986). Computerized Electrocardiography and Fuzzy Sets. In: Jones, A., Kaufmann, A., Zimmermann, HJ. (eds) Fuzzy Sets Theory and Applications. NATO ASI Series, vol 177. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-4682-8_15
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DOI: https://doi.org/10.1007/978-94-009-4682-8_15
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