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Syntactic pattern recognition of ECG for diagnostic justification

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Języki publikacji
EN
Abstrakty
EN
A novel hybrid structural-parametric model for ECG diagnostic justification is presented in the paper. In order to distinguish between specific subclasses of heart dysfunction phenomena both grammars and automata are enhanced with a formalism of dynamic programming. It allows one to construct a system, which is feasible for aiding a process of teaching and evaluating medical students' diagnostic reasoning in the area of electrocardiography.
Rocznik
Strony
43--55
Opis fizyczny
Bibliogr. 36 poz., wykr.
Twórcy
  • IT Systems Department, Jagiellonian University, ul. St. Lojasiewicza 4, Cracow 30-384, Poland
Bibliografia
  • [1] Stallmann F.W., Pipberger H.V. : Automatic recognition of electrocardiographic waves by digital computer. Circ. Res., 9:1138-1143, 1961.
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  • [3] Horowitz S.L. : A syntactic algorithm for peak detection in waveforms with applications to cardiography. Comm. ACM, 18:281-285, 1975.
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  • [5] Gonzales R.C., Thomason M.G. : Syntactic Pattern Recognition: An Introduction. Addison-Wesley, Reading, 1978.
  • [6] Belferte G., De Mori R., Ferraris F. : A contribution to the automatic processing of electrocardiograms using syntactic methods. IEEE Trans. Biomed. Eng., 26:125-136, 1979.
  • [7] Udupa J., Murthy I.S.N. : Syntactic approach to ECG rhythm analysis. IEEE Trans. Biomed. Eng., 27:370-375, 1980.
  • [8] Fu K.S. : Syntactic Pattern Recognition and Applications. Prentice Hall, Englewood Cliffs, 1982.
  • [9] Bunke H.O. : Graph grammars as a generative tool in image understanding. Lecture Notes in Computer Science, 153:8-19, 1983.
  • [10] Pan J., Tompkins W.J. : A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng., 32:230-236, 1985.
  • [11] Papakonstantinou G., Skordalakis E., Gritzali F. : An attribute grammar for QRS detection. Pattern Recognition, 19:297-303, 1986.
  • [12] Patel V., Groen G. : Knowledge based solution strategies in medical reasoning. Cognitive Science, 10:91-116, 1986.
  • [13] Skordalakis E. : Syntactic ECG processing: a review. Pattern Recognition, 19:305-313, 1986.
  • [14] Bunke H.O., Sanfeliu A. (eds.) : Syntactic and Structural Pattern Recognition - Theory and Applications. World Scientic, Singapore, 1990.
  • [15] Trahanias P., Skordalakis E. : Syntactic pattern recognition of the ECG. IEEE Trans. Patt. Analysis Mach. Intell., 12:648-657, 1990.
  • [16] Piętka E. : Feature extraction in computerized approach to the ECG analysis. Pattern Recognition, 24:139-146, 1991.
  • [17] Koski A., Juhola M., Meriste M. : Syntactic recognition of ECG signals by attributed nite automata. Pattern Recognition, 28:1927-1940.
  • [18] Gilhooly K.J., McGeorge P., Hunter J. et al : Biomedical knowledge in diagnostic thinking: the case of electrocardiogram (ECG) interpretation. European Journal of Cognitive Psychology, 9:199-223, 1997.
  • [19] Kolykhalov I.V., Iznak, A.F., Chayanov N.V. et al : EEG mapping in diagnostic assessment of patients with mild dementia. European Neuropsychopharmacology, 7:248, 1997.
  • [20] Barro S., Fernandez-Delgado M., Villa-Sobrino J.A., Regueiro C.V., Sanchez E. : Classifying multichannel ECG patterns with an adaptive neural network. IEEE Eng. Med. Biol. Mag., 17:45-55, 1998.
  • [21] Maglaveras N., Stamkopoulos T., Diamantaras K., Pappas C., Strintzis M. : ECG pattern recognition and classication using non-linear transforms and neural networks: a review. Int. J. Med. Inform., 52:191-208, 1998.
  • [22] Flasiński M., Jurek J. : Dynamically programmed automata for quasi context sensitive languages asa tool for inference support in pattern recognition-based real-time control expert systems. Pattern Recognition, 32:671-690, 1999.
  • [23] Osowski S., Linh T.H. : ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng., 48:1265-1271, 2001.
  • [24] Sternickel K. : Automatic pattern recognition in ECG time series. Comp. Meth. Programs in Biomedicine, 68:109-115, 2002.
  • [25] Tumer M.B., Belfore L.A., Ropella K.M. : A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations. IEEE Trans. on Syst. Man Cybern., 33:951-965, 2003.
  • [26] Engin M. : ECG beat classication using neuro-fuzzy network. Patt. Rec. Lett., 25:1715-1722, 2004.
  • [27] Flasiński M., Reroń E., Wójtowicz P., Atlasiewicz K. : Mathematical linguistics model for medical diagnostics of organ hearing in neonates, Lecture Notes in Computer Science (LNAI), 3019:746-753, 2004.
  • [28] Tadeusiewicz R., Ogiela M.R. : Medical Image Understanding Technology. Springer, Berlin-Heidelberg-New York, 2004.
  • [29] Flasiński M., Jurek J. : On the analysis of fuzzy string patterns with the help of extended and stochastic GDPLL(k) grammars. Fundamenta Informaticae, 71:1-14, 2006.
  • [30] Dong J., Xu S., Zhan C. : ECG recognition and classication: approaches, problems and new method. J. Biomed. Eng., 24:1224-1229, 2007.
  • [31] McLaughlin K.J. : The Contribution of Analytic Information Processing to Diagnostic Performance in Medicine, Ph.D. Thesis, Erasmus University Rotterdam, 2007.
  • [32] Martis R.J., Chakraborty C., Ray A.K. : A two-stage mechanism for registration and classication of ECG using Gaussian mixture model. Pattern Recognition, 42:2979-2988, 2009.
  • [33] Noponen K., Kortelainen J., Seppanen T. : Invariant trajectory classication of dynamical systems with a case study on ECG. Pattern Recognition, 42:1832-1844, 2009.
  • [34] Cianciolo A.T., Williams R.G., Klamen D.L., Roberts N.K. : Biomedical knowledge, clinical cognition and diagnostic justication: a structural equation model. Medical Education, 47:309-316, 2013.
  • [35] Flasiński M., Flasiński P., Konduracka E. : On the use of programmed automata for verication of ECG diagnoses. Advances in Intelligent Systems and Computing 226, Springer, Berlin-Heidelberg-New York, 591-599, 2013.
  • [36] Williams R.G., Klamen D.L., Markwell S.J. et al : Variations in senior medical studet diagnostic justication ability. Academic Medicine, 89:790-798, 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-167fc0a3-6374-4b86-8ea7-30b7ab3e1dba
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