Skip to main content

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 226))

Abstract

The recent results into constructing a formal model of a syntactic pattern recognition-based System for Teaching ElectroCardioGraphy (STECG) are presented. A class of programmed attributed regular grammars (PARG) is defined as a formal tool for a generation of ECG patterns. A programmed attributed finite-state automaton (PAFSA) is introduced for an analysis of ECG patterns. PAFSA is a basic formalism for a development of the STECG system.

A preliminary phase of the research has been made by the author at the Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Al. Mickiewicza 30, Cracow 30-059, Poland.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. Bunke, H.O.: Graph grammars as a generative tool in image understanding. In: Ehrig, H., Nagl, M., Rozenberg, G. (eds.) Graph Grammars 1982. LNCS, vol. 153, pp. 8–19. Springer, Heidelberg (1983)

    Chapter  Google Scholar 

  4. Bunke, H.O., Sanfeliu, A. (eds.): Syntactic and Structural Pattern Recognition - Theory and Applications. World Scientific, Singapore (1990)

    MATH  Google Scholar 

  5. Dong, J., Xu, S., Zhan, C.: ECG recognition and classification: approaches, problems and new method. J. Biomed. Eng. 24, 1224–1229 (2007)

    Google Scholar 

  6. Engin, M.: ECG beat classification using neuro-fuzzy network. Patt. Rec. Lett. 25, 1715–1722 (2004)

    Article  Google Scholar 

  7. Flasiński, M., Jurek, J.: Dynamically programmed automata for quasi context sensitive languages as a tool for inference support in pattern recognition-based real-time control expert systems. Pattern Recognition 32, 671–690 (1999)

    Article  Google Scholar 

  8. Flasiński, M., Reroń, E.z., Jurek, J., Wójtowicz, P., Atłasiewicz, K.: Mathematical linguistics model for medical diagnostics of organ of hearing in neonates. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2004. LNCS (LNAI), vol. 3019, pp. 746–753. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. 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)

    MathSciNet  MATH  Google Scholar 

  10. Fu, K.S.: Syntactic Pattern Recognition and Applications. Prentice Hall, Englewood Cliffs (1982)

    MATH  Google Scholar 

  11. Gonzales, R.C., Thomason, M.G.: Syntactic Pattern Recognition: An Introduction. Addison-Wesley, Reading (1978)

    Google Scholar 

  12. Horowitz, S.L.: A syntactic algorithm for peak detection in waveforms with applications to cardiography. Comm. ACM 18, 281–285 (1975)

    Article  MATH  Google Scholar 

  13. Koski, A., Juhola, M., Meriste, M.: Syntactic recognition of ECG signals by attributed finite automata. Pattern Recognition 28, 1927–1940 (1995)

    Article  Google Scholar 

  14. Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., Strintzis, M.: ECG pattern recognition and classification using non-linear transforms and neural networks: a review. Int. J. Med. Inform. 52, 191–208 (1998)

    Article  Google Scholar 

  15. Martis, R.J., Chakraborty, C., Ray, A.K.: A two-stage mechanism for registration and classification of ECG using Gaussian mixture model. Pattern Recognition 42, 2979–2988 (2009)

    Article  MATH  Google Scholar 

  16. Noponen, K., Kortelainen, J., Seppanen, T.: Invariant trajectory classification of dynamical systems with a case study on ECG. Pattern Recognition 42, 1832–1844 (2009)

    Article  MATH  Google Scholar 

  17. Osowski, S., Linh, T.H.: ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48, 1265–1271 (2001)

    Article  Google Scholar 

  18. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32, 230–236 (1985)

    Article  Google Scholar 

  19. Pavlidis, T.: Structural Pattern Recognition. Springer, New York (1977)

    MATH  Google Scholar 

  20. Papakonstantinou, G., Skordalakis, E., Gritzali, F.: An attribute grammar for QRS detection. Pattern Recognition 19, 297–303 (1986)

    Article  Google Scholar 

  21. Piętka, E.: Feature extraction in computerized approach to the ECG analysis. Pattern Recognition 24, 139–146 (1991)

    Article  Google Scholar 

  22. Rosenkrantz, D.J.: Programmed grammars and classes of formal languages. Journal of the Association for Computing Machinery 16, 107–131 (1969)

    Article  MathSciNet  MATH  Google Scholar 

  23. Skordalakis, E.: Syntactic ECG processing: a review. Pattern Recognition 19, 305–313 (1986)

    Article  Google Scholar 

  24. Stallmann, F.W., Pipberger, H.V.: Automatic recognition of electrocardiographic waves by digital computer. Circ. Res. 9, 1138–1143 (1961)

    Article  Google Scholar 

  25. Sternickel, K.: Automatic pattern recognition in ECG time series. Comp. Meth. Programs in Biomedicine 68, 109–115 (2002)

    Article  Google Scholar 

  26. Tadeusiewicz, R., Ogiela, M.R.: Medical Image Understanding Technology. Springer, Heidelberg (2004)

    Book  MATH  Google Scholar 

  27. Trahanias, P., Skordalakis, E.: Syntactic pattern recognition of the ECG. IEEE Trans. Patt. Analysis Mach. Intell. 12, 648–657 (1990)

    Article  Google Scholar 

  28. 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)

    Article  Google Scholar 

  29. Udupa, J., Murthy, I.S.N.: Syntactic approach to ECG rhythm analysis. IEEE Trans. Biomed. Eng. 27, 370–375 (1980)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mariusz Flasiński .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Flasiński, M., Flasiński, P., Konduracka, E. (2013). On the Use of Programmed Automata for a Verification of ECG Diagnoses. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-00969-8_58

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00968-1

  • Online ISBN: 978-3-319-00969-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics