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ECG Signal Processing, Classification and Interpretation pp 47–77Cite as

ECG Signal Analysis, Classification, and Interpretation: A Framework of Computational Intelligence

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Abstract

The study provides a general introduction to the principles, algorithms, and practice of Computational Intelligence (CI) and elaborates on those facets in relation with the ECG signal analysis. We discuss the main technologies of Computational Intelligence (namely, neural networks, fuzzy sets, and evolutionary optimization), identify their focal points, and stress an overall synergistic character, which ultimately gives rise to the highly symbiotic CI environment. Furthermore, the main advantages and limitations of the CI technologies are discussed. The design of information granules is elaborated on; their design realized on a basis of numeric data as well as pieces of domain knowledge is considered. Examples of the CI-based ECG signal processing problems are presented.

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References

  • Acharya, U.R., Bhat, P.S., Iyengar, S.S., Rao, A., Dua, S.: Classification of heart rate data using artificial neural network and fuzzy equivalence relation. Pattern Recognit. 36, 61–68 (2003)

    Article  MATH  Google Scholar 

  • Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic, Dordrecht (2002)

    Book  Google Scholar 

  • Bargiela, A., Pedrycz, W.: Recursive information granulation: aggregation and interpretation issues. IEEE Trans. Syst. Man Cybern. B 33(1), 96–112 (2003)

    Article  Google Scholar 

  • Barro, S., Ruiz, R., Mirai, J.: Fuzzy beat labeling for intelligent arrhythmia monitoring. Comput. Biomed. Res. 2, 240–258 (1981)

    Google Scholar 

  • Barro, S., Ruiz, R., Presedo, J., Mirai, J.: Grammatic representation of beat sequences for fuzzy arrhytmia diagnosis. Int. J. Biomed. Comput. 21, 245–259 (1991)

    Article  Google Scholar 

  • Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    MATH  Google Scholar 

  • Bezdek, J.C.: On the relationship between neural networks, pattern recognition and intelligence. Int. J. Approx. Reason. 6(2), 85–107 (1992)

    Article  Google Scholar 

  • Bezdek, J.C.: What is computational intelligence, In: Zurada, J.M., Marks II, R.J., Robinson, C.J. (eds.) Computational Intelligence Imitating Life, pp. 1–12. IEEE Press, Piscataway (1994)

    Google Scholar 

  • Chua, T.W., Tan, W.: Non-singleton genetic fuzzy logic system for arrhythmias classification. Eng. Appl. Artif. Intell. 24(2), 251–259 (2011)

    Article  Google Scholar 

  • Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  • Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, London (2005)

    Google Scholar 

  • Engin, M.: ECG beat classification using neuro-fuzzy network. Pattern Recognit. Lett. 25, 1715–1722 (2004)

    Article  Google Scholar 

  • Fei, S.W.: Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine. Exp. Syst. Appl. 37, 6748–6752 (2010)

    Article  Google Scholar 

  • Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic, New York (1990)

    MATH  Google Scholar 

  • Fulcher, J., Jain, L.C. (eds.): Computational Intelligence: A Compendium. Springer, Berlin (2008)

    MATH  Google Scholar 

  • Gacek, A., Pedrycz, W.: A genetic segmentation of ECG signals. IEEE Trans. Biomed. Eng. 50(10), 1203–1208 (2003)

    Article  Google Scholar 

  • Gacek, A., Pedrycz, W.: A granular description of ECG signals. IEEE Trans. Biomed. Eng. 53(10), 1972–1982 (2006)

    Article  Google Scholar 

  • Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading (1989)

    MATH  Google Scholar 

  • Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall Upper Saddle River (1999)

    Google Scholar 

  • Hirota, K.: Concepts of probabilistic sets. Fuzzy Sets Syst. 5(1), 31–46 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  • Hirota, K., Pedrycz, W.: Logic based neural networks. Inform. Sci. 71, 99 130 (1993)

    Google Scholar 

  • Hoppner, F., et al.: Fuzzy Cluster Analysis. Wiley, Chichester (1999)

    Google Scholar 

  • Klement, E., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Academic, Dordrecht (2000)

    MATH  Google Scholar 

  • Korurek, M., Dogan, B.: ECG beat classification using particle swarm optimization and radial basis function neural network. Exp. Syst. Appl. 37, 7563–7569 (2010)

    Article  Google Scholar 

  • Kundu, M., Nasipuri, M., Basu, D.K.: Knowledge-based ECG interpretation: a critical review. Pattern Recognit. 33, 351–373 (2000)

    Article  Google Scholar 

  • Lee, C.S., Wang, M.H.: Ontological fuzzy agent for electrocardiogram application. Exp. Syst. Appl. 35, 1223–1236 (2008)

    Article  Google Scholar 

  • Loia, V., Pedrycz, W., Senatore, S.: P-FCM: a proximity-based fuzzy clustering for user-centered web applications. Int. J. Approx. Reason. 34, 121–144 (2003)

    Article  MATH  Google Scholar 

  • Meau, Y.P., et al.: Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system. Comput. Methods Prog. Biomed. 8(2), 157–168 (2006)

    Article  Google Scholar 

  • Mitra, S., Mitra, M., Chaudhuri, B.B.: A rough-set-based inference engine for ECG classification. IEEE Trans. Instrum. Meas. 55(6), 2198–2206 (2006)

    Article  Google Scholar 

  • Moavenian, M., Khorrami, H.: A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification. Exp. Syst. Appl. 37, 3088–3093 (2010)

    Article  Google Scholar 

  • Moore, R.: Interval analysis. Prentice-Hall, Englewood Cliffs (1966)

    MATH  Google Scholar 

  • Mumford, C.L., Jain, L.C. (eds.): Computational Intelligence. Springer, Berlin (2009)

    MATH  Google Scholar 

  • Osowski, S., Markiewicz, T., Tran Hoai, L.: Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement 41, 610–617 (2008)

    Google Scholar 

  • Özbay, Y., Ceylan, R., Karlik, B.: Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Exp. Syst. Appl. 38, 1004–1010 (2011)

    Article  Google Scholar 

  • Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  • Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning About Data. Kluwer Academic, Dordercht (1991)

    MATH  Google Scholar 

  • Pawlak, Z., A. Skowron, rough sets and boolean reasoning. Inform. Sci. 177(1), 41–73 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Pedrycz, W.: Fuzzy sets in pattern recognition: methodology and methods. Pattern Recognit. 23(1–2), 121–146 (1990)

    Article  Google Scholar 

  • Pedrycz, W.: Computational Intelligence: An Introduction. CRC Press, Boca Raton (1997)

    MATH  Google Scholar 

  • Pedrycz, W.: Shadowed sets: representing and processing fuzzy sets. IEEE Trans. Syst. Man Cybern. Part B 28, 103–109 (1998)

    Article  Google Scholar 

  • Pedrycz, W.: Knowledge-Based Clustering: From Data to Information Granules. Wiley, Hoboken (2005)

    Book  MATH  Google Scholar 

  • Pedrycz, W., Bargiela, A.: Granular clustering: a granular signature of data. IEEE Trans. Syst. Man Cybern. 32(2), 212–224 (2002)

    Article  Google Scholar 

  • Pedrycz, W., Gacek, A.: Learning of fuzzy automata. Int. J. Comput. Intell. Appl. 1, 19–33 (2001)

    Article  Google Scholar 

  • Pedrycz, W., Gacek, A.: Temporal granulation and its application to signal analysis. Inform. Sci. 143(1–4), 47–71 (2002)

    Article  MATH  Google Scholar 

  • Pedrycz, W., Gomide, F.: Fuzzy Systems Engineering. Wiley, Hoboken (2007)

    Book  Google Scholar 

  • Pedrycz, W., Rocha, A.: Knowledge-based neural networks. IEEE Trans. Fuzzy Syst. 1, 254–266 (1993)

    Article  Google Scholar 

  • Pedrycz, W., Waletzky, J.: Neural network front-ends in unsupervised learning. IEEE Trans. Neural Netw. 8, 390–401 (1997a)

    Article  Google Scholar 

  • Pedrycz, W., Waletzky, J.: Fuzzy clustering with partial supervision. IEEE Trans. Syst. Man Cybern. 5, 787–795 (1997b)

    Google Scholar 

  • Presedo, J., et al.: Fuzzy modelling of the expert’s knowledge in ECG-based ischaemia detection. Fuzzy Sets Syst. 77, 63–75 (1996)

    Article  Google Scholar 

  • Yeh, Y.C., Wang, W.J., Chiou, C.W.: A novel fuzzy c-means method for classifying heartbeat cases from ECG signals. Measurement 43, 1542–1555 (2010)

    Article  Google Scholar 

  • Yeh, Y.C., Wang, W.J., Chiou, C.W.: Feature selection algorithm for ECG signals using range-overlaps method. Exp. Syst. Appl. 37, 3499–3512 (2010)

    Article  Google Scholar 

  • Wassermann, P.D.: Neural Computing: Theory and Practice. Van Nostrand, Reinhold, New York (1989)

    Google Scholar 

  • Zadeh, L.A.: Fuzzy sets. Inf. Control, 8, 338–353 (1965)

    MathSciNet  MATH  Google Scholar 

  • Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–117 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  • Zadeh, L.A.: Toward a generalized theory of uncertainty (GTU) – –an outline. Inf. Sci. 172, 1–40 (2005)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Institute of Medical Technology and Equipment, Zabrze, Poland

    Adam Gacek

  2. Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Canada

    Witold Pedrycz

  3. Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Adam Gacek
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  2. Witold Pedrycz
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Corresponding author

Correspondence to Adam Gacek .

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Editors and Affiliations

  1. Institute of Medical Technology and Equi, Roosevelta 118, Zabrze, 41-800, Poland

    Adam Gacek

  2. Dept. Electrical & Computer Engineering, University of Alberta, 116 Street 9107, Edmonton, T6G 2V4, Alberta, Canada

    Witold Pedrycz

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Gacek, A., Pedrycz, W. (2012). ECG Signal Analysis, Classification, and Interpretation: A Framework of Computational Intelligence. In: Gacek, A., Pedrycz, W. (eds) ECG Signal Processing, Classification and Interpretation. Springer, London. https://doi.org/10.1007/978-0-85729-868-3_3

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  • DOI: https://doi.org/10.1007/978-0-85729-868-3_3

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-867-6

  • Online ISBN: 978-0-85729-868-3

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