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Description, analysis, and classification of biomedical signals: a computational intelligence approach

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An Erratum to this article was published on 21 July 2015

Abstract

This study provides a general introduction to the principles, algorithms and practice of computational intelligence (CI) and elaborates on those facets with relation to biomedical signal analysis, especially ECG signals. We discuss the main technologies of computational intelligence (namely, neural networks, fuzzy sets or granular computing, 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. In the sequel, we present CI-oriented constructs in signal modeling, classification, and interpretation. Examples of the CI-based ECG signal processing problems are presented.

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References

  • Mumford CL, Jain LC (eds) (2009) Computational intelligence. Springer, Berlin

  • Fulcher J, Jain LC (eds) (2008) Computational intelligence: a compendium. Springer, Berlin

  • Acampora G, Lee CS, Wang MH, Loia V (2012a) Electrocardiogram application based on heart rate variability ontology and fuzzy markup language. In: Gacek A, Pedrycz W (eds) ECG signal processing, classification and interpretation. Springer, Heidelberg, pp 155–178

    Google Scholar 

  • Acampora G, Lee CS, Vitiello A, Wang MH (2012b) Evaluating cardiac health through semantic soft computing techniques. Soft Comput 16(7):1165–1181

    Google Scholar 

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

    Article  MATH  Google Scholar 

  • Bargiela A, Pedrycz W (2002) Granular computing: an introduction. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

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

    Article  Google Scholar 

  • Bargiela A, Pedrycz W, Hirota K (2004) Granular prototyping in fuzzy clustering. IEEE Trans Fuzzy Syst 12(5):697–709

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Book  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Bezdek JC (1994) What is computational intelligence. In: Robinson CJ, Zurada JM, Marks RJ II (eds) Computational Intelligence Imitating Life. IEEE Press, Piscataway, pp 1–12

    Google Scholar 

  • Castillo O, Melin P, Ramírez E, Soria J (2012) Hybrid intelligent system for cardiac arrhythmia classification with fuzzy K-Nearest Neighbors and neural networks combined with a fuzzy system. Expert Syst Appl 39:2947–2955

    Article  Google Scholar 

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

    Google Scholar 

  • Dumont J, Hernandez AI, Carrault G (2010) Improving ECG beats delineation with an evolutionary optimization process. IEEE Trans Biomed Eng 57:607–615

    Article  Google Scholar 

  • Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, London

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading

    MATH  Google Scholar 

  • Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Hoppner F et al (1999) Fuzzy cluster analysis. Wiley, Chichester

    Google Scholar 

  • Kiranyaz S, Ince T, Pulkkinen J, Gabbouj M (2011) Personalized long-term ECG classification: a systematic approach. Expert Syst Appl 38:3220–3226

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lee CS, Wang MH (2008) Ontological fuzzy agent for electrocardiogram application. Expert Syst Appl 35:1223–1236

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Pawlak Z (1991) Rough sets. Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht

    MATH  Google Scholar 

  • Pawlak Z, Skowron A (2007) Rough sets and Boolean reasoning. Inf Sci 177(1):41–73

    Article  MathSciNet  MATH  Google Scholar 

  • Pedrycz W (1997) Computational intelligence: an introduction. CRC Press, Boca Raton

    MATH  Google Scholar 

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

    Google Scholar 

  • Pedrycz W (2005) Knowledge-based clustering: from data to information granules. Wiley, Hoboken

    Book  Google Scholar 

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

    Article  Google Scholar 

  • Pedrycz W, Bargiela A (2005) A model of granular data: a design problem with the Tchebyschev FCM. Soft Comput 9(3):155–163

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Pedrycz W, Gomide F (2007) Fuzzy systems engineering. Wiley, Hoboken

    Book  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • Sufi F, Khalil I, Mahmood AB (2011) Clustering based system for instant detection of cardiac abnormalities from compressed ECG. Expert Syst Appl 38:4705–4713

    Article  Google Scholar 

  • Sun Y, Cheng AC (2012) Machine learning on-a-chip: a high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications. Comput Biol Med 42:751–757

    Article  Google Scholar 

  • Wassermann PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold, New York

    Google Scholar 

  • Yeh YC, Wang WJ, Chiou CW (2010a) A novel fuzzy c-means method for classifying heartbeat cases from ECG signals. Measurement 43:1542–1555

    Article  Google Scholar 

  • Yeh YC, Wang WJ, Chiou CW (2010b) Feature selection algorithm for ECG signals using range-overlaps method. Expert Syst Appl 37:3499–3512

    Article  Google Scholar 

  • Yeh YC, Chiou CW, Lin HJ (2012) Analyzing ECG for cardiac arrhythmia using cluster analysis. Expert Syst Appl 39:1000–1010

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

Support from the Polish National Science Centre is gratefully acknowledged.

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Authors and Affiliations

  1. Institute of Medical Technology and Equipment (ITAM), 118 Roosevelt Street, Zabrze, 41-800, Poland

    Adam Gacek

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

    Witold Pedrycz

  3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Witold Pedrycz

Authors
  1. Adam Gacek
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  2. Witold Pedrycz
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Corresponding author

Correspondence to Adam Gacek.

Additional information

Communicated by G. Acampora.

An erratum to this article is available at http://dx.doi.org/10.1007/s00500-015-1785-3.

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Gacek, A., Pedrycz, W. Description, analysis, and classification of biomedical signals: a computational intelligence approach. Soft Comput 17, 1659–1671 (2013). https://doi.org/10.1007/s00500-012-0967-5

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