Soft Computing

, Volume 17, Issue 9, pp 1659–1671 | Cite as

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

Methodologies and Application


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.


Computational intelligence Biomedical signals Neurocomputing Fuzzy sets Information granules Granular computing Interpretation Classification Synergy 


  1. Mumford CL, Jain LC (eds) (2009) Computational intelligence. Springer, BerlinGoogle Scholar
  2. Fulcher J, Jain LC (eds) (2008) Computational intelligence: a compendium. Springer, BerlinGoogle Scholar
  3. 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 155–178Google Scholar
  4. Acampora G, Lee CS, Vitiello A, Wang MH (2012b) Evaluating cardiac health through semantic soft computing techniques. Soft Comput 16(7):1165–1181Google Scholar
  5. 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 36:61–68MATHCrossRefGoogle Scholar
  6. Bargiela A, Pedrycz W (2002) Granular computing: an introduction. Kluwer Academic Publishers, DordrechtGoogle Scholar
  7. Bargiela A, Pedrycz W (2003) Recursive information granulation: aggregation and interpretation issues. IEEE Trans Syst Man Cybern B 33(1):96–112CrossRefGoogle Scholar
  8. Bargiela A, Pedrycz W, Hirota K (2004) Granular prototyping in fuzzy clustering. IEEE Trans Fuzzy Syst 12(5):697–709CrossRefGoogle Scholar
  9. Barro S, Ruiz R, Mirai J (1981) Fuzzy beat labeling for intelligent arrhythmia monitoring. Comput Biomed Res 2:240–258Google Scholar
  10. Barro S, Ruiz R, Presedo J, Mirai J (1991) Grammatic representation of beat sequences for fuzzy arrhythmia diagnosis. Int J Biomed Comput 21:245–259CrossRefGoogle Scholar
  11. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkMATHCrossRefGoogle Scholar
  12. Bezdek JC (1992) On the relationship between neural networks, pattern recognition and intelligence. Int J Approx Reason 6(2):85–107CrossRefGoogle Scholar
  13. Bezdek JC (1994) What is computational intelligence. In: Robinson CJ, Zurada JM, Marks RJ II (eds) Computational Intelligence Imitating Life. IEEE Press, Piscataway, pp 1–12Google Scholar
  14. 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 39:2947–2955CrossRefGoogle Scholar
  15. Chua TW, Tan W (2011) Non-singleton genetic fuzzy logic system for arrhythmias classification. Eng Appl Artif Intell 24(2):251–259Google Scholar
  16. Dumont J, Hernandez AI, Carrault G (2010) Improving ECG beats delineation with an evolutionary optimization process. IEEE Trans Biomed Eng 57:607–615CrossRefGoogle Scholar
  17. Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, LondonGoogle Scholar
  18. Engin M (2004) ECG beat classification using neuro-fuzzy network. Pattern Recogn Lett 25:1715–1722CrossRefGoogle Scholar
  19. Fei SW (2010) Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine. Expert Syst Appl 37:6748–6752Google Scholar
  20. Gacek A, Pedrycz W (2003) A genetic segmentation of ECG signals. IEEE Trans Biomed Eng 50(10):1203–1208CrossRefGoogle Scholar
  21. Gacek A, Pedrycz W (2006) A granular description of ECG signals. IEEE Trans Biomed Eng 53(10):1972–1982CrossRefGoogle Scholar
  22. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, ReadingMATHGoogle Scholar
  23. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle RiverMATHGoogle Scholar
  24. Hirota K (1981) Concepts of probabilistic sets. Fuzzy Sets Syst 5(1):31–46MathSciNetMATHCrossRefGoogle Scholar
  25. Hoppner F et al (1999) Fuzzy cluster analysis. Wiley, ChichesterGoogle Scholar
  26. Kiranyaz S, Ince T, Pulkkinen J, Gabbouj M (2011) Personalized long-term ECG classification: a systematic approach. Expert Syst Appl 38:3220–3226CrossRefGoogle Scholar
  27. Korurek M, Dogan B (2010) ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Syst Appl 37:7563–7569CrossRefGoogle Scholar
  28. Kundu M, Nasipuri M, Basu DK (2000) Knowledge-based ECG interpretation: a critical review. Pattern Recogn 33:351–373CrossRefGoogle Scholar
  29. Lee CS, Wang MH (2008) Ontological fuzzy agent for electrocardiogram application. Expert Syst Appl 35:1223–1236CrossRefGoogle Scholar
  30. Loia V, Pedrycz W, Senatore S (2003) P-FCM: a proximity-based fuzzy clustering for user-centered web applications. Int J Approx Reason 34:121–144MATHCrossRefGoogle Scholar
  31. 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):157–168CrossRefGoogle Scholar
  32. Mitra S, Mitra M, Chaudhuri BB (2006) A rough-set-based inference engine for ECG classification. IEEE Trans Instrum Meas 55(6):2198–2206CrossRefGoogle Scholar
  33. Moavenian M, Khorrami H (2010) A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification. Expert Syst Appl 37:3088–3093CrossRefGoogle Scholar
  34. Moore R (1966) Interval analysis. Prentice-Hall, Englewood CliffsMATHGoogle Scholar
  35. Osowski S, Markiewicz T, Tran Hoai L (2008) Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement 41:610–617CrossRefGoogle Scholar
  36. 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 38:1004–1010CrossRefGoogle Scholar
  37. Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11:341–356MathSciNetMATHCrossRefGoogle Scholar
  38. Pawlak Z (1991) Rough sets. Theoretical aspects of reasoning about data. Kluwer Academic Publishers, DordrechtMATHGoogle Scholar
  39. Pawlak Z, Skowron A (2007) Rough sets and Boolean reasoning. Inf Sci 177(1):41–73MathSciNetMATHCrossRefGoogle Scholar
  40. Pedrycz W (1997) Computational intelligence: an introduction. CRC Press, Boca RatonMATHGoogle Scholar
  41. Pedrycz W (1998) Shadowed sets: representing and processing fuzzy sets. IEEE Trans Syst Man Cybern Part B 28:103–109Google Scholar
  42. Pedrycz W (2005) Knowledge-based clustering: from data to information granules. Wiley, HobokenCrossRefGoogle Scholar
  43. Pedrycz W, Bargiela A (2002) Granular clustering: a granular signature of data. IEEE Trans Syst Man Cybern 32(2):212–224CrossRefGoogle Scholar
  44. Pedrycz W, Bargiela A (2005) A model of granular data: a design problem with the Tchebyschev FCM. Soft Comput 9(3):155–163MATHCrossRefGoogle Scholar
  45. Pedrycz W, Gacek A (2001) Learning of fuzzy automata. Int J Comput Intell Appl 1:19–33CrossRefGoogle Scholar
  46. Pedrycz W, Gomide F (2007) Fuzzy systems engineering. Wiley, HobokenCrossRefGoogle Scholar
  47. Pedrycz W, Waletzky J (1997a) Neural network front-ends in unsupervised learning. IEEE Trans Neural Netw 8:390–401CrossRefGoogle Scholar
  48. Pedrycz W, Waletzky J (1997b) Fuzzy clustering with partial supervision. IEEE Trans Syst Man Cybern 5:787–795Google Scholar
  49. Presedo J et al (1996) Fuzzy modelling of the expert’s knowledge in ECG-based ischaemia detection. Fuzzy Sets Syst 77:63–75CrossRefGoogle Scholar
  50. Sufi F, Khalil I, Mahmood AB (2011) Clustering based system for instant detection of cardiac abnormalities from compressed ECG. Expert Syst Appl 38:4705–4713CrossRefGoogle Scholar
  51. 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 42:751–757CrossRefGoogle Scholar
  52. Wassermann PD (1989) Neural computing: theory and practice. Van Nostrand Reinhold, New YorkGoogle Scholar
  53. Yeh YC, Wang WJ, Chiou CW (2010a) A novel fuzzy c-means method for classifying heartbeat cases from ECG signals. Measurement 43:1542–1555CrossRefGoogle Scholar
  54. Yeh YC, Wang WJ, Chiou CW (2010b) Feature selection algorithm for ECG signals using range-overlaps method. Expert Syst Appl 37:3499–3512CrossRefGoogle Scholar
  55. Yeh YC, Chiou CW, Lin HJ (2012) Analyzing ECG for cardiac arrhythmia using cluster analysis. Expert Syst Appl 39:1000–1010CrossRefGoogle Scholar
  56. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353MathSciNetMATHCrossRefGoogle Scholar
  57. Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–117MathSciNetMATHCrossRefGoogle Scholar
  58. Zadeh LA (2005) Toward a generalized theory of uncertainty (GTU)—an outline. Inf Sci 172:1–40MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Medical Technology and Equipment (ITAM)ZabrzePoland
  2. 2.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

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