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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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Acknowledgments

Support from the Polish National Science Centre is gratefully acknowledged.

Author information

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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