Soft Computing

, Volume 17, Issue 9, pp 1659–1671

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

Authors

    • Institute of Medical Technology and Equipment (ITAM)
  • Witold Pedrycz
    • Department of Electrical and Computer EngineeringUniversity of Alberta
    • Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz University
Methodologies and Application

DOI: 10.1007/s00500-012-0967-5

Cite this article as:
Gacek, A. & Pedrycz, W. Soft Comput (2013) 17: 1659. doi:10.1007/s00500-012-0967-5

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.

Keywords

Computational intelligenceBiomedical signalsNeurocomputingFuzzy setsInformation granulesGranular computingInterpretationClassificationSynergy

Copyright information

© Springer-Verlag Berlin Heidelberg 2013