An Introduction to the Use of Evolutionary Computation Techniques for Dealing with ECG Signals



Evolutionary Computation (EC) has become one of the most developed and successful computational intelligence techniques used for solving real-world problems from different application areas, including engineering, machine learning, signal processing, and data mining, among many others. It is indeed particularly worth noticing the success of the use of EC techniques for dealing with problems that involve the processing, classification, and interpretation of different sources of signals. From them, the treatment of ECG signals represents a challenge for the scientific community since such a problem has not only a high academic impact, but an important social impact, as well. In this chapter we present an introduction to basic EC concepts, including the description (under a unified perspective) of the most representative algorithms within this area. Furthermore, the chapter is aimed to provide the reader with the fundamentals of the most representative EC-based methodologies and other well-known bio-inspired metaheuristics that have been adopted for dealing with the treatment of ECG signals. Additionally, some areas for future research are also identified towards the end of the chapter.


Particle Swarm Optimization Discrete Wavelet Transform Radial Basis Function Neural Network Artificial Immune System Evolutionary Computation Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The second author acknowledges support from CONACyT project no. 103570.


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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Departmento de ComputaciónUMI LAFMIA 3175 CNRS at CINVESTAV-IPNMéxicoMéxico
  2. 2.LIDIC - Universidad Nacional de San LuisSan LuisArgentina
  3. 3.Departamento de ComputaciónCINVESTAV-IPN (Evolutionary Computation Group)MéxicoMéxico
  4. 4.UMI LAFMIA 3175 CNRS at CINVESTAV-IPNMéxicoMéxico

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