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Adaptable Noise Reduction of ECG Signals for Feature Extraction

  • Hyun Dong Kim
  • Chul Hong Min
  • Tae Seon Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

Conventionally, ECG signal is measured on static condition since various types of noise including muscle artifact noise and electrode moving artifact noise are in coupled in dynamic environment. To solve this problem, various noised signals are grouped into six categories by context estimation, and effectively reconfigured noise reduction filter by neural network and genetic algorithm (GA). Neural network based control module effectively select optimal filter block by noise context based clustering at running mode and filtering performance was improved by GA at evolution mode. Experimental results showed that proposed algorithm effectively remove baseline wander noise and muscle noise and feature extraction results showed significant improvement of T duration extraction values.

Keywords

Finite Impulse Response Decision Module Impulsive Noise Artifact Noise Noise Reduction Algorithm 
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.

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References

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    Pancer, T.P.: A Suppression of an Impulsive Noise in ECG Signal Processing. In: Proc. 26th Annual Int’l. Conf. IEEE EMBS, pp. 596–599 (2004)Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyun Dong Kim
    • 1
  • Chul Hong Min
    • 1
  • Tae Seon Kim
    • 1
  1. 1.School of Information, Communications and Electronics EngineeringCatholic University of KoreaBucheonKorea

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