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Wavelet Denoising as Preprocessing Stage to Improve ICA Performance in Atrial Fibrillation Analysis

  • César Sánchez
  • José Joaquín Rieta
  • Carlos Vayá
  • David Moratal Perez
  • Roberto Zangróniz
  • José Millet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3889)

Abstract

Blind Source Separation (BSS) has been probed as one of the most effective techniques for atrial activity (AA) extraction in supraventricular tachyarrhythmia episodes like atrial fibrillation (AF). In these situations, a wavelet transform denoising stage can improve the extraction quality with low computational cost. Each ECG lead is processed to obtain its representation in the wavelet domain where the BSS systems improve their performance. The comparison of spectral parameters (main peak and power spectral density concentration) and statistics values (kurtosis) proves that the sparse decomposition in the wavelet domain of the observed mixtures reduces Gaussian contamination of these signals, speeds up the convergence and increase the quality of the extracted signal. The easy and fast implementation, robustness and efficiency are some of the main advantages of this technique making possible the application in real time systems as a support tool to clinical diagnostics.

Keywords

Independent Component Analysis Atrial Activity Wavelet Domain Preprocessing Stage Atrial Fibrillation Episode 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • César Sánchez
    • 1
  • José Joaquín Rieta
    • 2
  • Carlos Vayá
    • 2
  • David Moratal Perez
    • 2
  • Roberto Zangróniz
    • 1
  • José Millet
    • 2
  1. 1.Innovation in BioengineeringCastilla-La Mancha UniversityCuencaSpain
  2. 2.Bioengineering, Electronics and TelemedicineValencia University of TechnologyGandíaSpain

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