A Robust Extraction Algorithm Based on a Specific Kurtosis Value Range

  • Authors:

  • Yalan Ye;Zhi-Lin Zhang;Jia Chen;Duojiao Wu

  • Affiliations:

  • University of Electronic Science and Technology of China;University of Electronic Science and Technology of China;University of Electronic Science and Technology of China;University of Electronic Science and Technology of China

  • Venue:
  • ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
  • Year:
  • 2007

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Abstract

Independent component analysis (ICA), blind source separation (BSS) and related methods like blind source extraction (BSE) have been considered as a fundamental data analysis tool in the fields of neural network and signal processing. In this paper, we propose a robust algorithm based on a specific kurtosis value range that can extract a desired source signal as the first output signal with a specific kurtosis value range. That is to say, if we know that the kurtosis value of the desired signal generally lies in a specific range, while the values of other unwanted source signals do not belong to this range, we can use the proposed algorithm to extract the desired signal successfully. Moreover, the algorithm can work well in some poor situation (when the kurtosis values of some source signals are very close to each other). In addition, because of adopting an interior point penalty function method, the algorithm is robust to the estimation error of the kurtosis value range. Finally, we use the proposed algorithm to extract the accurate and reliable fetal electrocardiogram (FECG).