Flexible Linear Discriminant Wavelet Networks for Rapid Physiological Signal Interpretation

  • Bing Nan Li
  • You Li
  • Kui Xiang
  • Ning An
  • M. C. Dong
  • M. I. Vai
Part of the IFMBE Proceedings book series (IFMBE, volume 42)

Abstract

Wavelet neural networks combine the advantages of fast wavelet analysis and adaptive network optimization. They receive widespread attention for physiological signal interpretation. The infrastructures of current wavelet neural networks are either loosely associated or intrinsically synthesized. The former systems are advantageous in flexible structure, while the latter ones are oriented to global optimization. In this study we propose a new discriminant wavelet modeling by incorporating the famous method of Fisher’s Linear Discrimination. It is then possible to construct a series of linear discriminant wavelet networks that inherit flexible infrastructure but achieve global optimization. Experiments on a well-known benchmark database effectively support this novel scheme for wavelet neural networks.

Keywords

Classification linear discrimination physiological signal interpretation wavelet neural networks 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bing Nan Li
    • 1
    • 2
  • You Li
    • 1
  • Kui Xiang
    • 3
  • Ning An
    • 2
    • 4
  • M. C. Dong
    • 5
  • M. I. Vai
    • 5
  1. 1.Department of Biomedical EngineeringHefei University of TechnologyHefeiChina
  2. 2.Gerontechnology LabHefei University of TechnologyHefeiChina
  3. 3.School of AutomationWuhan University of TechnologyWuhanChina
  4. 4.School of Computer and InformationHefei University of TechnologyHefeiChina
  5. 5.Department of Electrical and Computer EngineeringUniversity of MacauTaipaMacau

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