Classification of ion-channel signals using neural networks

B. Konnanath, P. Knee, Andreas Spanias, G. Wichern

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Ion-channel sensors can be used for detection of biochemical agents. A silicon-based ion-channel platform has been developed for stochastic sensing. In this paper, we present techniques to extract appropriate features from sensor data using a combination Walsh-Hadamard transform and Principal Component Analysis. Classification of these features is accomplished using neural network techniques. Results are presented for synthetic data.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2009
Pages19-22
Number of pages4
StatePublished - Dec 1 2009
Event6th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2009 - Innsbruck, Austria
Duration: Feb 17 2009Feb 19 2009

Publication series

NameProceedings of the 6th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2009

Other

Other6th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2009
CountryAustria
CityInnsbruck
Period2/17/092/19/09

Keywords

  • Feature extraction and pattern classification
  • Ion-channel sensors
  • Neural networks
  • PCA
  • Walsh transforms

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Communication

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  • Cite this

    Konnanath, B., Knee, P., Spanias, A., & Wichern, G. (2009). Classification of ion-channel signals using neural networks. In Proceedings of the 6th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2009 (pp. 19-22). (Proceedings of the 6th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2009).