A non-Gaussian approach for biosignal classification based on the Johnson SU translation system

Hideaki Hayashi, Yuichi Kurita, Toshio Tsuji

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

2 Citations (Scopus)

Abstract

This paper proposes a non-Gaussian approach for biosignal classification based on the Johnson SU translation system. The Johnson system is a normalizing translation that transforms data without normality to normal distribution using four parameters, thereby enabling the representation of a wide range of shapes for marginal distribution with skewness and kurtosis. In this study, a discriminative model based on the multivariate Johnson SU translation system is transformed into linear combinations of coefficients and input vectors using log-linearization, and is incorporated into a neural network structure, thereby allowing the determination of model parameters as weight coefficients of the network via backpropagation-based training. In the experiments, the classification performance of the proposed network is demonstrated using artificial data and electromyogram data.

Original languageEnglish
Title of host publication2015 IEEE 8th International Workshop on Computational Intelligence and Applications, IWCIA 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages115-120
Number of pages6
ISBN (Electronic)9781479998869
DOIs
Publication statusPublished - Apr 7 2016
Externally publishedYes
Event8th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2015 - Hiroshima, Japan
Duration: Nov 6 2015Nov 7 2015

Publication series

Name2015 IEEE 8th International Workshop on Computational Intelligence and Applications, IWCIA 2015 - Proceedings

Other

Other8th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2015
CountryJapan
CityHiroshima
Period11/6/1511/7/15

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Control and Optimization
  • Artificial Intelligence
  • Computer Science Applications

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