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Classification of Stationary Signals with Mixed Spectrum

Author

Listed:
  • Saavedra Pedro

    (University of Las Palmas de Gran Canaria)

  • Santana-del-Pino Angelo

    (Universidad de Las Palmas de Gran Canaria)

  • Hernández-Flores Carmen N.

    (Universidad de Las Palmas de Gran Canaria)

  • Artiles-Romero Juan

    (Universidad de Las Palmas de Gran Canaria)

  • González-Henríquez Juan J.

    (Universidad de Las Palmas de Gran Canaria)

Abstract

This paper deals with the problem of discrimination between two sets of complex signals generated by stationary processes with both random effects and mixed spectral distributions. The presence of outlier signals and their influence on the classification process is also considered. As an initial input, a feature vector obtained from estimations of the spectral distribution is proposed and used with two different learning machines, namely a single artificial neural network and the LogitBoost classifier. Performance of both methods is evaluated on five simulation studies as well as on a set of actual data of electroencephalogram (EEG) records obtained from both normal subjects and others having experienced epileptic seizures. Of the different classification methods, Logitboost is shown to be more robust to the presence of outlier signals.

Suggested Citation

  • Saavedra Pedro & Santana-del-Pino Angelo & Hernández-Flores Carmen N. & Artiles-Romero Juan & González-Henríquez Juan J., 2011. "Classification of Stationary Signals with Mixed Spectrum," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-17, January.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:7
    DOI: 10.2202/1557-4679.1288
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    References listed on IDEAS

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    1. P. Saavedra & C. Hernández & I. Luengo & J. Artiles & A. Santana, 2008. "Estimation of population spectrum for linear processes with random coefficients," Computational Statistics, Springer, vol. 23(1), pages 79-98, January.
    2. Rahim Chinipardaz & Trevor Cox, 2004. "Nonparametric discrimination of time series data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 59(1), pages 13-20, February.
    3. Javier Alagón, 1989. "Spectral Discrimination For Two Groups Of Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 203-214, May.
    4. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
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