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Psychotropic drug classification based on sleep–wake behaviour of rats

Author

Listed:
  • Kristien Wouters
  • Abdellah Ahnaou
  • Jose Cortinas Abrahantes
  • Geert Molenberghs
  • Helena Geys
  • Luc Bijnens
  • Wilhelmus H. I. M. Drinkenburg

Abstract

Summary. The aim of the paper is to present methodology for the classification of potential psychotropic drugs on the basis of their activity. We first sketch the background of this class of drugs and then zoom in on so‐called pharmacoelectroencephalogram studies. These data pose some statistical challenges. For classification purposes, we propose a flexible hierarchical discriminant analysis tool, allowing us to take the specific nature of the drug class into account, as well as the features of the mixed models, in combination with fractional polynomials, fitted to the electroencephalogram data. The method is evaluated against the background of existing methods. The method's performance is studied by using a comprehensive analysis of a large electroencephalogram data set.

Suggested Citation

  • Kristien Wouters & Abdellah Ahnaou & Jose Cortinas Abrahantes & Geert Molenberghs & Helena Geys & Luc Bijnens & Wilhelmus H. I. M. Drinkenburg, 2007. "Psychotropic drug classification based on sleep–wake behaviour of rats," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(2), pages 223-234, March.
    • Handle: RePEc:bla:jorssc:v:56:y:2007:i:2:p:223-234
    DOI: 10.1111/j.1467-9876.2007.00575.x
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    Cited by:

    1. Ana Arribas-Gil & Rolando De la Cruz & Emilie Lebarbier & Cristian Meza, 2015. "Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators," Biometrics, The International Biometric Society, vol. 71(2), pages 333-343, June.
    2. Luts, Jan & Molenberghs, Geert & Verbeke, Geert & Van Huffel, Sabine & Suykens, Johan A.K., 2012. "A mixed effects least squares support vector machine model for classification of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 611-628.

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