Table of Contents
Journal of Biophysics
Volume 2011, Article ID 290617, 11 pages
http://dx.doi.org/10.1155/2011/290617
Research Article

F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

1Physics Institute, CAP, University of Zürich, CH 8057 Zürich, Switzerland
2Research Department, Cantonal Psychiatric Hospital, CH 8462 Rheinau, Switzerland
3Department of Psychology, University of Konstanz, 78457 Konstanz, Germany
4Verhaltenstherapie, Post-Straße 3, 79098 Freiburg, Germany

Received 27 December 2010; Revised 18 April 2011; Accepted 24 May 2011

Academic Editor: Serdar Kuyucak

Copyright © 2011 R. M. Dünki and M. Dressel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating , that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio . A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, -, and -power was found.