Descriptive multidimensional statistical methods for analysing signals in a multifactorial biomedical database

Abstract

A methodology is presented to analyse multidimensional signals from several recording periods resulting from an experimental study on human or other living systems. The methodology is divided into two stages: intra-period analysis and inter-period analysis. The purpose of the first stage is to highlight general trends in multidimensional signal changes and the more informative components of the signals. The purpose of the second stage is to assess the influence of environmental or individual difference factors on a given signal component that appears to be discriminant in the first stage. To take into account the multivariable state of the system and the multi-observational aspect, a multidimensional descriptive statistical approach is used. The methods are correspondence analysis and hierarchical clustering. They are illustrated through an occupational medicine application from a study of sedentary posture.