Techniques in the consolidation, characterization, and expression of physiologic signals in the time domain

Abstract

The analysis of the dynamic behavior of complex systems requires the simultaneous and continuous monitoring of many variables, often over extended periods of time. In older to prepare the large amounts of data so obtained for analysis, efficient methods for data reduction and the characterization of condensed time series using appropriate screening and statistical procedures are essential. In this paper a solution to two of these problems is dealt with, namely the characterization of multivariate data, recored from biological systems under stress, in the time domain and the screening of study data for redundancy. The processing of such data is subject to two major constraints: the variables are frequently nonnormally distributed and the order of the behavior of the system to be characterized is frequently not known. The approach described comprises an initial data reduction for intermediate storage by a factor of one thousand to seventy through an averaging procedure. The resulting second time series is then characterized in terms of a) steady-state values during both the control and the response state; b) the variability of these steady state values and c) the shape and magnitude of the transient part of the response. The number of parameters resulting from this procedure is further reduced by screening the data for redundancy without investigator bias using a functional proposed by Andrews. Further processing of the data includes cluster and principal component analysis in an attempt to identify biologically meaningful basic system dimensions.