Institute of Information Theories and Applications FOI ITHEA
Accurate and efficient analysis of biomedical signals can be facilitated by proper identification based on
their dominant dynamic characteristics (deterministic, chaotic or random). Specific analysis techniques exist to
study the dynamics of each of these three categories of signals. However, comprehensive and yet adequately
simple screening tools to appropriately classify an unknown incoming biomedical signal are still lacking. This
study is aimed at presenting an efficient and simple method to classify model signals into the three categories of
deterministic, random or chaotic, using the dynamics of the False Nearest Neighbours (DFNN) algorithm, and
then to utilize the developed classification method to assess how some specific biomedical signals position with
respect to these categories. Model deterministic, chaotic and random signals were subjected to state space
decomposition, followed by specific wavelet and statistical analysis aiming at deriving a comprehensive plot
representing the three signal categories in clearly defined clusters. Previously recorded electrogastrographic
(EGG) signals subjected to controlled, surgically-invoked uncoupling were submitted to the proposed algorithm,
and were classified as chaotic. Although computationally intensive, the developed methodology was found to be
extremely useful and convenient to use.
* This study was supported in part by the Natural Sciences and Engineering Research Council of Canada, and by
the Gastrointestinal Motility Laboratory (University of Alberta Hospitals) in Edmonton, Alberta, Canada.