A temporal data mining framework for analyzing longitudinal data

Abstract

Longitudinal data consist of the repeated measurements of some variables which describe a process (or phenomenon) over time. They can be analyzed to unearth information on the dynamics of the process. In this paper we propose a temporal data mining framework to analyze these data and acquire knowledge, in the form of temporal patterns, on the events which can frequently trigger particular stages of the dynamic process. The application to a biomedical scenario is addressed. The goal is to analyze biosignal data in order to discover patterns of events, expressed in terms of breathing and cardiovascular system time-annotated disorders, which may trigger particular stages of the human central nervous system during sleep. © 2011 Springer-Verlag Berlin Heidelberg.