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.

Autore Pugliese

• Loglisci Corrado , Malerba Donato , Ceci Michelangelo

Tutti gli autori

• LOGLISCI C.;MALERBA D.;CECI M.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2011

ISSN

0302-9743

ISBN

978-3-642-23090-5

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

1

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile