Abstract

Analyzing biosignal data is an activity of great importance which can unearth information on the course of a disease. In this paper we propose a temporal data mining approach to analyze these data and acquire knowledge, in the form of temporal patterns, on the physiological events which can frequently trigger particular stages of disease. The proposed approach is realized through a four-stepped computational solution: first, disease stages are determined, then a subset of stages of interest is identified, subsequently physiological time-annotated events which can trigger those stages are detected, finally, patterns are discovered from the extracted events. The application to the sleep sickness scenario is addressed to discover patterns of events, in terms of breathing and cardiovascular system time-annotated disorders, which may trigger particular sleep stages.

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

2010

ISSN

Non Disponibile

ISBN

978-88-7488-369-1

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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