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.
Discovering Temporal Patterns of Complex Events in Biosignal Data
LOGLISCI, CORRADO;CECI, MICHELANGELO;MALERBA, Donato
2010-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
