Real-time analysis of physiological data to support medical applications.
This paper presents a flexible framework that performs real-time analysis of physiological data to monitor people's health conditions in any context (e.g., during daily activities, in hospital environments). Given historical physiological data, different behavioral models tailored to specific conditions (e.g., a particular disease, a specific patient) are automatically learnt. A suitable model for the currently monitored patient is exploited in the real-time stream classification phase. The framework has been designed to perform both instantaneous evaluation and stream analysis over a sliding time window. To allow ubiquitous monitoring, real-time analysis could also be executed on mobile devices. As a case study, the framework has been validated in the intensive care scenario. Experimental validation, performed on 64 patients affected by different critical illnesses, demonstrates the effectiveness and the flexibility of the proposed framework in detecting different severity levels of monitored people's clinical situations.
Apiletti
Daniele
D
Dipartimento di Automatica ed Informatica,Politecnico di Torino, Torino 10129, Italy. daniele.apiletti@polito.it
Baralis
Elena
E
Bruno
Giulia
G
Cerquitelli
Tania
T
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D008954:Models, Biological; D008991:Monitoring, Physiologic; D011379:Prognosis; D015203:Reproducibility of Results; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted