A Framework for Multidimensional Real-Time Data Analysis: A Case Study for the Detection of Apnoea of Prematurity

In this paper, the authors present a framework to support multidimensional analysis of real-time physiological data streams and clinical data. The clinical context for the case study demonstration is neonatal intensive care, focusing specifically on the detection of episodes of central apnoea, a clinically significant problem. The model accounts for the multidimensional and real-time nature of apnoea of prematurity and the associated clinical rules. The framework demonstration includes: 1) defining rules that quantify concurrent behaviours between multiple synchronous data streams and asynchronous data values; 2) designing UML models to define present practice event processing for episodes of apnoea; 3) translating the model in SPADE to enable the deployment within the real-time processing layer of the Artemis platform, which utilizes IBM’s InfoSphere Streams; 4) demonstrating knowledge discovery with simple and complex temporal abstractions of the data streams; and 5) presenting results for early detection of episodes of apnoea across multiple physiological data streams. DOI: 10.4018/978-1-4666-2653-9.ch002 J. Mikael Eklund University of Ontario Institute of Technology, Canada