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Istanbul, Turkey
Aug. 23, 2010 to Aug. 26, 2010
ISBN: 978-0-7695-4109-9
pp: 2460-2463
To address the challenges in critical care monitoring, we present a multi-modality bio-signal modeling and analysis modeling framework for real-time human state classification and predication. The novel bioinformatic framework is developed to solve the human state classification and predication issues from two aspects: a) achieve 1:1 mapping between the bio-signal and the human state via discriminant feature analysis and selection by using probabilistic principle component analysis (PPCA); b) avoid time-consuming data analysis and extensive integration resources by using Dynamic Bayesian Network (DBN). In addition, intelligent and automatic selection of the most suitable sensors from the bio-sensor array is also integrated in the proposed DBN.
Xiaokun Li, Fatih Porikli, "Human State Classification and Predication for Critical Care Monitoring by Real-Time Bio-signal Analysis", ICPR, 2010, Pattern Recognition, International Conference on, Pattern Recognition, International Conference on 2010, pp. 2460-2463, doi:10.1109/ICPR.2010.602
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