Human State Classification and Predication for Critical Care Monitoring by Real-Time Bio-signal Analysis
Pattern Recognition, International Conference on (2010)
Aug. 23, 2010 to Aug. 26, 2010
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2010.602
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", Pattern Recognition, International Conference on, vol. 00, no. , pp. 2460-2463, 2010, doi:10.1109/ICPR.2010.602