Human State Classification and Prediction for Critical Care Monitoring By Real-Time Bio-Signal Analysis

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 prediction. The novel bioinformatic framework is developed to solve the human state classification and prediction 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.