%0 Journal Article %T Evidence-based model for real-time surveillance of ARDS %+ Université Libanaise %+ Laboratoire Modélisation et Sûreté des Systèmes (LM2S) %A Taoum, Aline %A Mourad-Chehade, Farah %A Amoud, Hassan %< avec comité de lecture %@ 1746-8094 %J Biomedical Signal Processing and Control %I Elsevier %V 50 %P 83-91 %8 2019-04 %D 2019 %R 10.1016/j.bspc.2019.01.016 %Z Engineering Sciences [physics]/Signal and Image processing %Z Life Sciences [q-bio]/BioengineeringJournal articles %X Real-time health surveillance becomes important and necessary with the increase of the elderly population to preserve their quality of life. Real-time models aim to provide alerts before the severe illness occurs. Acute respiratory distress syndrome is a crucial disease of the respiratory system that threats the health of the elderly. This paper proposes a real-time model for the surveillance of ARDS based on belief functions theory. Non-invasive physiological signals are considered such as heart rate, respiratory rate, oxygen saturation and mean airway blood pressure. Different linear and nonlinear parameters are extracted from these signals; then a parameters selection procedure is performed to reduce their dimensionality. Afterwards, classifiers are constructed using parameters distributions defined in the evidence framework. Real-time prediction is then performed by combining all classifiers decisions. As results, high performances are obtained over the testing sets with performances of 77% and 71% for sensitivity and specificity, respectively. %G English %2 https://utt.hal.science/hal-02311170/document %2 https://utt.hal.science/hal-02311170/file/S1746809419300163.pdf %L hal-02311170 %U https://utt.hal.science/hal-02311170 %~ CNRS %~ UNIV-TROYES %~ ENS-RENNES %~ UNIV-RENNES %~ UTT %~ UTT-LIST3N %~ UTT-FULL-TEXT %~ ELSEVIER %~ LM2S-UTT