Analysis of physiological data

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Cardiopulmonary exercise testing is a non-invasive method widely used to monitor various physiological signals, describing the cardiac and respiratory response of the patient to increasing workload. Since this method is physically very demanding, innovative data analysis techniques are needed to predict patient response thus lowering body stress and avoiding cardiopulmonary overload. The Cardiopulmonary Response Prediction (CRP) framework has been proposed for early predicting the physiological signal values that can be reached during an incremental exercise test. The learning phase creates different models tailored to specific conditions (i.e., single-test and multiple-test models). Each model can be exploited in the real-time stream prediction phase to periodically predict, during the test execution, signal values achievable by the patient. Experimental results on a real dataset showed that CRP prediction is performed with a limited and acceptable error.


Elena Baralis, Tania Cerquitelli, Silvia Chiusano, Andrea Giordano, Alessandro Mezzani, Davide Susta, Xin Xiao
Predicting cardiopulmonary response to incremental exercise test
28th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2015), June 22nd-25th 2015, São Carlos and Ribeirão Preto, Brazil

Master thesis – Analysis of the cardiopulmonary response in incremental exercise tests using data mining techniques