Predicting cardiopulmonary response to incremental exercise test

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. This paper proposes the Cardiopulmonary Response Prediction (CRP) framework 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