Early prediction of the highest workload in incremental cardiopulmonary tests

Authors:
Elena Baralis;Tania Cerquitelli;Silvia Chiusano;Vincenzo D'elia;Riccardo Molinari;Davide Susta
Affiliations:
Politecnico di Torino, Italy;Politecnico di Torino, Italy;Politecnico di Torino, Italy;Politecnico di Torino, Italy;Sport Training Center, Eupilio, Italy;Dublin City University, Ireland
Venue:
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Year:
2013

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Abstract

Incremental tests are widely used in cardiopulmonary exercise testing, both in the clinical domain and in sport sciences. The highest workload (denoted Wpeak) reached in the test is key information for assessing the individual body response to the test and for analyzing possible cardiac failures and planning rehabilitation, and training sessions. Being physically very demanding, incremental tests can significantly increase the body stress on monitored individuals and may cause cardiopulmonary overload. This article presents a new approach to cardiopulmonary testing that addresses these drawbacks. During the test, our approach analyzes the individual body response to the exercise and predicts the Wpeak value that will be reached in the test and an evaluation of its accuracy. When the accuracy of the prediction becomes satisfactory, the test can be prematurely stopped, thus avoiding its entire execution. To predict Wpeak, we introduce a new index, the CardioPulmonary Efficiency Index (CPE), summarizing the cardiopulmonary response of the individual to the test. Our approach analyzes the CPE trend during the test, together with the characteristics of the individual, and predicts Wpeak. A K-nearest-neighbor-based classifier and an ANN-based classier are exploited for the prediction. The experimental evaluation showed that the Wpeak value can be predicted with a limited error from the first steps of the test.