Key result
CardioPulmonary Efficiency Index and machine learning predict Wpeak from early test steps with limited error.
Why the study?
Incremental cardiopulmonary exercise tests are physically demanding and may cause cardiopulmonary overload, motivating the need for early prediction of the highest workload to potentially stop tests prematurely.
Can a machine learning approach using the CardioPulmonary Efficiency Index accurately predict the highest workload in incremental cardiopulmonary tests to allow early test termination?
Can a machine learning approach using the CardioPulmonary Efficiency Index accurately predict the highest workload in incremental cardiopulmonary tests to allow early test termination?
A novel machine learning approach using the CardioPulmonary Efficiency Index can accurately predict peak workload early during cardiopulmonary exercise testing, potentially avoiding the need for maximal exertion.
May support early cardiopulmonary test termination; hypothesis-generating and requires prospective validation before practice change.
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 Wpeakvalue 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 Wpeakvalue can be predicted with a limited error from the first steps of the test.
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Baralis et al. (2013) studied Cardiopulmonary exercise testing. CardioPulmonary Efficiency Index (CPE) based prediction approach was evaluated on Prediction of highest workload (Wpeak). A new approach using the CardioPulmonary Efficiency Index and machine learning classifiers predicted the highest workload (Wpeak) with limited error from the first steps of the test.
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