Prediction of the Wingate anaerobic mechanical power outputs from a maximal incremental cardiopulmonary exercise stress test using machine-learning approach

Abstract

The Wingate Anaerobic Test (WAnT) is a short-term maximal intensity cycle ergometer test, which provides anaerobic mechanical power output variables. Despite the physiological significance of the variables extracted from the WAnT, the test is very intense, and generally applies for athletes. Our goal, in this paper, was to develop a new approach to predict the anaerobic mechanical power outputs using maximal incremental cardiopulmonary exercise stress test (CPET). We hypothesized that maximal incremental exercise stress test hold hidden information about the anaerobic components, which can be directly translated into mechanical power outputs. We therefore designed a computational model that included aerobic variables (features), and used a new computational \ predictive algorithm, which enabled the prediction of the anaerobic mechanical power outputs. We analyzed the chosen predicted features using clustering on a network. For peak power (PP) and mean power (MP) outputs, the equations included six features and four features, respectively. The combination of these features produced a prediction model of r = 0.94 and r = 0.9, respectively, on the validation set between the real and predicted PP/MP values (P< 0.001). The newly predictive model allows the accurate prediction of the anaerobic mechanical power outputs at high accuracy. The assessment of additional tests is desired for the development of a robust application for athletes, older individuals, and/or non-healthy populations.

Fig 1. Schematic flow of the research.

Fig 2. Flow diagram that describe the feature selection procedure.

Fig 3. Representation of a network that connects all the aerobic features.

Fig 4

Plots illustrating the validation set for the predicted (x-axes) versus the known (y-axes) values for peak power (PP) (w) (a) and mean power (MP) (w) (b). The bar diagram illustrates the Spearman correlation coefficient between the predicted values of the peak power (PP) (w) (c) and mean power (MP) (w) (d) while adding an additional feature each iteration.

Fig 5

Plots illustrating the validation set for the predicted (x-axes) versus the known (y-axes) values for peak power (PP) (w/kg) (a) and mean power (MP) (w/kg) (b). The bar diagram illustrates the Spearman correlation coefficient between the predicted values of the peak power (PP) (w/kg) (c) and mean power (MP) (w/kg) (d) while adding an additional feature each iteration.