2019
DOI: 10.1371/journal.pone.0212199
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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 exerc… Show more

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Cited by 6 publications
(11 citation statements)
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“…We previously developed predictive model, which includes inputs of CPET features and outputs of anaerobic mechanical power [12]. More specifically, 51 features, from gas exchange analysis driven from a maximal incremental CPET were measured and calculated and used as input on the predictive algorithm.…”
Section: Experimental Designmentioning
confidence: 99%
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“…We previously developed predictive model, which includes inputs of CPET features and outputs of anaerobic mechanical power [12]. More specifically, 51 features, from gas exchange analysis driven from a maximal incremental CPET were measured and calculated and used as input on the predictive algorithm.…”
Section: Experimental Designmentioning
confidence: 99%
“…More specifically, 51 features, from gas exchange analysis driven from a maximal incremental CPET were measured and calculated and used as input on the predictive algorithm. Eventually, the PP and mean power (MP) outputs form the Wingate anaerobic test (WAnT) were predicted, at which the predicted correlation were r = 0.94 and r = 0.9, respectively [12]. In the current paper, the same data that was collected from subjects performing both CPET and WAnT was used [12], yet the CPET data was adjusted/re-calculated in a way that represents a standard EST (which do not include gas exchange data).…”
Section: Experimental Designmentioning
confidence: 99%
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“…From the perspective of research methods, in addition to traditional statistical analysis, data mining of CPET using machine learning techniques is gradually becoming a research hotspot. Leopold et al developed a greedy heuristic algorithm based on feature clustering to study the ability of CPET to predict the anaerobic mechanical power outputs [ 27 ]. Braccioni et al used a random forest algorithm to analyze the relationship between symptoms and cardiopulmonary parameters of lung transplant recipients based on incremental CPET [ 28 ].…”
Section: Related Workmentioning
confidence: 99%
“…Researchers have attempted to apply machine learning to interpret CPET data, achieving suggestive results. Leopold et al developed a greedy heuristic algorithm based on feature clustering to study the ability of CPET to predict the anaerobic mechanical power outputs [ 20 ]. Braccioni et al used a random forest algorithm to analyze the relationship between symptoms and cardiopulmonary parameters of lung transplant recipients; this was based on incremental CPET [ 21 ].…”
Section: Introductionmentioning
confidence: 99%