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European Journal of Sport Science Volume 19, 2019 - Issue 9
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PHYSIOLOGY AND NUTRITION

Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks

Andrea ZignoliDepartment of Industrial Engineering, University of Trento, Trento, Italy;CeRiSM Research Centre, University of Verona, Rovereto, Trento, ItalyCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-1315-5573View further author information
ORCID Icon,
Alessandro FornasieroCeRiSM Research Centre, University of Verona, Rovereto, Trento, Italy;Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, ItalyView further author information
,
Federico StellaCeRiSM Research Centre, University of Verona, Rovereto, Trento, ItalyView further author information
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Barbara PellegriniCeRiSM Research Centre, University of Verona, Rovereto, Trento, Italy;Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, ItalyORCID Iconhttps://orcid.org/0000-0002-9626-2632View further author information
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Federico SchenaCeRiSM Research Centre, University of Verona, Rovereto, Trento, Italy;Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, ItalyView further author information
,
Francesco BiralDepartment of Industrial Engineering, University of Trento, Trento, ItalyORCID Iconhttps://orcid.org/0000-0001-8098-7965View further author information
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Paul B. LaursenSports Performance Research Institute NZ, Auckland University of Technology, Auckland, New ZealandView further author information
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Pages 1221-1229 | Published online: 18 Mar 2019

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Read on this site (2)

Christos Chalitsios, Thomas Nikodelis, Vasileios Konstantakos & Iraklis Kollias. (2022) Sensitivity of movement features to fatigue during an exhaustive treadmill run. European Journal of Sport Science 22:9, pages 1374-1382.
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A. Zignoli, A. Fornasiero, P. Rota, V. Muollo, L.A. Peyré-Tartaruga, D.A. Low, F.Y. Fontana, D. Besson, M. Pühringer, S. Ring-Dimitriou & L. Mourot. (2022) Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests. European Journal of Sport Science 22:3, pages 425-435.
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Articles from other publishers (14)

Hyun-Myung Cho, Sungmin Han, Joon-Kyung Seong & Inchan Youn. (2023) Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms. Computer Methods and Programs in Biomedicine, pages 107973.
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Dragutin Stojmenović & Tamara Stojmenović. (2023) Physiological parameters of professional football players in teams of various levels. Pedagogy of Physical Culture and Sports 27:5, pages 361-367.
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Andrea Zignoli, Alessandro Fornasiero, Federica Gilli, Barbara Pellegrini & Federico Schena. (2023) How the Oxynet web applications are used to crowdsource and interpret cardiopulmonary exercising tests data. Biomedical Signal Processing and Control 85, pages 104836.
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Simon Nolte. (2023) spiro: An R package for analyzing data from cardiopulmonary exercise testing. Journal of Open Source Software 8:81, pages 5089.
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Andrea Zignoli. (2023) Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation. Sensors 23:2, pages 826.
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Julio J. Portella, Brian J. Andonian, Donald E. Brown, Joao Mansur, Derek Wales, Vivian L. West, William E. Kraus & William Ed Hammond. (2022) Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise Testing. IEEE Journal of Biomedical and Health Informatics 26:8, pages 4228-4237.
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Andrea Zignoli & Damiano Fruet. (2022) Automatic generation of realistic cardiopulmonary exercise test data with a conditional generative adversarial neural network. Automatic generation of realistic cardiopulmonary exercise test data with a conditional generative adversarial neural network.
James A. Jablonski, Siddhartha S. Angadi, Suchetha Sharma & Donald E. Brown. (2022) Enabling Clinically Relevant and Interpretable Deep Learning Models for Cardiopulmonary Exercise Testing. Enabling Clinically Relevant and Interpretable Deep Learning Models for Cardiopulmonary Exercise Testing.
Alexander Chikov, Nikolay Egorov, Dmitry Medvedev, Svetlana Chikova, Evgeniy Pavlov, Pavel Drobintsev, Alexander Krasichkov & Dmitry Kaplun. (2022) Determination of the athletes' anaerobic threshold using machine learning methods. Biomedical Signal Processing and Control 73, pages 103414.
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Julia Kathrin Baumgart, Gertjan Ettema, Katy E. Griggs, Victoria Louise Goosey-Tolfrey & Christof Andreas Leicht. (2021) A Reappraisal of Ventilatory Thresholds in Wheelchair Athletes With a Spinal Cord Injury: Do They Really Exist?. Frontiers in Physiology 12.
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Brian J. Andonian, Nicolas Hardy, Alon Bendelac, Nicholas Polys & William E. Kraus. (2021) Making Cardiopulmonary Exercise Testing Interpretable for Clinicians. Current Sports Medicine Reports 20:10, pages 545-552.
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Francesca Anselmi, Luna Cavigli, Antonio Pagliaro, Serafina Valente, Francesca Valentini, Matteo Cameli, Marta Focardi, Nicola Mochi, Paul Dendale, Dominique Hansen, Marco Bonifazi, Martin Halle & Flavio D’Ascenzi. (2021) The importance of ventilatory thresholds to define aerobic exercise intensity in cardiac patients and healthy subjects. Scandinavian Journal of Medicine & Science in Sports 31:9, pages 1796-1808.
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Bruce Rogers, David Giles, Nick Draper, Laurent Mourot & Thomas Gronwald. (2021) Detection of the Anaerobic Threshold in Endurance Sports: Validation of a New Method Using Correlation Properties of Heart Rate Variability. Journal of Functional Morphology and Kinesiology 6:2, pages 38.
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Andrea Zignoli, Alessandro Fornasiero, Enrico Bertolazzi, Barbara Pellegrini, Federico Schena, Francesco Biral & Paul B. Laursen. (2019) State-of-the art concepts and future directions in modelling oxygen consumption and lactate concentration in cycling exercise. Sport Sciences for Health 15:2, pages 295-310.
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