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Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks.
Zignoli, Andrea; Fornasiero, Alessandro; Stella, Federico; Pellegrini, Barbara; Schena, Federico; Biral, Francesco; Laursen, Paul B.
Afiliación
  • Zignoli A; Department of Industrial Engineering, University of Trento , Trento , Italy.
  • Fornasiero A; CeRiSM Research Centre, University of Verona , Rovereto , Trento , Italy.
  • Stella F; CeRiSM Research Centre, University of Verona , Rovereto , Trento , Italy.
  • Pellegrini B; Department of Neuroscience, Biomedicine and Movement, University of Verona , Verona , Italy.
  • Schena F; CeRiSM Research Centre, University of Verona , Rovereto , Trento , Italy.
  • Biral F; CeRiSM Research Centre, University of Verona , Rovereto , Trento , Italy.
  • Laursen PB; Department of Neuroscience, Biomedicine and Movement, University of Verona , Verona , Italy.
Eur J Sport Sci ; 19(9): 1221-1229, 2019 Oct.
Article en En | MEDLINE | ID: mdl-30880591
First and second ventilatory thresholds (VT1 and VT2) represent the boundaries of the moderate-heavy and heavy-severe exercise intensity. Currently, VTs are primarily detected visually from cardiopulmonary exercise test (CPET) data, beginning with an initial data screening followed by data processing and statistical analysis. Automated VT detection is a challenging task owing to the high signal to noise ratio typical of CPET data. Recurrent neural networks describe a machine learning form of Artificial Intelligence that can be used to uncover complex non-linear relationships between input and output variables. Here we proposed detection of VTs using a single neural network classifier, trained with a database of 228 laboratory CPET data. We tested the neural network performance against the judgement of 7 couples of board-certified exercise-physiologists on 25 CPET tests. The neural network achieved expert-level performances across the tasks (mean absolute error was 9.5% (r = 0.79) and 4.2% (r = 0.94) for VT1 and VT2, respectively). Estimation errors are compatible with the typical error of the current gold standard visual methodology. The neural network demonstrated VT detecting and exercise intensity level classifying at a high competence level. Neural networks could potentially be embedded in CPET hardware/software to extend the reach of exercise physiologists beyond their laboratories.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Ventilación Pulmonar / Prueba de Esfuerzo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Eur J Sport Sci Año: 2019 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Ventilación Pulmonar / Prueba de Esfuerzo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Eur J Sport Sci Año: 2019 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido