Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning

Abstract : Purpose The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique. Method We compared two models of machine learning (ML) for estimating (V) over cap (RMP): a linear model (multiple linear regression- MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged 24: 1 +/- 3: 4 years participated in the present study. The protocol was conducted in a laboratory test room. The anteroposterior displacements of the rib cage and abdomen, and the axial displacements of the chest wall and spine were measured using two pairs of magnetometers. (V) over cap (RMP) was estimated from these four signals, and the respiratory volume was simultaneously measured using a spirometer (V-SP) under lying, sitting and standing conditions as well as various exercise conditions (working on computer, treadmill walking at 4 and 6 km h(-1), treadmill running at 9 and 12 km h(-1) and ergometer cycling at 90 and 110 W). Results The results from the ANN model fitted the spirometer volume significantly better than those obtained through MLR. Considering all activities, the difference between (V) over cap (RMP) and VSP (bias) was higher for the MLR model (0: 00191 +/- 0: 141 L) than for the ANN model (0: 00158 +/- 0: 150 L). Conclusion Our results demonstrate that this new processing approach for RMP seems to be a valid tool for estimating V with sufficient accuracy during lying, sitting and standing and under various exercise conditions.
Type de document :
Article dans une revue
European Journal of Applied Physiology, Springer Verlag, 2017, 117 (8), pp.1533--1555. 〈10.1007/s00421-017-3630-0〉
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Contributeur : Laurent Jonchère <>
Soumis le : mardi 12 septembre 2017 - 11:49:52
Dernière modification le : mercredi 16 mai 2018 - 11:23:42

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Remy Dumond, Steven Gastinger, Hala Abdul Rahman, Alexis Le Faucheur, Patrice Quinton, et al.. Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning. European Journal of Applied Physiology, Springer Verlag, 2017, 117 (8), pp.1533--1555. 〈10.1007/s00421-017-3630-0〉. 〈hal-01585982〉

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