Machine-learning based feature selection for a non-invasive breathing change detection View Full Text


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Article Info

DATE

2021-07-18

AUTHORS

Juliana Alves Pegoraro, Sophie Lavault, Nicolas Wattiez, Thomas Similowski, Jésus Gonzalez-Bermejo, Etienne Birmelé

ABSTRACT

BackgroundChronic Obstructive Pulmonary Disease (COPD) is one of the top 10 causes of death worldwide, representing a major public health problem. Researchers have been looking for new technologies and methods for patient monitoring with the intention of an early identification of acute exacerbation events. Many of these works have been focusing in breathing rate variation, while achieving unsatisfactory sensitivity and/or specificity. This study aims to identify breathing features that better describe respiratory pattern changes in a short-term adjustment of the load-capacity-drive balance, using exercising data.ResultsUnder any tested circumstances, breathing rate alone leads to poor capability of classifying rest and effort periods. The best performances were achieved when using Fourier coefficients or when combining breathing rate with the signal amplitude and/or ARIMA coefficients.ConclusionsBreathing rate alone is a quite poor feature in terms of prediction of breathing change and the addition of any of the other proposed features improves the classification power. Thus, the combination of features may be considered for enhancing exacerbation prediction methods based in the breathing signal.Trial Registration : ClinicalTrials NCT03753386. Registered 27 November 2018, https://clinicaltrials.gov/show/NCT03753386 More... »

PAGES

33

References to SciGraph publications

  • 2017. Outlier Analysis in NONE
  • 1999-06. Mahalanobis distance in RESONANCE
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    http://dx.doi.org/10.1186/s13040-021-00265-8

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34275469


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    179 schema:name Institut de Recherche Mathématique Avancée, UMR 7501 Université de Strasbourg et CNRS, 7 rue René-Descartes, 67000, Strasbourg, France
    180 UMR CNRS 8145, Laboratoire MAP5, Université de Paris, 45 rue des Saints-Pères, 75006, Paris, France
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    182 grid-institutes:grid.50550.35 schema:alternateName AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Service de Pneumologie, Médecine Intensive et Réanimation (Département R3S), F-75013, Paris, France
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    184 Sorbonne Université, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, F-75005, Paris, France
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