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dc.contributor.authorMuñoz Ortega, Isabel Cristina
dc.contributor.authorHernández Valdivieso, Alher Mauricio
dc.contributor.authorMañanas Villanueva, Miguel Ángel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2019-07-24T05:58:31Z
dc.date.available2019-07-24T05:58:31Z
dc.date.issued2019-05-01
dc.identifier.citationMuñoz, I.; Hernández, A.M.; Mañanas, M.A. Estimation of work of breathing from respiratory muscle activity in spontaneous ventilation: A pilot study. "Applied sciences", 1 Maig 2019, vol. 9, núm. 10, p. 1-18.
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/2117/166645
dc.description.abstractWork of breathing (WOB) offers information that may be relevant to determine the patient’s status under spontaneous mechanical ventilation in Intensive Care Unit (ICU). Nowadays, the most reliable technique to measure WOB is based on the use of invasive catheters, but the use of qualitative observations such as the level of dyspnea is preferred as a possible indicator of WOB level. In this pilot study, the activity of three respiratory muscles were recorded on healthy subjects through surface electromyography while they were under non-invasive mechanical ventilation, using restrictive and obstructive maneuvers to obtain different WOB levels. The respiratory pattern between restrictive and obstructive maneuvers was classified with the Nearest Neighbor Algorithm with a 91% accuracy and a neural network model helped classify the samples into three WOB levels with a 89% accuracy, Low: [0.3–0.8) J/L, Medium: [0.8–1.3] J/L and Elevated: (1.3–1.8] J/L, demonstrating the relationship between the respiratory muscle activity and WOB. This technique is a promising tool for the healthcare staff in the decision-making process when selecting the best ventilation settings to maintain a low WOB. This study identified a model to estimate the WOB in different ventilatory patterns, being an alternative to invasive conventional techniques
dc.format.extent18 p.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica
dc.subject.lcshBiomedical engineering
dc.subject.lcshLungs -- Diseases
dc.subject.otherNon-invasive ventilation
dc.subject.otherLung diseases
dc.subject.otherWork of breathing
dc.subject.otherRespiratory muscles
dc.subject.otherSurface electromyography
dc.subject.othermachine learning
dc.titleEstimation of work of breathing from respiratory muscle activity in spontaneous ventilation: A pilot study
dc.typeArticle
dc.subject.lemacEnginyeria biomedica
dc.subject.lemacPulmons -- Malalties
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy
dc.identifier.doi10.3390/app9102007
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/9/10/2007
dc.rights.accessOpen Access
local.identifier.drac25176981
dc.description.versionPostprint (author's final draft)
local.citation.authorMuñoz, I.; Hernández, A.M.; Mañanas, M.A.
local.citation.publicationNameApplied sciences
local.citation.volume9
local.citation.number10
local.citation.startingPage1
local.citation.endingPage18


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain