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dc.contributor.authorArcentales Viteri, Andrés Ricardo
dc.contributor.authorCaminal Magrans, Pere
dc.contributor.authorDiaz, Ivan
dc.contributor.authorBenito Vales, Salvador
dc.contributor.authorGiraldo Giraldo, Beatriz
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherInstitut de Bioenginyeria de Catalunya
dc.date.accessioned2015-10-08T11:26:28Z
dc.date.available2015-10-08T11:26:28Z
dc.date.created2015-05
dc.date.issued2015-05
dc.identifier.citationArcentales, A., Caminal, P., Diaz, I., Benito, S., Giraldo, B. Classification of patients undergoing weaning from mechanical ventilation using the coherence between heart rate variability and respiratory flow signal”. "Physiological measurement", Maig 2015, vol. 36, p. 1439-1452.
dc.identifier.issn0967-3334
dc.identifier.urihttp://hdl.handle.net/2117/77489
dc.description.abstractWeaning from mechanical ventilation is still one of the most challenging problems in intensive care. Unnecessary delays in discontinuation and weaning trials that are undertaken too early are both undesirable. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GSucc), 33 failed to maintain spontaneous breathing so were reconnected (GFail), and 15 were extubated after the test but reintubated within 48 h (GRein). The power spectral density and magnitude squared coherence (MSC) of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) and sequential floating feature selection. The patients were classified using a fuzzy K-nearest neighbour method. PCA of the MSC gave the best classification with the highest accuracy of 92% classifying GSucc versus GFail patients, and 86% classifying GSucc versus GRein patients. PCA of the respiratory flow signal gave the best classification between GFail and GRein patients (79% accuracy). These classifiers showed a good balance between sensitivity and specificity. Besides, the spectral coherence between HRV and the respiratory flow signal, in patients on weaning trial process, can contribute to the extubation decision.
dc.format.extent14 p.
dc.language.isoeng
dc.publisherInstitute of Physics (IOP)
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica
dc.subject.lcshArtificial respiration
dc.subject.otherheart rate variability
dc.subject.otherrespiratory flow signal
dc.subject.otherweaning process
dc.subject.othercoherence
dc.subject.otherprincipal component analysis
dc.titleClassification of patients undergoing weaning from mechanical ventilation using the coherence between heart rate variability and respiratory flow signal”
dc.typeArticle
dc.subject.lemacRespiració artificial
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOSPIN - Biomedical Signal Processing and Interpretation
dc.identifier.doi10.1088/0967-3334/36/7/1439
dc.rights.accessOpen Access
local.identifier.drac16638056
dc.description.versionPostprint (published version)
local.citation.authorArcentales, A.; Caminal, P.; Diaz, I.; Benito, S.; Giraldo, B.
local.citation.publicationNamePhysiological measurement
local.citation.volume36
local.citation.startingPage1439
local.citation.endingPage1452


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