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dc.contributor.authorMorgenstern de Muller, Christian Rudolf
dc.contributor.authorSchwaibold, Matthias
dc.contributor.authorRanderath, Winfried J.
dc.contributor.authorBolz, Armin
dc.contributor.authorJané Campos, Raimon
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherInstitut de Bioenginyeria de Catalunya
dc.date.accessioned2010-11-05T09:53:57Z
dc.date.available2010-11-05T09:53:57Z
dc.date.created2010-04-15
dc.date.issued2010-04-15
dc.identifier.citationMorgenstern, C.R. [et al.]. An invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas. "IEEE transactions on biomedical engineering", 15 Abril 2010, vol. 57, núm. 8, p. 1927-1936.
dc.identifier.issn0018-9294
dc.identifier.urihttp://hdl.handle.net/2117/10135
dc.description.abstractThe automatic differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep-disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifierwith nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted fromthe Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.
dc.format.extent10 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut
dc.subject.lcshEsophageal Diseases
dc.subject.lcshBiomedical engineering
dc.subject.lcshPolysomnography
dc.titleAn invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas
dc.typeArticle
dc.subject.lemacEnginyeria biomèdica
dc.subject.lemacRespiració -- Mesurament
dc.subject.lemacSíndromes d'apnea del son
dc.contributor.groupUniversitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
dc.identifier.doi10.1109/TBME.2010.2047505
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5447740&tag=1
dc.rights.accessOpen Access
local.identifier.drac2748824
dc.description.versionPostprint (published version)
local.citation.authorMorgenstern, C.R.; Schwaibold, M.; Randerath, W.; Bolz, A.; Jané, R.
local.citation.publicationNameIEEE transactions on biomedical engineering
local.citation.volume57
local.citation.number8
local.citation.startingPage1927
local.citation.endingPage1936


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