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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.accessioned2011-07-06T09:45:53Z
dc.date.available2011-07-06T09:45:53Z
dc.date.created2010
dc.date.issued2010
dc.identifier.citationMorgenstern, C.R. [et al.]. Automatic non-invasive differentiation of obstructive and central hypopneas with nasal airflow compared to esophageal pressure. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society". Buenos Aires: 2010, p. 6142-6145.
dc.identifier.urihttp://hdl.handle.net/2117/12874
dc.description.abstractThe 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 but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been shown with invasive techniques to be useful to differentiate between central and obstructive hypopneas. In this study we present a new method for the automatic non-invasive differentiation of obstructive and central hypopneas solely with nasal airflow. An overall of 36 patients underwent full night polysomnography with systematic Pes recording and a total of 1069 hypopneas were manually scored by human experts to create a goldstandard annotation set. Features were automatically extracted from the nasal airflow signal to train and test our automatic classifier (Discriminant Analysis). Flattening patterns were non-invasively assessed in the airflow signal using spectral and time analysis. The automatic non-invasive classifier obtained a sensitivity of 0.71 and an accuracy of 0.69, similar to the results obtained with a manual non-invasive classification algorithm. Hence, flattening airflow patterns seem promising for the noninvasive differentiation of obstructive and central hypopneas.
dc.format.extent4 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.subject.lcshHypopnea -- Diagnosis
dc.subject.lcshSleep apnea, obstructive -- Diagnosis
dc.titleAutomatic non-invasive differentiation of obstructive and central hypopneas with nasal airflow compared to esophageal pressure
dc.typeConference report
dc.subject.lemacSon -- Aspectes fisiològics
dc.subject.lemacHipopnea
dc.contributor.groupUniversitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
dc.identifier.doi10.1109/IEMBS.2010.5627787
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5627787&tag=1
dc.rights.accessOpen Access
local.identifier.drac5740473
dc.description.versionPostprint (published version)
local.citation.authorMorgenstern, C.R.; Schwaibold, M.; Randerath, W.; Bolz, A.; Jané, R.
local.citation.contributorAnnual International Conference of the IEEE Engineering in Medicine and Biology Society
local.citation.pubplaceBuenos Aires
local.citation.publicationName2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
local.citation.startingPage6142
local.citation.endingPage6145


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