Robust signal extraction for on-line monitoring data

dc.contributor.authorDavies, P. L.de
dc.contributor.authorFried, Rolandde
dc.contributor.authorGather, Ursulade
dc.date.accessioned2004-12-06T18:42:53Z
dc.date.available2004-12-06T18:42:53Z
dc.date.issued2002de
dc.description.abstractData from the automatic monitoring of intensive care patients exhibits trends, outliers, and level changes as well as periods of relative constancy. All this is overlaid with a high level of noise and there are dependencies between the different items measured. Current monitoring systems tend to deliver too many false warnings which reduces their acceptability by medical staff. The challenge is to develop a method which allows a fast and reliable denoising of the data and which can separate artifacts from clinical relevant structural changes in the patients condition (Gather et al., 2002). A simple median filter works well as long as there is no substantial trend in the data but improvements may be possible by approximating the data by a local linear trend. As a first step in this programme the paper examines the relative merits of the L1 regression, the repeated median (Siegel, 1982) and the least median of squares (Hampel, 1975, Rousseeuw, 1984). The question of dependency between different items is a topic for future research.en
dc.format.extent2038276 bytes
dc.format.extent532999 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5043
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5530
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectlinear regressionen
dc.subjectlevel changeen
dc.subjecttrenden
dc.subjectoutliersen
dc.subjectsmall-sample efficiencyen
dc.subject.ddc310de
dc.subject.rswksignal extractionen
dc.titleRobust signal extraction for on-line monitoring dataen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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