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dc.contributor.authorLeube, Julian-
dc.contributor.authorZschocke, Johannes-
dc.contributor.authorKluge, Maria-
dc.contributor.authorPelikan, Luise-
dc.contributor.authorGraf, Antonia-
dc.contributor.authorGlos, Martin-
dc.contributor.authorMüller, Alexander-
dc.contributor.authorBartsch, Ronny P.-
dc.contributor.authorPenzel, Thomas-
dc.contributor.authorKantelhardt, Jan W.-
dc.date.accessioned2023-07-04T12:10:21Z-
dc.date.available2023-07-04T12:10:21Z-
dc.date.issued2020-
dc.identifier.urihttps://opendata.uni-halle.de//handle/1981185920/110767-
dc.identifier.urihttp://dx.doi.org/10.25673/108812-
dc.description.abstractRespiratory rate and changes in respiratory activity provide important markers of health and fitness. Assessing the breathing signal without direct respiratory sensors can be very helpful in large cohort studies and for screening purposes. In this paper, we demonstrate that long-term nocturnal acceleration measurements from the wrist yield significantly better respiration proxies than four standard approaches of ECG (electrocardiogram) derived respiration. We validate our approach by comparison with flow-derived respiration as standard reference signal, studying the full-night data of 223 subjects in a clinical sleep laboratory. Specifically, we find that phase synchronization indices between respiration proxies and the flow signal are large for five suggested acceleration-derived proxies with γ=0.55±0.13 for males and 0.58±0.14 for females (means ± standard deviations), while ECG-derived proxies yield only γ=0.36±0.16 for males and 0.39±0.14 for females. Similarly, respiratory rates can be determined more precisely by wrist-worn acceleration devices compared with a derivation from the ECG. As limitation we must mention that acceleration-derived respiration proxies are only available during episodes of non-physical activity (especially during sleep).eng
dc.language.isoeng-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subject.ddc610-
dc.titleReconstruction of the respiratory signal through ECG and wrist accelerometer dataeng
dc.typeArticle-
local.versionTypepublishedVersion-
local.bibliographicCitation.journaltitleScientific reports-
local.bibliographicCitation.volume10-
local.bibliographicCitation.issue14530-
local.bibliographicCitation.publishernameMacmillan Publishers Limited, part of Springer Nature-
local.bibliographicCitation.publisherplace[London]-
local.bibliographicCitation.doi10.1038/s41598-020-71539-0-
local.subject.keywordsRespiratory signs and symptoms, Signal processing, Time series-
local.openaccesstrue-
dc.identifier.ppn1735517682-
local.bibliographicCitation.year2020-
cbs.sru.importDate2023-07-04T12:09:46Z-
local.bibliographicCitationEnthalten in Scientific reports - [London] : Macmillan Publishers Limited, part of Springer Nature, 2011-
local.accessrights.dnbfree-
Enthalten in den Sammlungen:Open Access Publikationen der MLU

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