Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/110387
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZschocke, Johannes-
dc.contributor.authorBartsch, Ronny-
dc.contributor.authorGlos, Martin-
dc.contributor.authorPenzel, Thomas-
dc.contributor.authorMikolajczyk, Rafael-
dc.contributor.authorKantelhardt, Jan W.-
dc.date.accessioned2023-09-13T06:14:59Z-
dc.date.available2023-09-13T06:14:59Z-
dc.date.issued2022-
dc.identifier.urihttps://opendata.uni-halle.de//handle/1981185920/112342-
dc.identifier.urihttp://dx.doi.org/10.25673/110387-
dc.description.abstractSome details of cardiovascular and cardio-respiratory regulation and their changes during different sleep stages remain still unknown. In this paper we compared the fluctuations of heart rate, pulse rate, respiration frequency, and pulse transit times as well as EEG alpha-band power on time scales from 6 to 200 s during different sleep stages in order to better understand regulatory pathways. The five considered time series were derived from ECG, photoplethysmogram, nasal air flow, and central electrode EEG measurements from full-night polysomnography recordings of 246 subjects with suspected sleep disorders. We applied detrended fluctuation analysis, distinguishing between short-term (6–16 s) and long-term (50–200 s) correlations, i.e., scaling behavior characterized by the fluctuation exponents α1 and α2 related with parasympathetic and sympathetic control, respectively. While heart rate (and pulse rate) are characterized by sex and age-dependent short-term correlations, their long-term correlations exhibit the well-known sleep stage dependence: weak long-term correlations during non-REM sleep and pronounced long-term correlations during REM sleep and wakefulness. In contrast, pulse transit times, which are believed to be mainly affected by blood pressure and arterial stiffness, do not show differences between short-term and long-term exponents. This is in constrast to previous results for blood pressure time series, where α1 was much larger than α2, and therefore questions a very close relation between pulse transit times and blood pressure values. Nevertheless, very similar sleep-stage dependent differences are observed for the long-term fluctuation exponent α2 in all considered signals including EEG alpha-band power. In conclusion, we found that the observed fluctuation exponents are very robust and hardly modified by body mass index, alcohol consumption, smoking, or sleep disorders. The long-term fluctuations of all observed systems seem to be modulated by patterns following sleep stages generated in the brain and thus regulated in a similar manner, while short-term regulations differ between the organ systems. Deviations from the reported dependence in any of the signals should be indicative of problems in the function of the particular organ system or its control mechanisms.eng
dc.language.isoeng-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subject.ddc610-
dc.titleLong- and short-term fluctuations compared for several organ systems across sleep stageseng
dc.typeArticle-
local.versionTypepublishedVersion-
local.bibliographicCitation.journaltitleFrontiers in network physiology-
local.bibliographicCitation.volume2-
local.bibliographicCitation.pagestart1-
local.bibliographicCitation.pageend12-
local.bibliographicCitation.publishernameFrontiers Media-
local.bibliographicCitation.publisherplaceLausanne-
local.bibliographicCitation.doi10.3389/fnetp.2022.937130-
local.subject.keywordsTime series analysis, long-term correlations, persistence, scaling analysis, heartbeat, pulse-transit time, respiration, brain-wave amplitudes-
local.openaccesstrue-
dc.identifier.ppn1859212727-
cbs.publication.displayform2022-
local.bibliographicCitation.year2022-
cbs.sru.importDate2023-09-13T06:14:29Z-
local.bibliographicCitationEnthalten in Frontiers in network physiology - Lausanne : Frontiers Media, 2021-
local.accessrights.dnbfree-
Appears in Collections:Open Access Publikationen der MLU

Files in This Item:
File Description SizeFormat 
fnetp-02-937130.pdf1.64 MBAdobe PDFThumbnail
View/Open