Show simple item record

dc.contributor.authorMayor, David
dc.contributor.authorPanday, Deepak
dc.contributor.authorKandel, Hari Kala
dc.contributor.authorSteffert, Tony
dc.contributor.authorBanks, Duncan
dc.contributor.authorWatson, Tim
dc.date.accessioned2021-04-27T23:10:57Z
dc.date.available2021-04-27T23:10:57Z
dc.date.issued2021-03-08
dc.identifier.citationMayor , D , Panday , D , Kandel , H K , Steffert , T , Banks , D & Watson , T 2021 , ' CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals ' , Entropy , vol. 23 , no. 3 , 321 . https://doi.org/10.3390/e23030321
dc.identifier.issn1099-4300
dc.identifier.otherORCID: /0000-0002-1332-9337/work/125979217
dc.identifier.urihttp://hdl.handle.net/2299/24329
dc.description© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.description.abstractBackground: We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. Methods: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. Results: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ (‘tau’) where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. Conclusions: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathingen
dc.format.extent34
dc.format.extent3130640
dc.language.isoeng
dc.relation.ispartofEntropy
dc.titleCEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signalsen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Allied Health Professions, Midwifery and Social Work
dc.contributor.institutionSchool of Health and Social Work
dc.contributor.institutionPhysiotherapy
dc.contributor.institutionBiocomputation Research Group
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.3390/e23030321
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record