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Complexity analysis of human physiological signals based on case studies

Angelova, Maia, Holloway, Philip and Ellis, Jason 2015, Complexity analysis of human physiological signals based on case studies, in Group30 2014 : Proceedings of the 30th Group Theoretical Methods in Physics International Colloquium, Institute of Physics, Bristol, Eng., pp. 1-9, doi: 10.1088/1742-6596/597/1/012010.

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Title Complexity analysis of human physiological signals based on case studies
Author(s) Angelova, MaiaORCID iD for Angelova, Maia orcid.org/0000-0002-0931-0916
Holloway, Philip
Ellis, Jason
Conference name Group Theoretical Methods in Physics. International Colloquium (30th : 2014 : Ghent, Belgium)
Conference location Ghent, Belgium
Conference dates 2014/07/14 - 2014/07/18
Title of proceedings Group30 2014 : Proceedings of the 30th Group Theoretical Methods in Physics International Colloquium
Publication date 2015
Series Journal of physics: conference series
Conference series Group Theoretical Methods in Physics International Colloquium
Start page 1
End page 9
Total pages 9
Publisher Institute of Physics
Place of publication Bristol, Eng.
Summary This work focuses on methods for investigation of physiological time series based on complexity analysis. It is a part of a wider programme to determine non-invasive markers for healthy ageing. We consider two case studies investigated with actigraphy: (a) sleep and alternations with insomnia, and (b) ageing effects on mobility patterns. We illustrate, using these case studies, the application of fractal analysis to the investigation of regulation patterns and control, and change of physiological function. In the first case study, fractal analysis techniques were implemented to study the correlations present in sleep actigraphy for individuals suffering from acute insomnia in comparison with healthy controls. The aim was to investigate if complexity analysis can detect the onset of adverse health-related events. The subjects with acute insomnia displayed significantly higher levels of complexity, possibly a result of too much activity in the underlying regulatory systems. The second case study considered mobility patterns during night time and their variations with age. It showed that complexity metrics can identify change in physiological function with ageing. Both studies demonstrated that complexity analysis can be used to investigate markers of health, disease and healthy ageing.
Notes Article no: 012010
ISSN 1742-6588
1742-6596
Language eng
DOI 10.1088/1742-6596/597/1/012010
Field of Research 02 Physical Sciences
09 Engineering
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092687

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.