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NONLINEAR TIME SERIES ANALYSIS USING ORDINAL NETWORKS WITH SELECT APPLICATIONS IN BIOMEDICAL SIGNAL PROCESSING

Published online by Cambridge University Press:  17 May 2019

MICHAEL HUGH MCCULLOUGH*
Affiliation:
Queensland Brain Institute, University of Queensland, St Lucia 4072, Queensland, Australia email [email protected]
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Abstract

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Type
Abstracts of Australasian PhD Theses
Copyright
© 2019 Australian Mathematical Publishing Association Inc. 

Footnotes

Thesis submitted to The University of Western Australia in October 2017; degree approved 29 January 2018; coordinating supervisor Michael Small, supervisors Thomas Stemler and Herbert Iu.

References

McCullough, M., Sakellariou, K., Stemler, T. and Small, M., ‘Counting forbidden patterns in irregularly sampled time series. I. The effects of under-sampling, random depletion, and timing jitter’, Chaos 26(12) (2016), Article ID 123103.Google Scholar
McCullough, M., Sakellariou, K., Stemler, T. and Small, M., ‘Regenerating time series from ordinal networks’, Chaos 27(3) (2017), Article ID 035814.Google Scholar
McCullough, M., Small, M., Iu, H. and Stemler, T., ‘Multiscale ordinal network analysis of human cardiac dynamics’, Philos. Trans. R. Soc. Lond. Ser. A 375(2096) (2017), Article ID 20160292, 17 pages.Google Scholar
McCullough, M., Small, M., Stemler, T. and Iu, H. H.-C., ‘Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems’, Chaos 25(5) (2015), Article ID 053101.Google Scholar
Small, M., McCullough, M. and Sakellariou, K., ‘Ordinal network measures—quantifying determinism in data’, in: 2018 IEEE International Symposium on Circuits and Systems (ISCAS) (IEEE, New York, 2018), 15.Google Scholar