Optimising approximate entropy for assessing cardiac dyssynchrony with radionuclide ventriculography

Jones, K.A. , Paterson, C.A., Hamilton, D.J. , Small, A.D., Martin, W., Robinson, J. and Goodfield, N.E.R. (2021) Optimising approximate entropy for assessing cardiac dyssynchrony with radionuclide ventriculography. Biomedical Signal Processing and Control, 68, 102703. (doi: 10.1016/j.bspc.2021.102703)

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Left ventricular dyssynchrony can be assessed with phase parameters from radionuclide ventriculography (RNVG), including approximate entropy (ApEn). The input values used to calculate ApEn will affect the results significantly, so it is essential to optimise ApEn for the application. However to date, no optimisation for ApEn applied to images has been published. In this paper, generated data were used to simulate patient phase images, allowing the input parameters for ApEn to be tested and optimised in a controlled environment. Clinical images were then used to confirm that the selected parameters were appropriate. The results demonstrate the effect of input parameters for ApEn and the most appropriate use with RNVG phase images. This work demonstrates the importance of optimisation and standardisation when using ApEn as a measure of dyssynchrony.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Martin, Dr William and Jones, Kirsty and Robinson, Mr Jamie and Paterson, Mr Craig and Small, Dr Alexander and Hamilton, Dr David
Creator Roles:
Jones, K.Conceptualization, Software, Visualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review and editing
Paterson, C.Supervision, Conceptualization, Writing – review and editing
Hamilton, D.Supervision, Software, Writing – review and editing
Small, A.Supervision, Conceptualization, Writing – review and editing
Martin, W.Conceptualization, Data curation
Robinson, J.Writing – review and editing
Authors: Jones, K.A., Paterson, C.A., Hamilton, D.J., Small, A.D., Martin, W., Robinson, J., and Goodfield, N.E.R.
College/School:College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
College of Science and Engineering
College of Science and Engineering > School of Physics and Astronomy
Journal Name:Biomedical Signal Processing and Control
ISSN (Online):1746-8108
Published Online:11 May 2021
Copyright Holders:Copyright © 2021 Elsevier Ltd.
First Published:First published in Biomedical Signal Processing and Control 68: 102703
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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