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Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series

BACKGROUND: The detection of change in magnitude of directional coupling between two non-linear time series is a common subject of interest in the biomedical domain, including studies involving the respiratory chemoreflex system. Although transfer entropy is a useful tool in this avenue, no study to...

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Autores principales: Lee, Joon, Nemati, Shamim, Silva, Ikaro, Edwards, Bradley A, Butler, James P, Malhotra, Atul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403001/
https://www.ncbi.nlm.nih.gov/pubmed/22500692
http://dx.doi.org/10.1186/1475-925X-11-19
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author Lee, Joon
Nemati, Shamim
Silva, Ikaro
Edwards, Bradley A
Butler, James P
Malhotra, Atul
author_facet Lee, Joon
Nemati, Shamim
Silva, Ikaro
Edwards, Bradley A
Butler, James P
Malhotra, Atul
author_sort Lee, Joon
collection PubMed
description BACKGROUND: The detection of change in magnitude of directional coupling between two non-linear time series is a common subject of interest in the biomedical domain, including studies involving the respiratory chemoreflex system. Although transfer entropy is a useful tool in this avenue, no study to date has investigated how different transfer entropy estimation methods perform in typical biomedical applications featuring small sample size and presence of outliers. METHODS: With respect to detection of increased coupling strength, we compared three transfer entropy estimation techniques using both simulated time series and respiratory recordings from lambs. The following estimation methods were analyzed: fixed-binning with ranking, kernel density estimation (KDE), and the Darbellay-Vajda (D-V) adaptive partitioning algorithm extended to three dimensions. In the simulated experiment, sample size was varied from 50 to 200, while coupling strength was increased. In order to introduce outliers, the heavy-tailed Laplace distribution was utilized. In the lamb experiment, the objective was to detect increased respiratory-related chemosensitivity to O(2 )and CO(2 )induced by a drug, domperidone. Specifically, the separate influence of end-tidal PO(2 )and PCO(2 )on minute ventilation [Formula: see text] before and after administration of domperidone was analyzed. RESULTS: In the simulation, KDE detected increased coupling strength at the lowest SNR among the three methods. In the lamb experiment, D-V partitioning resulted in the statistically strongest increase in transfer entropy post-domperidone for [Formula: see text]. In addition, D-V partitioning was the only method that could detect an increase in transfer entropy for [Formula: see text] , in agreement with experimental findings. CONCLUSIONS: Transfer entropy is capable of detecting directional coupling changes in non-linear biomedical time series analysis featuring a small number of observations and presence of outliers. The results of this study suggest that fixed-binning, even with ranking, is too primitive, and although there is no clear winner between KDE and D-V partitioning, the reader should note that KDE requires more computational time and extensive parameter selection than D-V partitioning. We hope this study provides a guideline for selection of an appropriate transfer entropy estimation method.
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spelling pubmed-34030012012-07-25 Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series Lee, Joon Nemati, Shamim Silva, Ikaro Edwards, Bradley A Butler, James P Malhotra, Atul Biomed Eng Online Research BACKGROUND: The detection of change in magnitude of directional coupling between two non-linear time series is a common subject of interest in the biomedical domain, including studies involving the respiratory chemoreflex system. Although transfer entropy is a useful tool in this avenue, no study to date has investigated how different transfer entropy estimation methods perform in typical biomedical applications featuring small sample size and presence of outliers. METHODS: With respect to detection of increased coupling strength, we compared three transfer entropy estimation techniques using both simulated time series and respiratory recordings from lambs. The following estimation methods were analyzed: fixed-binning with ranking, kernel density estimation (KDE), and the Darbellay-Vajda (D-V) adaptive partitioning algorithm extended to three dimensions. In the simulated experiment, sample size was varied from 50 to 200, while coupling strength was increased. In order to introduce outliers, the heavy-tailed Laplace distribution was utilized. In the lamb experiment, the objective was to detect increased respiratory-related chemosensitivity to O(2 )and CO(2 )induced by a drug, domperidone. Specifically, the separate influence of end-tidal PO(2 )and PCO(2 )on minute ventilation [Formula: see text] before and after administration of domperidone was analyzed. RESULTS: In the simulation, KDE detected increased coupling strength at the lowest SNR among the three methods. In the lamb experiment, D-V partitioning resulted in the statistically strongest increase in transfer entropy post-domperidone for [Formula: see text]. In addition, D-V partitioning was the only method that could detect an increase in transfer entropy for [Formula: see text] , in agreement with experimental findings. CONCLUSIONS: Transfer entropy is capable of detecting directional coupling changes in non-linear biomedical time series analysis featuring a small number of observations and presence of outliers. The results of this study suggest that fixed-binning, even with ranking, is too primitive, and although there is no clear winner between KDE and D-V partitioning, the reader should note that KDE requires more computational time and extensive parameter selection than D-V partitioning. We hope this study provides a guideline for selection of an appropriate transfer entropy estimation method. BioMed Central 2012-04-13 /pmc/articles/PMC3403001/ /pubmed/22500692 http://dx.doi.org/10.1186/1475-925X-11-19 Text en Copyright ©2012 Lee et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Lee, Joon
Nemati, Shamim
Silva, Ikaro
Edwards, Bradley A
Butler, James P
Malhotra, Atul
Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series
title Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series
title_full Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series
title_fullStr Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series
title_full_unstemmed Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series
title_short Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series
title_sort transfer entropy estimation and directional coupling change detection in biomedical time series
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403001/
https://www.ncbi.nlm.nih.gov/pubmed/22500692
http://dx.doi.org/10.1186/1475-925X-11-19
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