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Multiscale based nonlinear dynamics analysis of heart rate variability signals

Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple ti...

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Autores principales: Kazmi, Syed Zaki Hassan, Habib, Nazneen, Riaz, Rabia, Rizvi, Sanam Shahla, Abbas, Syed Ali, Chung, Tae-Sun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746153/
https://www.ncbi.nlm.nih.gov/pubmed/33332361
http://dx.doi.org/10.1371/journal.pone.0243441
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author Kazmi, Syed Zaki Hassan
Habib, Nazneen
Riaz, Rabia
Rizvi, Sanam Shahla
Abbas, Syed Ali
Chung, Tae-Sun
author_facet Kazmi, Syed Zaki Hassan
Habib, Nazneen
Riaz, Rabia
Rizvi, Sanam Shahla
Abbas, Syed Ali
Chung, Tae-Sun
author_sort Kazmi, Syed Zaki Hassan
collection PubMed
description Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.
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spelling pubmed-77461532020-12-31 Multiscale based nonlinear dynamics analysis of heart rate variability signals Kazmi, Syed Zaki Hassan Habib, Nazneen Riaz, Rabia Rizvi, Sanam Shahla Abbas, Syed Ali Chung, Tae-Sun PLoS One Research Article Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy. Public Library of Science 2020-12-17 /pmc/articles/PMC7746153/ /pubmed/33332361 http://dx.doi.org/10.1371/journal.pone.0243441 Text en © 2020 Hassan Kazmi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kazmi, Syed Zaki Hassan
Habib, Nazneen
Riaz, Rabia
Rizvi, Sanam Shahla
Abbas, Syed Ali
Chung, Tae-Sun
Multiscale based nonlinear dynamics analysis of heart rate variability signals
title Multiscale based nonlinear dynamics analysis of heart rate variability signals
title_full Multiscale based nonlinear dynamics analysis of heart rate variability signals
title_fullStr Multiscale based nonlinear dynamics analysis of heart rate variability signals
title_full_unstemmed Multiscale based nonlinear dynamics analysis of heart rate variability signals
title_short Multiscale based nonlinear dynamics analysis of heart rate variability signals
title_sort multiscale based nonlinear dynamics analysis of heart rate variability signals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746153/
https://www.ncbi.nlm.nih.gov/pubmed/33332361
http://dx.doi.org/10.1371/journal.pone.0243441
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