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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7746153 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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