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Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological sys...

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Autores principales: Awan, Imtiaz, Aziz, Wajid, Shah, Imran Hussain, Habib, Nazneen, Alowibdi, Jalal S., Saeed, Sharjil, Nadeem, Malik Sajjad Ahmed, Shah, Syed Ahsin Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5957340/
https://www.ncbi.nlm.nih.gov/pubmed/29771977
http://dx.doi.org/10.1371/journal.pone.0196823
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author Awan, Imtiaz
Aziz, Wajid
Shah, Imran Hussain
Habib, Nazneen
Alowibdi, Jalal S.
Saeed, Sharjil
Nadeem, Malik Sajjad Ahmed
Shah, Syed Ahsin Ali
author_facet Awan, Imtiaz
Aziz, Wajid
Shah, Imran Hussain
Habib, Nazneen
Alowibdi, Jalal S.
Saeed, Sharjil
Nadeem, Malik Sajjad Ahmed
Shah, Syed Ahsin Ali
author_sort Awan, Imtiaz
collection PubMed
description Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features.
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spelling pubmed-59573402018-05-31 Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis Awan, Imtiaz Aziz, Wajid Shah, Imran Hussain Habib, Nazneen Alowibdi, Jalal S. Saeed, Sharjil Nadeem, Malik Sajjad Ahmed Shah, Syed Ahsin Ali PLoS One Research Article Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features. Public Library of Science 2018-05-17 /pmc/articles/PMC5957340/ /pubmed/29771977 http://dx.doi.org/10.1371/journal.pone.0196823 Text en © 2018 Awan 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
Awan, Imtiaz
Aziz, Wajid
Shah, Imran Hussain
Habib, Nazneen
Alowibdi, Jalal S.
Saeed, Sharjil
Nadeem, Malik Sajjad Ahmed
Shah, Syed Ahsin Ali
Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis
title Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis
title_full Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis
title_fullStr Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis
title_full_unstemmed Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis
title_short Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis
title_sort studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5957340/
https://www.ncbi.nlm.nih.gov/pubmed/29771977
http://dx.doi.org/10.1371/journal.pone.0196823
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