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The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities

The Poincaré plot is a geometrical technique used to visualize and quantify the correlation between two consecutive data points in a time-series. Since the dynamics of fluctuations in physiological rhythms exhibit long-term correlation and memory, this study aimed to extend the Poincaré plot by calc...

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Autores principales: Satti, Reem, Abid, Noor-Ul-Hoda, Bottaro, Matteo, De Rui, Michele, Garrido, Maria, Raoufy, Mohammad R., Montagnese, Sara, Mani, Ali R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6390508/
https://www.ncbi.nlm.nih.gov/pubmed/30837892
http://dx.doi.org/10.3389/fphys.2019.00116
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author Satti, Reem
Abid, Noor-Ul-Hoda
Bottaro, Matteo
De Rui, Michele
Garrido, Maria
Raoufy, Mohammad R.
Montagnese, Sara
Mani, Ali R.
author_facet Satti, Reem
Abid, Noor-Ul-Hoda
Bottaro, Matteo
De Rui, Michele
Garrido, Maria
Raoufy, Mohammad R.
Montagnese, Sara
Mani, Ali R.
author_sort Satti, Reem
collection PubMed
description The Poincaré plot is a geometrical technique used to visualize and quantify the correlation between two consecutive data points in a time-series. Since the dynamics of fluctuations in physiological rhythms exhibit long-term correlation and memory, this study aimed to extend the Poincaré plot by calculating the correlation between sequential data points in a time-series, rather than between two consecutive points. By incorporating this so-called lag, we hope to integrate a temporal aspect into quantifying the correlation, to depict whether a physiological system holds prolonged association between events separated by time. In doing so, it attempts to instantaneously characterize the intrinsic behavior of a complex system. We tested this hypothesis on three different physiological time-series: heart rate variability in patients with liver cirrhosis, respiratory rhythm in asthma and body temperature fluctuation in patients with cirrhosis, to evaluate the potential application of the extended Poincaré method in clinical practice. When studying the cardiac inter-beat intervals, the extended Poincaré plot revealed a stronger autocorrelation for patients with decompensated liver cirrhosis compared to less severe cases using Pearson’s correlation coefficient. In addition, long-term variability (known as SD2 in the extended Poincaré plot) appeared as an independent prognostic variable. This holds significance by acting as a non-invasive tool to evaluate patients with chronic liver disease and potentially facilitate transplant selection as an adjuvant to traditional criteria. For asthmatics, employing the extended Poincaré plot allowed for a non-invasive tool to differentially diagnose various classifications of respiratory disease. In the respiratory inter-breath interval analysis, the receiver operating characteristic (ROC) curve provided evidence that the extension of the Poincaré plot holds a greater advantage in the classification of asthmatic patients, over the traditional Poincaré plot. Lastly, the analysis of body temperature from patients using the extended Poincaré plot helped identify inpatients from outpatients with cirrhosis. Through these analyses, the extended Poincaré plot provided unique and additional information which could potentially make a difference in clinical practice. Conclusively, the potential use of our work lies in its possible application of predicting mortality for the organ allocation procedure in patients with cirrhosis and non-invasively distinguish between atopic and non-atopic asthma.
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spelling pubmed-63905082019-03-05 The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities Satti, Reem Abid, Noor-Ul-Hoda Bottaro, Matteo De Rui, Michele Garrido, Maria Raoufy, Mohammad R. Montagnese, Sara Mani, Ali R. Front Physiol Physiology The Poincaré plot is a geometrical technique used to visualize and quantify the correlation between two consecutive data points in a time-series. Since the dynamics of fluctuations in physiological rhythms exhibit long-term correlation and memory, this study aimed to extend the Poincaré plot by calculating the correlation between sequential data points in a time-series, rather than between two consecutive points. By incorporating this so-called lag, we hope to integrate a temporal aspect into quantifying the correlation, to depict whether a physiological system holds prolonged association between events separated by time. In doing so, it attempts to instantaneously characterize the intrinsic behavior of a complex system. We tested this hypothesis on three different physiological time-series: heart rate variability in patients with liver cirrhosis, respiratory rhythm in asthma and body temperature fluctuation in patients with cirrhosis, to evaluate the potential application of the extended Poincaré method in clinical practice. When studying the cardiac inter-beat intervals, the extended Poincaré plot revealed a stronger autocorrelation for patients with decompensated liver cirrhosis compared to less severe cases using Pearson’s correlation coefficient. In addition, long-term variability (known as SD2 in the extended Poincaré plot) appeared as an independent prognostic variable. This holds significance by acting as a non-invasive tool to evaluate patients with chronic liver disease and potentially facilitate transplant selection as an adjuvant to traditional criteria. For asthmatics, employing the extended Poincaré plot allowed for a non-invasive tool to differentially diagnose various classifications of respiratory disease. In the respiratory inter-breath interval analysis, the receiver operating characteristic (ROC) curve provided evidence that the extension of the Poincaré plot holds a greater advantage in the classification of asthmatic patients, over the traditional Poincaré plot. Lastly, the analysis of body temperature from patients using the extended Poincaré plot helped identify inpatients from outpatients with cirrhosis. Through these analyses, the extended Poincaré plot provided unique and additional information which could potentially make a difference in clinical practice. Conclusively, the potential use of our work lies in its possible application of predicting mortality for the organ allocation procedure in patients with cirrhosis and non-invasively distinguish between atopic and non-atopic asthma. Frontiers Media S.A. 2019-02-19 /pmc/articles/PMC6390508/ /pubmed/30837892 http://dx.doi.org/10.3389/fphys.2019.00116 Text en Copyright © 2019 Satti, Abid, Bottaro, De Rui, Garrido, Raoufy, Montagnese and Mani. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Satti, Reem
Abid, Noor-Ul-Hoda
Bottaro, Matteo
De Rui, Michele
Garrido, Maria
Raoufy, Mohammad R.
Montagnese, Sara
Mani, Ali R.
The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities
title The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities
title_full The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities
title_fullStr The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities
title_full_unstemmed The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities
title_short The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities
title_sort application of the extended poincaré plot in the analysis of physiological variabilities
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6390508/
https://www.ncbi.nlm.nih.gov/pubmed/30837892
http://dx.doi.org/10.3389/fphys.2019.00116
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