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A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate

Atrial fibrillation (AF), the most frequent cause of cardioembolic stroke, is increasing in prevalence as the population ages, and presents with a broad spectrum of symptoms and severity. The early identification of AF is an essential part for preventing the possibility of blood clotting and stroke....

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Autores principales: Zhou, Xiaolin, Ding, Hongxia, Wu, Wanqing, Zhang, Yuanting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4573734/
https://www.ncbi.nlm.nih.gov/pubmed/26376341
http://dx.doi.org/10.1371/journal.pone.0136544
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author Zhou, Xiaolin
Ding, Hongxia
Wu, Wanqing
Zhang, Yuanting
author_facet Zhou, Xiaolin
Ding, Hongxia
Wu, Wanqing
Zhang, Yuanting
author_sort Zhou, Xiaolin
collection PubMed
description Atrial fibrillation (AF), the most frequent cause of cardioembolic stroke, is increasing in prevalence as the population ages, and presents with a broad spectrum of symptoms and severity. The early identification of AF is an essential part for preventing the possibility of blood clotting and stroke. In this work, a real-time algorithm is proposed for accurately screening AF episodes in electrocardiograms. This method adopts heart rate sequence, and it involves the application of symbolic dynamics and Shannon entropy. Using novel recursive algorithms, a low-computational complexity can be obtained. Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. The first database was selected as a training set; the receiver operating characteristic (ROC) curve was performed, and the best performance was achieved at the threshold of 0.639: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.14%, 95.73%, 97.03% and 95.97%, respectively. The other three databases were used for independent testing. Using the obtained decision-making threshold (i.e., 0.639), for the second set, the obtained parameters were 97.37%, 98.44%, 97.89% and 97.99%, respectively; for the third database, these parameters were 97.83%, 87.41%, 47.67% and 88.51%, respectively; the Sp was 99.68% for the fourth set. The latest methods were also employed for comparison. Collectively, results presented in this study indicate that the combination of symbolic dynamics and Shannon entropy yields a potent AF detector, and suggest this method could be of practical use in both clinical and out-of-clinical settings.
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spelling pubmed-45737342015-09-18 A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate Zhou, Xiaolin Ding, Hongxia Wu, Wanqing Zhang, Yuanting PLoS One Research Article Atrial fibrillation (AF), the most frequent cause of cardioembolic stroke, is increasing in prevalence as the population ages, and presents with a broad spectrum of symptoms and severity. The early identification of AF is an essential part for preventing the possibility of blood clotting and stroke. In this work, a real-time algorithm is proposed for accurately screening AF episodes in electrocardiograms. This method adopts heart rate sequence, and it involves the application of symbolic dynamics and Shannon entropy. Using novel recursive algorithms, a low-computational complexity can be obtained. Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. The first database was selected as a training set; the receiver operating characteristic (ROC) curve was performed, and the best performance was achieved at the threshold of 0.639: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.14%, 95.73%, 97.03% and 95.97%, respectively. The other three databases were used for independent testing. Using the obtained decision-making threshold (i.e., 0.639), for the second set, the obtained parameters were 97.37%, 98.44%, 97.89% and 97.99%, respectively; for the third database, these parameters were 97.83%, 87.41%, 47.67% and 88.51%, respectively; the Sp was 99.68% for the fourth set. The latest methods were also employed for comparison. Collectively, results presented in this study indicate that the combination of symbolic dynamics and Shannon entropy yields a potent AF detector, and suggest this method could be of practical use in both clinical and out-of-clinical settings. Public Library of Science 2015-09-16 /pmc/articles/PMC4573734/ /pubmed/26376341 http://dx.doi.org/10.1371/journal.pone.0136544 Text en © 2015 Zhou 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhou, Xiaolin
Ding, Hongxia
Wu, Wanqing
Zhang, Yuanting
A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate
title A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate
title_full A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate
title_fullStr A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate
title_full_unstemmed A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate
title_short A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate
title_sort real-time atrial fibrillation detection algorithm based on the instantaneous state of heart rate
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4573734/
https://www.ncbi.nlm.nih.gov/pubmed/26376341
http://dx.doi.org/10.1371/journal.pone.0136544
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