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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....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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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. |
format | Online Article Text |
id | pubmed-4573734 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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