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Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events
BACKGROUND: Screening the general public for atrial fibrillation (AF) may enable early detection and timely intervention, which could potentially decrease the incidence of stroke. Existing screening methods require professional monitoring and involve high costs. AF is characterized by an irregular i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7930338/ https://www.ncbi.nlm.nih.gov/pubmed/33679450 http://dx.doi.org/10.3389/fphys.2021.637680 |
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author | Keidar, Noam Elul, Yonatan Schuster, Assaf Yaniv, Yael |
author_facet | Keidar, Noam Elul, Yonatan Schuster, Assaf Yaniv, Yael |
author_sort | Keidar, Noam |
collection | PubMed |
description | BACKGROUND: Screening the general public for atrial fibrillation (AF) may enable early detection and timely intervention, which could potentially decrease the incidence of stroke. Existing screening methods require professional monitoring and involve high costs. AF is characterized by an irregular irregularity of the cardiac rhythm, which may be detectable using an index quantifying and visualizing this type of irregularity, motivating wide screening programs and promoting the research of AF patient subgroups and clinical impact of AF burden. METHODS: We calculated variability, normality and mean of the difference between consecutive RR interval series (denoted as modified entropy scale—MESC) to quantify irregular irregularities. Based on the variability and normality indices calculated for long 1-lead ECG records, we created a plot termed a regularogram (RGG), which provides a visual presentation of irregularly irregular rates and their burden in a given record. To inspect the potency of these indices, they were applied to train and test a machine learning classifier to identify AF episodes in gold-standard, publicly available databases (PhysioNet) that include recordings from both patients with AF and/or other rhythm disturbances, and from healthy volunteers. The classifier was trained and validated on one database and tested on three other databases. RESULTS: Irregular irregularities were identified using normality, variability and mean MESC indices. The RGG displayed visually distinct differences between patients with vs. without AF and between patients with different levels of AF burden. Training a simple, explainable machine learning tool integrating these three indices enabled AF detection with 99.9% accuracy, when trained on the same person, and 97.8%, when trained on patients from a different database. Comparison to other RR interval-based AF detection methods that utilize signal processing, classic machine learning and deep learning techniques, showed superiority of our suggested method. CONCLUSION: Visualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available. |
format | Online Article Text |
id | pubmed-7930338 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79303382021-03-05 Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events Keidar, Noam Elul, Yonatan Schuster, Assaf Yaniv, Yael Front Physiol Physiology BACKGROUND: Screening the general public for atrial fibrillation (AF) may enable early detection and timely intervention, which could potentially decrease the incidence of stroke. Existing screening methods require professional monitoring and involve high costs. AF is characterized by an irregular irregularity of the cardiac rhythm, which may be detectable using an index quantifying and visualizing this type of irregularity, motivating wide screening programs and promoting the research of AF patient subgroups and clinical impact of AF burden. METHODS: We calculated variability, normality and mean of the difference between consecutive RR interval series (denoted as modified entropy scale—MESC) to quantify irregular irregularities. Based on the variability and normality indices calculated for long 1-lead ECG records, we created a plot termed a regularogram (RGG), which provides a visual presentation of irregularly irregular rates and their burden in a given record. To inspect the potency of these indices, they were applied to train and test a machine learning classifier to identify AF episodes in gold-standard, publicly available databases (PhysioNet) that include recordings from both patients with AF and/or other rhythm disturbances, and from healthy volunteers. The classifier was trained and validated on one database and tested on three other databases. RESULTS: Irregular irregularities were identified using normality, variability and mean MESC indices. The RGG displayed visually distinct differences between patients with vs. without AF and between patients with different levels of AF burden. Training a simple, explainable machine learning tool integrating these three indices enabled AF detection with 99.9% accuracy, when trained on the same person, and 97.8%, when trained on patients from a different database. Comparison to other RR interval-based AF detection methods that utilize signal processing, classic machine learning and deep learning techniques, showed superiority of our suggested method. CONCLUSION: Visualizing and quantifying irregular irregularities will be of value for both rapid visual inspection of long Holter recordings for the presence and the burden of AF, and for machine learning classification to identify AF episodes. A free online tool for calculating the indices, drawing RGGs and estimating AF burden, is available. Frontiers Media S.A. 2021-02-18 /pmc/articles/PMC7930338/ /pubmed/33679450 http://dx.doi.org/10.3389/fphys.2021.637680 Text en Copyright © 2021 Keidar, Elul, Schuster and Yaniv. 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 Keidar, Noam Elul, Yonatan Schuster, Assaf Yaniv, Yael Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events |
title | Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events |
title_full | Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events |
title_fullStr | Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events |
title_full_unstemmed | Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events |
title_short | Visualizing and Quantifying Irregular Heart Rate Irregularities to Identify Atrial Fibrillation Events |
title_sort | visualizing and quantifying irregular heart rate irregularities to identify atrial fibrillation events |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7930338/ https://www.ncbi.nlm.nih.gov/pubmed/33679450 http://dx.doi.org/10.3389/fphys.2021.637680 |
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