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Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

Implantable-cardioverter defibrillators (ICD) detect and terminate life-threatening ventricular tachyarrhythmia with electric shocks after they occur. This puts patients at risk if they are driving or in a situation where they can fall. ICD’s shocks are also very painful and affect a patient’s quali...

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Autores principales: Au-Yeung, Wan-Tai M., Reinhall, Per G., Bardy, Gust H., Brunton, Steven L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6235358/
https://www.ncbi.nlm.nih.gov/pubmed/30427880
http://dx.doi.org/10.1371/journal.pone.0207215
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author Au-Yeung, Wan-Tai M.
Reinhall, Per G.
Bardy, Gust H.
Brunton, Steven L.
author_facet Au-Yeung, Wan-Tai M.
Reinhall, Per G.
Bardy, Gust H.
Brunton, Steven L.
author_sort Au-Yeung, Wan-Tai M.
collection PubMed
description Implantable-cardioverter defibrillators (ICD) detect and terminate life-threatening ventricular tachyarrhythmia with electric shocks after they occur. This puts patients at risk if they are driving or in a situation where they can fall. ICD’s shocks are also very painful and affect a patient’s quality of life. It would be ideal if ICDs can accurately predict the occurrence of ventricular tachyarrhythmia and then issue a warning or provide preventive therapy. Our study explores the use of ICD data to automatically predict ventricular arrhythmia using heart rate variability (HRV). A 5 minute and a 10 second warning system are both developed and compared. The participants for this study consist of 788 patients who were enrolled in the ICD arm of the Sudden Cardiac Death–Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed and different HRV features are extracted. Machine learning algorithms, including random forests (RF) and support vector machines (SVM), are trained on these features to classify the two groups of rhythms in a subset of the data comprising the training set. These algorithms are then used to classify rhythms in a separate test set. This performance is quantified by the area under the curve (AUC) of the ROC curve. Both RF and SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean AUC of 0.87–0.88 for 10-second prediction; an AUC over 0.8 typically warrants further clinical investigation. Our work shows that moderate classification accuracy can be achieved to predict ventricular tachyarrhythmia with machine learning algorithms using HRV features from ICD data. These results provide a realistic view of the practical challenges facing implementation of machine learning algorithms to predict ventricular tachyarrhythmia using HRV data, motivating continued research on improved algorithms and additional features with higher predictive power.
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spelling pubmed-62353582018-12-01 Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data Au-Yeung, Wan-Tai M. Reinhall, Per G. Bardy, Gust H. Brunton, Steven L. PLoS One Research Article Implantable-cardioverter defibrillators (ICD) detect and terminate life-threatening ventricular tachyarrhythmia with electric shocks after they occur. This puts patients at risk if they are driving or in a situation where they can fall. ICD’s shocks are also very painful and affect a patient’s quality of life. It would be ideal if ICDs can accurately predict the occurrence of ventricular tachyarrhythmia and then issue a warning or provide preventive therapy. Our study explores the use of ICD data to automatically predict ventricular arrhythmia using heart rate variability (HRV). A 5 minute and a 10 second warning system are both developed and compared. The participants for this study consist of 788 patients who were enrolled in the ICD arm of the Sudden Cardiac Death–Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed and different HRV features are extracted. Machine learning algorithms, including random forests (RF) and support vector machines (SVM), are trained on these features to classify the two groups of rhythms in a subset of the data comprising the training set. These algorithms are then used to classify rhythms in a separate test set. This performance is quantified by the area under the curve (AUC) of the ROC curve. Both RF and SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean AUC of 0.87–0.88 for 10-second prediction; an AUC over 0.8 typically warrants further clinical investigation. Our work shows that moderate classification accuracy can be achieved to predict ventricular tachyarrhythmia with machine learning algorithms using HRV features from ICD data. These results provide a realistic view of the practical challenges facing implementation of machine learning algorithms to predict ventricular tachyarrhythmia using HRV data, motivating continued research on improved algorithms and additional features with higher predictive power. Public Library of Science 2018-11-14 /pmc/articles/PMC6235358/ /pubmed/30427880 http://dx.doi.org/10.1371/journal.pone.0207215 Text en © 2018 Au-Yeung 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
Au-Yeung, Wan-Tai M.
Reinhall, Per G.
Bardy, Gust H.
Brunton, Steven L.
Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
title Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
title_full Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
title_fullStr Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
title_full_unstemmed Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
title_short Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
title_sort development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6235358/
https://www.ncbi.nlm.nih.gov/pubmed/30427880
http://dx.doi.org/10.1371/journal.pone.0207215
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