Cargando…

A Detector for Premature Atrial and Ventricular Complexes

The relationship between premature atrial complexes (PACs) and atrial fibrillation (AF), stroke and myocardium degradation is unclear. Current PAC detectors are beat classifiers that attain low sensitivity on PAC detection. The lack of a proper PAC detector hinders the study of the implications of t...

Descripción completa

Detalles Bibliográficos
Autores principales: García-Isla, Guadalupe, Mainardi, Luca, Corino, Valentina D. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8243653/
https://www.ncbi.nlm.nih.gov/pubmed/34220543
http://dx.doi.org/10.3389/fphys.2021.678558
_version_ 1783715788904988672
author García-Isla, Guadalupe
Mainardi, Luca
Corino, Valentina D. A.
author_facet García-Isla, Guadalupe
Mainardi, Luca
Corino, Valentina D. A.
author_sort García-Isla, Guadalupe
collection PubMed
description The relationship between premature atrial complexes (PACs) and atrial fibrillation (AF), stroke and myocardium degradation is unclear. Current PAC detectors are beat classifiers that attain low sensitivity on PAC detection. The lack of a proper PAC detector hinders the study of the implications of this event and its monitoring. In this work a PAC and ventricular detector is presented. Two PhysioNet open-source databases were used: the long-term ST database (LTSTDB) and the supraventricular arrhythmia database (SVDB). A combination of heart rate variability (HRV) and morphological features were used to classify beats. Morphological features were extracted from the ECG as well as on the 4th scale of the discrete wavelet transform (DWT). After feature selection, a random forest algorithm was trained for a binary classification of PAC (S) vs. others and for a multi-labels classification to discriminate between normal (N), S and ventricular (V) beats. The algorithm was tested in a 10-fold cross-validation following a patient-wise train-test division (i.e., no beats belonging to the same patient were included both in the test and train set). The resultant median sensitivity, specificity and positive predictive value (PPV) were 99.29, 99.54, and 100% for (N), 95.83, 99.39, and 35.68% for (S), 100, 99.90, and 79.63% for (V). The proposed method attains a greater PAC and ventricular beat sensitivity and PPV than the state-of-the-art classifiers.
format Online
Article
Text
id pubmed-8243653
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-82436532021-07-01 A Detector for Premature Atrial and Ventricular Complexes García-Isla, Guadalupe Mainardi, Luca Corino, Valentina D. A. Front Physiol Physiology The relationship between premature atrial complexes (PACs) and atrial fibrillation (AF), stroke and myocardium degradation is unclear. Current PAC detectors are beat classifiers that attain low sensitivity on PAC detection. The lack of a proper PAC detector hinders the study of the implications of this event and its monitoring. In this work a PAC and ventricular detector is presented. Two PhysioNet open-source databases were used: the long-term ST database (LTSTDB) and the supraventricular arrhythmia database (SVDB). A combination of heart rate variability (HRV) and morphological features were used to classify beats. Morphological features were extracted from the ECG as well as on the 4th scale of the discrete wavelet transform (DWT). After feature selection, a random forest algorithm was trained for a binary classification of PAC (S) vs. others and for a multi-labels classification to discriminate between normal (N), S and ventricular (V) beats. The algorithm was tested in a 10-fold cross-validation following a patient-wise train-test division (i.e., no beats belonging to the same patient were included both in the test and train set). The resultant median sensitivity, specificity and positive predictive value (PPV) were 99.29, 99.54, and 100% for (N), 95.83, 99.39, and 35.68% for (S), 100, 99.90, and 79.63% for (V). The proposed method attains a greater PAC and ventricular beat sensitivity and PPV than the state-of-the-art classifiers. Frontiers Media S.A. 2021-06-16 /pmc/articles/PMC8243653/ /pubmed/34220543 http://dx.doi.org/10.3389/fphys.2021.678558 Text en Copyright © 2021 García-Isla, Mainardi and Corino. https://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
García-Isla, Guadalupe
Mainardi, Luca
Corino, Valentina D. A.
A Detector for Premature Atrial and Ventricular Complexes
title A Detector for Premature Atrial and Ventricular Complexes
title_full A Detector for Premature Atrial and Ventricular Complexes
title_fullStr A Detector for Premature Atrial and Ventricular Complexes
title_full_unstemmed A Detector for Premature Atrial and Ventricular Complexes
title_short A Detector for Premature Atrial and Ventricular Complexes
title_sort detector for premature atrial and ventricular complexes
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8243653/
https://www.ncbi.nlm.nih.gov/pubmed/34220543
http://dx.doi.org/10.3389/fphys.2021.678558
work_keys_str_mv AT garciaislaguadalupe adetectorforprematureatrialandventricularcomplexes
AT mainardiluca adetectorforprematureatrialandventricularcomplexes
AT corinovalentinada adetectorforprematureatrialandventricularcomplexes
AT garciaislaguadalupe detectorforprematureatrialandventricularcomplexes
AT mainardiluca detectorforprematureatrialandventricularcomplexes
AT corinovalentinada detectorforprematureatrialandventricularcomplexes