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Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts

Reliable P wave detection is necessary for accurate and automatic electrocardiogram (ECG) analysis. Currently, methods for P wave detection in physiological conditions are well-described and efficient. However, methods for P wave detection during pathology are not generally found in the literature,...

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Autores principales: Maršánová, Lucie, Němcová, Andrea, Smíšek, Radovan, Vítek, Martin, Smital, Lukáš
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6911105/
https://www.ncbi.nlm.nih.gov/pubmed/31836760
http://dx.doi.org/10.1038/s41598-019-55323-3
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author Maršánová, Lucie
Němcová, Andrea
Smíšek, Radovan
Vítek, Martin
Smital, Lukáš
author_facet Maršánová, Lucie
Němcová, Andrea
Smíšek, Radovan
Vítek, Martin
Smital, Lukáš
author_sort Maršánová, Lucie
collection PubMed
description Reliable P wave detection is necessary for accurate and automatic electrocardiogram (ECG) analysis. Currently, methods for P wave detection in physiological conditions are well-described and efficient. However, methods for P wave detection during pathology are not generally found in the literature, or their performance is insufficient. This work introduces a novel method, based on a phasor transform, as well as innovative rules that improve P wave detection during pathology. These rules are based on the extraction of a heartbeats’ morphological features and knowledge of heart manifestation during both physiological and pathological conditions. To properly evaluate the performance of the proposed algorithm in pathological conditions, a standard database with a sufficient number of reference P wave positions is needed. However, such a database did not exist. Thus, ECG experts annotated 12 chosen pathological records from the MIT-BIH Arrhythmia Database. These annotations are publicly available via Physionet. The algorithm performance was also validated using physiological records from the MIT-BIH Arrhythmia and QT databases. The results for physiological signals were Se = 98.42% and PP = 99.98%, which is comparable to other methods. For pathological signals, the proposed method reached Se = 96.40% and PP = 85.84%, which greatly outperforms other methods. This improvement represents a huge step towards fully automated analysis systems being respected by ECG experts. These systems are necessary, particularly in the area of long-term monitoring.
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spelling pubmed-69111052019-12-16 Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts Maršánová, Lucie Němcová, Andrea Smíšek, Radovan Vítek, Martin Smital, Lukáš Sci Rep Article Reliable P wave detection is necessary for accurate and automatic electrocardiogram (ECG) analysis. Currently, methods for P wave detection in physiological conditions are well-described and efficient. However, methods for P wave detection during pathology are not generally found in the literature, or their performance is insufficient. This work introduces a novel method, based on a phasor transform, as well as innovative rules that improve P wave detection during pathology. These rules are based on the extraction of a heartbeats’ morphological features and knowledge of heart manifestation during both physiological and pathological conditions. To properly evaluate the performance of the proposed algorithm in pathological conditions, a standard database with a sufficient number of reference P wave positions is needed. However, such a database did not exist. Thus, ECG experts annotated 12 chosen pathological records from the MIT-BIH Arrhythmia Database. These annotations are publicly available via Physionet. The algorithm performance was also validated using physiological records from the MIT-BIH Arrhythmia and QT databases. The results for physiological signals were Se = 98.42% and PP = 99.98%, which is comparable to other methods. For pathological signals, the proposed method reached Se = 96.40% and PP = 85.84%, which greatly outperforms other methods. This improvement represents a huge step towards fully automated analysis systems being respected by ECG experts. These systems are necessary, particularly in the area of long-term monitoring. Nature Publishing Group UK 2019-12-13 /pmc/articles/PMC6911105/ /pubmed/31836760 http://dx.doi.org/10.1038/s41598-019-55323-3 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Maršánová, Lucie
Němcová, Andrea
Smíšek, Radovan
Vítek, Martin
Smital, Lukáš
Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts
title Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts
title_full Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts
title_fullStr Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts
title_full_unstemmed Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts
title_short Advanced P Wave Detection in Ecg Signals During Pathology: Evaluation in Different Arrhythmia Contexts
title_sort advanced p wave detection in ecg signals during pathology: evaluation in different arrhythmia contexts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6911105/
https://www.ncbi.nlm.nih.gov/pubmed/31836760
http://dx.doi.org/10.1038/s41598-019-55323-3
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