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Reliable P wave detection in pathological ECG signals

Accurate automated detection of P waves in ECG allows to provide fast correct diagnosis of various cardiac arrhythmias and select suitable strategy for patients’ treatment. However, P waves detection is a still challenging task, especially in long-term ECGs with manifested cardiac pathologies. Softw...

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Autores principales: Saclova, Lucie, Nemcova, Andrea, Smisek, Radovan, Smital, Lukas, Vitek, Martin, Ronzhina, Marina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023481/
https://www.ncbi.nlm.nih.gov/pubmed/35449228
http://dx.doi.org/10.1038/s41598-022-10656-4
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author Saclova, Lucie
Nemcova, Andrea
Smisek, Radovan
Smital, Lukas
Vitek, Martin
Ronzhina, Marina
author_facet Saclova, Lucie
Nemcova, Andrea
Smisek, Radovan
Smital, Lukas
Vitek, Martin
Ronzhina, Marina
author_sort Saclova, Lucie
collection PubMed
description Accurate automated detection of P waves in ECG allows to provide fast correct diagnosis of various cardiac arrhythmias and select suitable strategy for patients’ treatment. However, P waves detection is a still challenging task, especially in long-term ECGs with manifested cardiac pathologies. Software tools used in medical practice usually fail to detect P waves under pathological conditions. Most of recently published approaches have not been tested on such the signals at all. Here we introduce a novel method for accurate and reliable P wave detection, which is success in both normal and pathological cases. Our method uses phasor transform of ECG and innovative decision rules in order to improve P waves detection in pathological signals. The rules are based on a deep knowledge of heart manifestation during various arrhythmias, such as atrial fibrillation, premature ventricular contraction, etc. By involving the rules into the decision process, we are able to find the P wave in the correct location or, alternatively, not to search for it at all. In contrast to another studies, we use three, highly variable annotated ECG databases, which contain both normal and pathological records, to objectively validate our algorithm. The results for physiological records are Se = 98.56% and PP = 99.82% for MIT-BIH Arrhythmia Database (MITDP, with MITDB P-Wave Annotations) and Se = 99.23% and PP = 99.12% for QT database. These results are comparable with other published methods. For pathological signals, the proposed method reaches Se = 96.40% and PP = 91.56% for MITDB and Se = 93.07% and PP = 88.60% for Brno University of Technology ECG Signal Database with Annotations of P wave (BUT PDB). In these signals, the proposed detector greatly outperforms other methods and, thus, represents a huge step towards effective use of fully automated ECG analysis in a real medical practice.
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spelling pubmed-90234812022-04-25 Reliable P wave detection in pathological ECG signals Saclova, Lucie Nemcova, Andrea Smisek, Radovan Smital, Lukas Vitek, Martin Ronzhina, Marina Sci Rep Article Accurate automated detection of P waves in ECG allows to provide fast correct diagnosis of various cardiac arrhythmias and select suitable strategy for patients’ treatment. However, P waves detection is a still challenging task, especially in long-term ECGs with manifested cardiac pathologies. Software tools used in medical practice usually fail to detect P waves under pathological conditions. Most of recently published approaches have not been tested on such the signals at all. Here we introduce a novel method for accurate and reliable P wave detection, which is success in both normal and pathological cases. Our method uses phasor transform of ECG and innovative decision rules in order to improve P waves detection in pathological signals. The rules are based on a deep knowledge of heart manifestation during various arrhythmias, such as atrial fibrillation, premature ventricular contraction, etc. By involving the rules into the decision process, we are able to find the P wave in the correct location or, alternatively, not to search for it at all. In contrast to another studies, we use three, highly variable annotated ECG databases, which contain both normal and pathological records, to objectively validate our algorithm. The results for physiological records are Se = 98.56% and PP = 99.82% for MIT-BIH Arrhythmia Database (MITDP, with MITDB P-Wave Annotations) and Se = 99.23% and PP = 99.12% for QT database. These results are comparable with other published methods. For pathological signals, the proposed method reaches Se = 96.40% and PP = 91.56% for MITDB and Se = 93.07% and PP = 88.60% for Brno University of Technology ECG Signal Database with Annotations of P wave (BUT PDB). In these signals, the proposed detector greatly outperforms other methods and, thus, represents a huge step towards effective use of fully automated ECG analysis in a real medical practice. Nature Publishing Group UK 2022-04-21 /pmc/articles/PMC9023481/ /pubmed/35449228 http://dx.doi.org/10.1038/s41598-022-10656-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Saclova, Lucie
Nemcova, Andrea
Smisek, Radovan
Smital, Lukas
Vitek, Martin
Ronzhina, Marina
Reliable P wave detection in pathological ECG signals
title Reliable P wave detection in pathological ECG signals
title_full Reliable P wave detection in pathological ECG signals
title_fullStr Reliable P wave detection in pathological ECG signals
title_full_unstemmed Reliable P wave detection in pathological ECG signals
title_short Reliable P wave detection in pathological ECG signals
title_sort reliable p wave detection in pathological ecg signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023481/
https://www.ncbi.nlm.nih.gov/pubmed/35449228
http://dx.doi.org/10.1038/s41598-022-10656-4
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