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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9023481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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