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An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals
This study introduces a method to classify single-lead ECG signals by extracting features through traditional methods and deep neural network methods. At first step, the statistical type features of the ECG signals are exacted by traditional methods, including time domain features, frequency domain...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789462/ https://www.ncbi.nlm.nih.gov/pubmed/35087647 http://dx.doi.org/10.1155/2022/2205460 |
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author | Zhao, Tianxia Wang, Xin'an Qiu, Changpei |
author_facet | Zhao, Tianxia Wang, Xin'an Qiu, Changpei |
author_sort | Zhao, Tianxia |
collection | PubMed |
description | This study introduces a method to classify single-lead ECG signals by extracting features through traditional methods and deep neural network methods. At first step, the statistical type features of the ECG signals are exacted by traditional methods, including time domain features, frequency domain features, and medical domain features. And then, deep neural networks are used to extract the deeper features of the ECG signal. The database of ECG signals is from Cinc 17, which have 8528 samples of short-time ECG signal. The huge amount of data makes the classification and identification more accurate by atrial fibrillation, normal sinus rhythm, noise, and indiscernible. Compare the base model built by the classified data and the data collected by the ECG device of CareON to enable daily early screening and a remote alert function with WeChat app. This method can extend the prevention, detection, and diagnosis of heart disease to the family, company, and other out-of-hospital scenarios, thus enabling faster treatment of heart patients and saving medical resources. |
format | Online Article Text |
id | pubmed-8789462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-87894622022-01-26 An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals Zhao, Tianxia Wang, Xin'an Qiu, Changpei J Healthc Eng Research Article This study introduces a method to classify single-lead ECG signals by extracting features through traditional methods and deep neural network methods. At first step, the statistical type features of the ECG signals are exacted by traditional methods, including time domain features, frequency domain features, and medical domain features. And then, deep neural networks are used to extract the deeper features of the ECG signal. The database of ECG signals is from Cinc 17, which have 8528 samples of short-time ECG signal. The huge amount of data makes the classification and identification more accurate by atrial fibrillation, normal sinus rhythm, noise, and indiscernible. Compare the base model built by the classified data and the data collected by the ECG device of CareON to enable daily early screening and a remote alert function with WeChat app. This method can extend the prevention, detection, and diagnosis of heart disease to the family, company, and other out-of-hospital scenarios, thus enabling faster treatment of heart patients and saving medical resources. Hindawi 2022-01-18 /pmc/articles/PMC8789462/ /pubmed/35087647 http://dx.doi.org/10.1155/2022/2205460 Text en Copyright © 2022 Tianxia Zhao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhao, Tianxia Wang, Xin'an Qiu, Changpei An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals |
title | An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals |
title_full | An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals |
title_fullStr | An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals |
title_full_unstemmed | An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals |
title_short | An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals |
title_sort | early warning of atrial fibrillation based on short-time ecg signals |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789462/ https://www.ncbi.nlm.nih.gov/pubmed/35087647 http://dx.doi.org/10.1155/2022/2205460 |
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