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ECG signal classification in wearable devices based on compressed domain
Wearable devices are often used to diagnose arrhythmia, but the electrocardiogram (ECG) monitoring process generates a large amount of data, which will affect the detection speed and accuracy. In order to solve this problem, many studies have applied deep compressed sensing (DCS) technology to ECG m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072459/ https://www.ncbi.nlm.nih.gov/pubmed/37014879 http://dx.doi.org/10.1371/journal.pone.0284008 |
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author | Hua, Jing Chu, Binbin Zou, Jiawen Jia, Jing |
author_facet | Hua, Jing Chu, Binbin Zou, Jiawen Jia, Jing |
author_sort | Hua, Jing |
collection | PubMed |
description | Wearable devices are often used to diagnose arrhythmia, but the electrocardiogram (ECG) monitoring process generates a large amount of data, which will affect the detection speed and accuracy. In order to solve this problem, many studies have applied deep compressed sensing (DCS) technology to ECG monitoring, which can under-sampling and reconstruct ECG signals, greatly optimizing the diagnosis process, but the reconstruction process is complex and expensive. In this paper, we propose an improved classification scheme for deep compressed sensing models. The framework is comprised of four modules: pre-processing; compression; and classification. Firstly, the normalized ECG signals are compressed adaptively in the three convolutional layers, and then the compressed data is directly put into the classification network to obtain the results of four kinds of ECG signals. We conducted our experiments on the MIT-BIH Arrhythmia Database and Ali Cloud Tianchi ECG signal Database to validate the robustness of our model, adopting Accuracy, Precision, Sensitivity and F1-score as the evaluation metrics. When the compression ratio (CR) is 0.2, our model has 98.16% accuracy, 98.28% average accuracy, 98.09% Sensitivity and 98.06% F1-score, all of which are better than other models. |
format | Online Article Text |
id | pubmed-10072459 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-100724592023-04-05 ECG signal classification in wearable devices based on compressed domain Hua, Jing Chu, Binbin Zou, Jiawen Jia, Jing PLoS One Research Article Wearable devices are often used to diagnose arrhythmia, but the electrocardiogram (ECG) monitoring process generates a large amount of data, which will affect the detection speed and accuracy. In order to solve this problem, many studies have applied deep compressed sensing (DCS) technology to ECG monitoring, which can under-sampling and reconstruct ECG signals, greatly optimizing the diagnosis process, but the reconstruction process is complex and expensive. In this paper, we propose an improved classification scheme for deep compressed sensing models. The framework is comprised of four modules: pre-processing; compression; and classification. Firstly, the normalized ECG signals are compressed adaptively in the three convolutional layers, and then the compressed data is directly put into the classification network to obtain the results of four kinds of ECG signals. We conducted our experiments on the MIT-BIH Arrhythmia Database and Ali Cloud Tianchi ECG signal Database to validate the robustness of our model, adopting Accuracy, Precision, Sensitivity and F1-score as the evaluation metrics. When the compression ratio (CR) is 0.2, our model has 98.16% accuracy, 98.28% average accuracy, 98.09% Sensitivity and 98.06% F1-score, all of which are better than other models. Public Library of Science 2023-04-04 /pmc/articles/PMC10072459/ /pubmed/37014879 http://dx.doi.org/10.1371/journal.pone.0284008 Text en © 2023 Hua et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hua, Jing Chu, Binbin Zou, Jiawen Jia, Jing ECG signal classification in wearable devices based on compressed domain |
title | ECG signal classification in wearable devices based on compressed domain |
title_full | ECG signal classification in wearable devices based on compressed domain |
title_fullStr | ECG signal classification in wearable devices based on compressed domain |
title_full_unstemmed | ECG signal classification in wearable devices based on compressed domain |
title_short | ECG signal classification in wearable devices based on compressed domain |
title_sort | ecg signal classification in wearable devices based on compressed domain |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072459/ https://www.ncbi.nlm.nih.gov/pubmed/37014879 http://dx.doi.org/10.1371/journal.pone.0284008 |
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