Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hua, Jing, Chu, Binbin, Zou, Jiawen, Jia, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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
_version_ 1785019385811828736
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
work_keys_str_mv AT huajing ecgsignalclassificationinwearabledevicesbasedoncompresseddomain
AT chubinbin ecgsignalclassificationinwearabledevicesbasedoncompresseddomain
AT zoujiawen ecgsignalclassificationinwearabledevicesbasedoncompresseddomain
AT jiajing ecgsignalclassificationinwearabledevicesbasedoncompresseddomain