Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG...

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Autores principales:	Lvheng Zhang, Jihong Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	arrhythmias polymer materials deep learning electrocardiogram generative adversarial networks myocardial ischemia Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/eb67aee4a5c84fcda98018bb3d02124d
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id	oai:doaj.org-article:eb67aee4a5c84fcda98018bb3d02124d
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spelling	oai:doaj.org-article:eb67aee4a5c84fcda98018bb3d02124d2021-11-25T18:22:45ZResearch Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials10.3390/mi121112822072-666Xhttps://doaj.org/article/eb67aee4a5c84fcda98018bb3d02124d2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-666X/12/11/1282https://doaj.org/toc/2072-666XHeart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.Lvheng ZhangJihong LiuMDPI AGarticlearrhythmiaspolymer materialsdeep learningelectrocardiogramgenerative adversarial networksmyocardial ischemiaMechanical engineering and machineryTJ1-1570ENMicromachines, Vol 12, Iss 1282, p 1282 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	arrhythmias polymer materials deep learning electrocardiogram generative adversarial networks myocardial ischemia Mechanical engineering and machinery TJ1-1570
spellingShingle	arrhythmias polymer materials deep learning electrocardiogram generative adversarial networks myocardial ischemia Mechanical engineering and machinery TJ1-1570 Lvheng Zhang Jihong Liu Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials
description	Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.
format	article
author	Lvheng Zhang Jihong Liu
author_facet	Lvheng Zhang Jihong Liu
author_sort	Lvheng Zhang
title	Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials
title_short	Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials
title_full	Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials
title_fullStr	Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials
title_full_unstemmed	Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials
title_sort	research progress of ecg monitoring equipment and algorithms based on polymer materials
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/eb67aee4a5c84fcda98018bb3d02124d
work_keys_str_mv	AT lvhengzhang researchprogressofecgmonitoringequipmentandalgorithmsbasedonpolymermaterials AT jihongliu researchprogressofecgmonitoringequipmentandalgorithmsbasedonpolymermaterials
_version_	1718411292451012608

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

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