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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/12/11/1282 |
| _version_ | 1827676049706582016 |
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| author | Lvheng Zhang Jihong Liu |
| author_facet | Lvheng Zhang Jihong Liu |
| author_sort | Lvheng Zhang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:17:15Z |
| format | Article |
| id | doaj.art-eb67aee4a5c84fcda98018bb3d02124d |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T05:17:15Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-eb67aee4a5c84fcda98018bb3d02124d2023-11-23T00:25:10ZengMDPI AGMicromachines2072-666X2021-10-011211128210.3390/mi12111282Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer MaterialsLvheng Zhang0Jihong Liu1College of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaHeart 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.https://www.mdpi.com/2072-666X/12/11/1282arrhythmiaspolymer materialsdeep learningelectrocardiogramgenerative adversarial networksmyocardial ischemia |
| spellingShingle | Lvheng Zhang Jihong Liu Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials Micromachines arrhythmias polymer materials deep learning electrocardiogram generative adversarial networks myocardial ischemia |
| title | 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_short | 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 |
| topic | arrhythmias polymer materials deep learning electrocardiogram generative adversarial networks myocardial ischemia |
| url | https://www.mdpi.com/2072-666X/12/11/1282 |
| work_keys_str_mv | AT lvhengzhang researchprogressofecgmonitoringequipmentandalgorithmsbasedonpolymermaterials AT jihongliu researchprogressofecgmonitoringequipmentandalgorithmsbasedonpolymermaterials |