Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review
ECG (Electrocardiogram), the most commonly used tool in the diagnosis of cardiac diseases, contains a large amount of physiologic information about the electrical activity of the heart. Research on automatic diagnosis of cardiac diseases by means of computer-aided ECG diagnosis has been carried out...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10804109/ |
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| author | Haibo Sun Dan Luo Xin Niu Xiaoxi Zeng Bin Zheng Hao Liu Jingye Pan |
| author_facet | Haibo Sun Dan Luo Xin Niu Xiaoxi Zeng Bin Zheng Hao Liu Jingye Pan |
| author_sort | Haibo Sun |
| collection | DOAJ |
| description | ECG (Electrocardiogram), the most commonly used tool in the diagnosis of cardiac diseases, contains a large amount of physiologic information about the electrical activity of the heart. Research on automatic diagnosis of cardiac diseases by means of computer-aided ECG diagnosis has been carried out for decades. Computer assisted therapy is able to timely detect heart diseases and reduce the mortality rate of cardiovascular disease patients. In recent years, classification algorithms applied to automatic ECG arrhythmia diagnosis have been proposed and optimized. With the application and development of neural network-based deep learning technology in automatic ECG diagnosis, the accuracy and reliability of automatic ECG arrhythmia classification have been significantly improved. This paper systematically analyzes the literatures related to the automatic classification of arrhythmias based on ECG signals, summarizes the classification algorithms of different arrhythmias based on neural network model and non-neural network model, and summarizes the classification characteristics of the two main classification models in the application of automatic diagnosis of arrhythmias under the same AAMI standard and MIT-BIH arrhythmia database. This paper summarizes the selected research results in the field of arrhythmia classification under the same database from the perspective of data quality, evaluation paradigm and performance indicators, and discusses the correlation between the specific characteristics of arrhythmia detection and different classification algorithms. Finally, the existing problems in the research of automatic classification of arrhythmias are put forward, which provides a reference for the future development and clinical practice of automatic diagnosis of arrhythmias. |
| format | Article |
| id | doaj-art-43caf32ef9dd4bea8af45e1a056c2221 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-43caf32ef9dd4bea8af45e1a056c22212025-01-15T00:01:57ZengIEEEIEEE Access2169-35362024-01-011219192119193510.1109/ACCESS.2024.351877610804109Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A ReviewHaibo Sun0https://orcid.org/0000-0002-5029-5359Dan Luo1Xin Niu2Xiaoxi Zeng3Bin Zheng4Hao Liu5https://orcid.org/0000-0003-0089-4254Jingye Pan6School of Electronics and Information Engineering, Tiangong University, Tianjin, ChinaInstitute of Smart Wearable Electronic Textiles, Tiangong University, Tianjin, ChinaInstitute of Smart Wearable Electronic Textiles, Tiangong University, Tianjin, ChinaBiomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, ChinaWenzhou Safety (Emergency) Institute, Tianjin University, Wenzhou, Zhejiang, ChinaSchool of Electronics and Information Engineering, Tiangong University, Tianjin, ChinaThe First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, ChinaECG (Electrocardiogram), the most commonly used tool in the diagnosis of cardiac diseases, contains a large amount of physiologic information about the electrical activity of the heart. Research on automatic diagnosis of cardiac diseases by means of computer-aided ECG diagnosis has been carried out for decades. Computer assisted therapy is able to timely detect heart diseases and reduce the mortality rate of cardiovascular disease patients. In recent years, classification algorithms applied to automatic ECG arrhythmia diagnosis have been proposed and optimized. With the application and development of neural network-based deep learning technology in automatic ECG diagnosis, the accuracy and reliability of automatic ECG arrhythmia classification have been significantly improved. This paper systematically analyzes the literatures related to the automatic classification of arrhythmias based on ECG signals, summarizes the classification algorithms of different arrhythmias based on neural network model and non-neural network model, and summarizes the classification characteristics of the two main classification models in the application of automatic diagnosis of arrhythmias under the same AAMI standard and MIT-BIH arrhythmia database. This paper summarizes the selected research results in the field of arrhythmia classification under the same database from the perspective of data quality, evaluation paradigm and performance indicators, and discusses the correlation between the specific characteristics of arrhythmia detection and different classification algorithms. Finally, the existing problems in the research of automatic classification of arrhythmias are put forward, which provides a reference for the future development and clinical practice of automatic diagnosis of arrhythmias.https://ieeexplore.ieee.org/document/10804109/Arrhythmiasautomatic diagnosisECG signalneural networkssupport vector machine |
| spellingShingle | Haibo Sun Dan Luo Xin Niu Xiaoxi Zeng Bin Zheng Hao Liu Jingye Pan Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review IEEE Access Arrhythmias automatic diagnosis ECG signal neural networks support vector machine |
| title | Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review |
| title_full | Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review |
| title_fullStr | Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review |
| title_full_unstemmed | Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review |
| title_short | Classification Algorithms in Automatic Diagnosis of ECG Arrhythmias: A Review |
| title_sort | classification algorithms in automatic diagnosis of ecg arrhythmias a review |
| topic | Arrhythmias automatic diagnosis ECG signal neural networks support vector machine |
| url | https://ieeexplore.ieee.org/document/10804109/ |
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