Deep learning in ECG diagnosis: A review
Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels abnormality that serves as a global leading reason for death. The earlier the abnormal heart rhythm is discovered, the less severe the sequela and the faster the recovery. Electrocardiogram (ECG), as a main way to...
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| Vydáno v: | Knowledge-based systems Ročník 227; s. 107187 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
05.09.2021
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels abnormality that serves as a global leading reason for death. The earlier the abnormal heart rhythm is discovered, the less severe the sequela and the faster the recovery. Electrocardiogram (ECG), as a main way to detect the electrical activity of heart, is a very important harmless means of predicting and diagnosing CVDs. However, ECG signal has characteristics of complex and high chaos, making it time-consuming and exhausting to interpret ECG signal even for experts. Hence, computer-aided methods are required to relief human burden and reduce errors caused by tiredness, inter- and intra-difference. Deep learning shows outstanding performance on ECG classification studies recent few years. Its hierarchical architecture enables higher-level features obtained and its strong ability to feature extraction contributes to classification project. Latest studies can achieve higher accuracy and efficiency than manual classification by experts. In this paper, we review the existing studies of deep learning applied in ECG diagnosis according to four typical algorithms: stacked auto-encoders, deep belief network, convolutional neural network and recurrent neural network. We first introduced the mechanism, development and application of the algorithms. Then we review their applications in ECG diagnosis systematically, discussing their highlights and limitations. Our view about future potential development of deep learning in ECG diagnosis is stated in the final part of this paper. |
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| AbstractList | Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels abnormality that serves as a global leading reason for death. The earlier the abnormal heart rhythm is discovered, the less severe the sequela and the faster the recovery. Electrocardiogram (ECG), as a main way to detect the electrical activity of heart, is a very important harmless means of predicting and diagnosing CVDs. However, ECG signal has characteristics of complex and high chaos, making it time-consuming and exhausting to interpret ECG signal even for experts. Hence, computer-aided methods are required to relief human burden and reduce errors caused by tiredness, inter- and intra-difference. Deep learning shows outstanding performance on ECG classification studies recent few years. Its hierarchical architecture enables higher-level features obtained and its strong ability to feature extraction contributes to classification project. Latest studies can achieve higher accuracy and efficiency than manual classification by experts. In this paper, we review the existing studies of deep learning applied in ECG diagnosis according to four typical algorithms: stacked auto-encoders, deep belief network, convolutional neural network and recurrent neural network. We first introduced the mechanism, development and application of the algorithms. Then we review their applications in ECG diagnosis systematically, discussing their highlights and limitations. Our view about future potential development of deep learning in ECG diagnosis is stated in the final part of this paper. |
| ArticleNumber | 107187 |
| Author | Wang, Huan Liu, Xinwen Qin, Lang Li, Zongjin |
| Author_xml | – sequence: 1 givenname: Xinwen orcidid: 0000-0001-5830-6027 surname: Liu fullname: Liu, Xinwen email: xinwenliu.carrie@qq.com organization: Glasgow College, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Huan orcidid: 0000-0002-1403-5314 surname: Wang fullname: Wang, Huan email: wh.huanwang@gmail.com organization: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 3 givenname: Zongjin orcidid: 0000-0002-5132-4721 surname: Li fullname: Li, Zongjin email: lizongjin.alan@qq.com organization: Glasgow College, University of Electronic Science and Technology of China, Chengdu, China – sequence: 4 givenname: Lang surname: Qin fullname: Qin, Lang email: qinlang51@126.com organization: Glasgow College, University of Electronic Science and Technology of China, Chengdu, China |
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| SubjectTerms | Algorithms Artificial neural networks Belief networks Blood vessels Cardiovascular diseases Classification Coders Convolutional neural network Deep belief network Deep learning Diagnosis Electrocardiogram Electrocardiography Errors Experts Extraction Feature extraction Heart diseases Learning Machine learning Medical diagnosis Networks Neural networks Recurrent Recurrent neural network Recurrent neural networks Rhythm Stacked auto-encoders Ultrasonic imaging |
| Title | Deep learning in ECG diagnosis: A review |
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