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
Hlavní autoři: Liu, Xinwen, Wang, Huan, Li, Zongjin, Qin, Lang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 05.09.2021
Elsevier Science Ltd
Témata:
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
Deep learning
Stacked auto-encoders
Recurrent neural network
Deep belief network
Electrocardiogram
Convolutional neural network
ISSN:0950-7051, 1872-7409
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Popis
Shrnutí: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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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.107187