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CLC number: TP391.5

On-line Access: 2023-01-21

Received: 2021-11-03

Revision Accepted: 2022-07-21

Crosschecked: 2023-01-21

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.1 P.59-72


Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification

Author(s):  Jie SUN

Affiliation(s):  School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China

Corresponding email(s):   sunjie@nbut.edu.cn

Key Words:  Domain knowledge, Cardiac arrhythmia, Electrocardiogram (ECG), Clinical decision-making

Jie SUN. Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 59-72.

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A1 - Jie SUN
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2100519

Deep learning provides an effective way for automatic classification of cardiac arrhythmias, but in clinical decision-making, pure data-driven methods working as black-boxes may lead to unsatisfactory results. A promising solution is combining domain knowledge with deep learning. This paper develops a flexible and extensible framework for integrating domain knowledge with a deep neural network. The model consists of a deep neural network to capture the statistical pattern between input data and the ground-truth label, and a knowledge module to guarantee consistency with the domain knowledge. These two components are trained interactively to bring the best of both worlds. The experiments show that the domain knowledge is valuable in refining the neural network prediction and thus improves accuracy.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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