Frontiers of Information Technology & Electronic Engineering
>> 2023,
Volume 24,
Issue 1
doi:
10.1631/FITEE.2100519
Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification
宁波工程学院网络空间安全学院,中国宁波市,315211
Received: 2021-11-03
Accepted: 2023-01-21
Available online: 2023-01-21
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Abstract
Deep learning provides an effective way for automatic classification of s, but in , pure data-driven methods working as black-boxes may lead to unsatisfactory results. A promising solution is combining with deep learning. This paper develops a flexible and extensible framework for integrating 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 . These two components are trained interactively to bring the best of both worlds. The experiments show that the is valuable in refining the neural network prediction and thus improves accuracy.