融入领域知识的深度学习在心律失常分类中的应用

孙洁

doi:10.1631/FITEE.2100519

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融入领域知识的深度学习在心律失常分类中的应用

常规文章 | Updated：2023-01-28

- 融入领域知识的深度学习在心律失常分类中的应用
- Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification
- 信息与电子工程前沿(英文版) 2023年24卷第1期页码：59-72
- Affiliations：
  
  School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo 315211, China
- Author bio：
  
  E-mail: sunjie@nbut.edu.cn
- Funds：
  
  the Scientific Research Fund of Zhejiang Provincial Education Department(Y201839519);the Ningbo Natural Science Foundation(2019A610087)
- DOI：10.1631/FITEE.2100519
  中图分类号： TP391.5
- 收稿：2021-11-03，
  
  修回：2022-07-21，
  
  纸质出版：2023-01
- Accepted：
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孙洁. 融入领域知识的深度学习在心律失常分类中的应用[J]. 信息与电子工程前沿(英文版), 2023,24(1):59-72.

Jie SUN. Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 59-72.
孙洁. 融入领域知识的深度学习在心律失常分类中的应用[J]. 信息与电子工程前沿(英文版), 2023,24(1):59-72. DOI： 10.1631/FITEE.2100519.

Jie SUN. Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(1): 59-72. DOI： 10.1631/FITEE.2100519.

摘要

深度学习为心律失常的自动分类提供了一种有效的方法，但在临床决策中，纯数据驱动的方法以黑盒形式运行，可能会导致不良预测结果。将领域知识与深度学习相结合是一种很有前景的解决方案。本文开发了一个灵活且可扩展的框架，用于集成领域知识与深度神经网络。该模型由深度神经网络和知识推理模块组成，深度神经网络用于捕捉输入数据的统计模式，知识模块用于确保与领域知识的一致性。这两个组成部分经过交互训练，以实现两种机制的最佳效果。实验表明，领域知识可以较好地改善神经网络的预测结果，从而提高预测精度。

Abstract

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.

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references

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