基于心电图的心脏病分类的可解释的基于深度学习的技术:一个系统的文献审查和未来的方向
Gouthamaan Manimaran1, Abdolrahman Peimankar2, Sadasivan Puthusserypady1
1Department of Health Technology, Technical University of Denmark, Copenhagen, 2800, Denmark.
Computers in biology and medicine
|November 20, 2025
概括
本研究回顾了深度学习 (DL) 中的可解释AI (XAI),用于使用心电图 (ECG) 进行心脏病 (HD) 分类. 它发现了许多XAI-DL模型,但强调了其局限性和未来的研究需要更好的可解释性.
科学领域:
- 人工智能的人工智能
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
背景情况:
- 深度学习 (DL) 模型越来越多地用于从心电图 (ECG) 数据对心脏病 (HD) 的分类.
- 这些DL模型中的可解释性对于临床信任和理解诊断推理至关重要.
- 需要进行系统审查,以了解当前应用到基于ECG的疾病分类中可解释AI (XAI) 的现状.
研究的目的:
- 系统地审查和分析基于XAI的DL架构中的方法选择,用于基于ECG的HD分类.
- 评估这些选择对模型解释性的影响,并确定当前的挑战.
- 提出未来的研究方向,以提高该领域的可解释性.
主要方法:
- 按照基奇纳姆和宪章的指导方针进行了系统文献审查 (SLR).
- 分析了2018年1月至2024年9月期间发表的关于基于XAI的DL用于基于ECG的HD分类的学术文章.
- 对数据集,预处理方法和XAI技术的洞察力进行了合成.
主要成果:
- 在6448项已识别的研究中,51项采用基于XAI的DL架构进行基于ECG的HD分类.
- 该审查确定了25个不同的数据集,16个DL架构和8种新的XAI技术.
- 传统的XAI方法如SHAP, Saliency Maps,Grad-Cam和LIME是最常用的.
结论:
- 许多基于XAI的DL架构存在于基于ECG的HD分类中,但仍然存在显著的限制.
- 确定的挑战包括数据标准化,不一致的解释性,时间依赖可视化和缺乏XAI基准测试.
- 未来的研究应该专注于解决这些局限性,以提高XAI在心电图分析中的可靠性和临床实用性.
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