自动检测心律不整,使用从12导电心电图中提取的新型可解释特征集
Jibin Joy Kolliyil1, Melissa C Brindise1
1Department of Mechanical Engineering, Pennsylvania State University, University Park, PA, USA.
Computers in biology and medicine
|March 16, 2025
概括
本研究引入了一种可解释的基于特征的方法,用于使用心电图 (ECG) 诊断心律失常. 该方法实现了与深度学习模型可比的性能,提高了对自动化系统的临床实用性和信任.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 自动心律失常诊断依赖于大型心电图 (ECG) 数据集和机器学习.
- 深度学习模型提供了高精度,但缺乏临床解释性,阻碍了采用.
- 诊断系统需要平衡性能与临床透明度.
研究的目的:
- 开发一种可解释的基于特征的方法来诊断心律失常.
- 实现与深度学习模型可比的性能,同时提高临床实用性.
- 为了验证诊断特征的解释性.
主要方法:
- 使用数学技术 (福里埃变换,波形变换,交叉相关) 提取654个心电图特征.
- 用极端梯度增强将心电图分为9个诊断类别进行分类.
- 使用沙普利增量解释 (SHAP) 分析对159个特征进行特征集优化.
主要成果:
- 与最先进的深度学习分类器可比较的性能 (加权F1得分为81%的交叉验证,68%的外部测试).
- 在识别心房动 (89% CV,79% 测试) 和阻塞异常 (87% CV,75% 测试) 中具有卓越的准确性.
- SHAP分析证实了与临床诊断过程一致的特征相关性.
结论:
- 提出的基于特征的方法为基于心电图的心律失常诊断提供了对深度学习的临床可解释的替代方案.
- 该方法为各种临床环境提供了更好的实施方便性和适应性.
- 这项工作弥合了高性能AI和心脏病学中必不可少的临床解释性之间的差距.
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