在使用深度学习组合的心电图扫描中检测可解释性心律失常:一种遗传编程方法
Arkadiusz Czerwinski1, Damian Kucharski1, Agata M Wijata2
1Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
NPJ digital medicine
|November 6, 2025
概括
深度学习组合准确地检测心律不整,并从心电图中预测心房的复发. 可解释的人工智能增强了这些心血管疾病检测模型的临床解释性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 心血管疾病是全球主要的死亡原因.
- 准确的心律失常检测和心房 (AF) 复发预测对于患者的治疗结果至关重要.
- 目前的诊断方法在速度和准确性上可能受到限制.
研究的目的:
- 开发和验证使用心电图 (ECG) 数据进行心律失常检测和AF复发预测的深度学习组合.
- 通过可解释的人工智能 (XAI) 增强模型的解释性.
- 将集体模型与个人和投票模型的性能进行比较.
主要方法:
- 使用深度学习组合模型,对来自两个大型患者队列 (数据集G和数据集L) 的心电图数据进行训练.
- 采用可解释的人工智能 (XAI) 技术,为模型决策提供洞察力.
- 使用包括接收器运行特征曲线下的面积 (ROC-AUC) 和精度召回 AUC 在内的指标验证模型性能.
主要成果:
- 与单个模型相比,深度学习组合在心律失常检测方面表现优越.
- 实现了高的ROC-AUC值:数据集G的0.980和数据集L的0.799.
- 集体模型显示,AF复发预测的精度回忆AUC有所改善 (0.765) 与单个模型 (0.737) 相比.
结论:
- 深度学习组合提供了一个强大的工具,用于检测心律失常和AF复发预测ECGs.
- XAI集成提高了人工智能驱动的心血管诊断的临床适用性和可靠性.
- 这些发现支持人工智能的潜力,以提高心血管疾病的管理.
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