一个可解释的深度学习框架,用于从心电图信号中可靠地检测心律失常.
Md Alamin Talukder1, Amira Samy Talaat2, Nusrat Jahan Muna3
1Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh. alamin.cse@iubat.edu.
Scientific reports
|November 11, 2025
概括
这项研究引入了一个可解释的深度学习框架,用于从心电图信号中准确检测心律失常. 该模型实现了高精度,同时提供了可解释的见解,增强了对AI诊断的临床信任.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 心血管疾病 (CVD) 是一个主要的全球健康问题,心律失常会增加死亡率和发病率.
- 从心电图 (ECG) 信号中准确检测心律失常是至关重要的,但由于数据的复杂性而具有挑战性.
- 目前用于ECG分析的深度学习 (DL) 模型缺乏解释性,并面临采用障碍.
研究的目的:
- 开发一个可解释的深度学习 (DL) 框架,以准确可靠地检测心律失常.
- 提高EDG分析中DL模型的可解释性,以便在临床上采用.
- 通过先进的数据平衡技术,提高DL模型的概括性和性能.
主要方法:
- 卷积神经网络 (CNN) 和密集神经网络 (DNN) 架构的集成.
- 实施一个多阶段的管道,包括数据准备,信号预处理和多策略数据平衡 (ADASYN,SMOTE,SMOTETomek,随机过量采样).
- 纳入可解释的人工智能 (XAI) 方法 (SHAP,LIME,特征重要性分析) 以实现模型透明度.
主要成果:
- 随机过量采样与CNN (ROS+CNN) 模型相结合,实现了高分类准确率:99.74% (MITDB),99.43% (PTBDB) 和99.98% (NSTDB).
- 该框架在基准ECG数据集上表现出卓越的表现.
- XAI组件为模型的决策过程提供了可操作的见解.
结论:
- 开发的可解释DL框架提供了准确可靠的心律失常检测.
- 集成XAI促进了临床信任,并促进了AI在心血管诊断中的采用.
- 这种方法为心脏病学中更具影响力的AI驱动解决方案铺平了道路.
关键词:
检测心律失常的检测方式卷积神经网络 (CNN) 是一种神经网络.数据平衡 (ROS) 是指数据的平衡.深度学习 (Deep Learning) 是一种深度学习.电心电图 (ECG) 是一种心电图.可解释的人工智能 (XAI)更多相关视频
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