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多尺度特征学习与CNN-RNN-注意力框架基于心电图的癌症治疗相关心脏功能障碍检测.

Natsu Suyama, Akira Furui, Takio Kurita

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括
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    使用心电图信号的新深度学习模型可以检测与癌症治疗相关的心脏功能障碍 (CTRCD). 这种具有成本效益的方法提供了可靠的心声回声学替代方案,用于在癌症治疗期间监测心脏健康.

    科学领域:

    • 心脏病学 心脏病学
    • 在瘤学瘤学.
    • 人工智能的人工智能

    背景情况:

    • 与癌症治疗相关的心脏功能障碍 (CTRCD) 是抗癌药物的严重副作用.
    • 标准诊断工具心声回声仪 (Echocardiography) 是需要操作人员的,耗时且昂贵.
    • 电心电图 (ECG) 为心脏监测提供了一个更容易获得和更具成本效益的替代方案.

    研究的目的:

    • 开发一种深度学习模型,用于使用心电图信号检测CTRCD.
    • 创建一种可靠且具有成本效益的方法来监测癌症治疗期间的心脏功能.
    • 提高心脏诊断中的深度学习模型的可解释性.

    主要方法:

    • 开发了一个混合深度学习模型,集成卷积神经网络 (CNN) 和循环神经网络 (RNN).
    • 整合了注意力机制,以权衡不同ECG特征的重要性.
    • 注意力权重被可视化,以提高模型的解释性,并确定关键诊断特征.

    主要成果:

    • 拟议的深度学习模型有效地从12导电图数据中检测到CTRCD.
    • 废除研究证实了综合CNN-RNN架构和注意力机制的有效性.
    • 对注意力权重的可视化确定了重要的ECG特征,有助于CTRCD分类.

    结论:

    • 开发的深度学习模型显示,它是一个具有成本效益和可靠的CTRCD检测工具.
    • 这种方法可以帮助癌症患者早期识别心脏副作用.
    • 这些发现支持基于心电图的AI用于瘤学的常规心脏监测.