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Multi-scale Feature Learning with CNN-RNN-Attention Framework for ECG-based Cancer Therapy-Related Cardiac

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
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    A new deep learning model using ECG signals can detect cancer therapy-related cardiac dysfunction (CTRCD). This cost-effective approach offers a reliable alternative to echocardiography for monitoring heart health during cancer treatment.

    Area of Science:

    • Cardiology
    • Oncology
    • Artificial Intelligence

    Background:

    • Cancer therapy-related cardiac dysfunction (CTRCD) is a serious side effect of anticancer drugs.
    • Echocardiography, the standard diagnostic tool, is operator-dependent, time-consuming, and expensive.
    • Electrocardiogram (ECG) offers a more accessible and cost-effective alternative for cardiac monitoring.

    Purpose of the Study:

    • To develop a deep learning model for detecting CTRCD using ECG signals.
    • To create a reliable and cost-effective method for monitoring cardiac function during cancer therapy.
    • To improve the interpretability of deep learning models in cardiac diagnostics.

    Main Methods:

    • A hybrid deep learning model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) was developed.

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  • An attention mechanism was incorporated to weigh the importance of different ECG features.
  • Attention weights were visualized to enhance model interpretability and identify key diagnostic features.

    • Main Results:

    • The proposed deep learning model effectively detects CTRCD from 12-lead ECG data.
    • Ablation studies confirmed the effectiveness of the integrated CNN-RNN architecture and attention mechanism.
    • Visualization of attention weights identified significant ECG features contributing to CTRCD classification.

    Conclusions:

    • The developed deep learning model shows promise as a cost-effective and reliable tool for CTRCD detection.
    • This approach can aid in the early identification of cardiac side effects in cancer patients.
    • The findings support the use of ECG-based AI for routine cardiac monitoring in oncology.