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<p>As cardiac arrhythmia remains one of the leading causes of death worldwide, early and accurate diagnosis of cardiac arrhythmia is critical to improving patient outcomes. Electrocardiogram (ECG) analysis plays a critical role in the diagnosis of these diseases, and recent advances in deep learning have led to significant advances in automated ECG interpretation. However, the “black box” nature of these models limits clinical confidence and highlights the need for explainable artificial intelligence methods. This study presents an innovative MM-GradCAM method that combines two different data formats, providing explainability for both 1D ECG signal and 2D ECG image data. Using a dataset of more than 10,000 patients, a 17-layer CNN model capable of four-class arrhythmia detection was developed and separate explainability outputs were obtained for each data form. The resulting explainability maps were evaluated by a cardiologist and the interpretability and clinical significance of the model were verified. The signal form achieved 93.07% accuracy, while the image form achieved 97.44% accuracy. As a pioneering approach for explainability in medical diagnosis, MM-GradCAM has the potential to increase reliability and transparency in medical AI applications.</p>

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MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Fatma Murat Duranay,
  • Ender Murat,
  • Özal Yıldırım,
  • Yakup Demir,
  • Ru-San Tan,
  • Niranjana Sampathila,
  • U. Rajendra Acharya

摘要

As cardiac arrhythmia remains one of the leading causes of death worldwide, early and accurate diagnosis of cardiac arrhythmia is critical to improving patient outcomes. Electrocardiogram (ECG) analysis plays a critical role in the diagnosis of these diseases, and recent advances in deep learning have led to significant advances in automated ECG interpretation. However, the “black box” nature of these models limits clinical confidence and highlights the need for explainable artificial intelligence methods. This study presents an innovative MM-GradCAM method that combines two different data formats, providing explainability for both 1D ECG signal and 2D ECG image data. Using a dataset of more than 10,000 patients, a 17-layer CNN model capable of four-class arrhythmia detection was developed and separate explainability outputs were obtained for each data form. The resulting explainability maps were evaluated by a cardiologist and the interpretability and clinical significance of the model were verified. The signal form achieved 93.07% accuracy, while the image form achieved 97.44% accuracy. As a pioneering approach for explainability in medical diagnosis, MM-GradCAM has the potential to increase reliability and transparency in medical AI applications.