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The chapter addresses the issue of detecting short-term ECG changes using the Siamese neural network. The architecture of neural network contains KNN classifier, embedding calculation module, contrastive loss error function, and complex data processing pipeline. Applying XAI methods using GRADCAM library ensures that the prediction results correspond to the clinical and diagnostic experience of cardiologists. Experimental studies have shown that the constructed model meets the requirements put forward by cardiologists and complies with requirements for identifying short-term ECG changes in life-threatening cases.

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Siamese Neural Networks for Detecting Short-Term ECG Changes in Life-Threatening Cases

  • 会议论文

  • 发表日期：

    2025年

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  • Alexandra Vatian,
  • Natalia Gusarova,
  • Maxim Ulizko,
  • Pavel Sukachev,
  • Alexander Golubev

摘要

The chapter addresses the issue of detecting short-term ECG changes using the Siamese neural network. The architecture of neural network contains KNN classifier, embedding calculation module, contrastive loss error function, and complex data processing pipeline. Applying XAI methods using GRADCAM library ensures that the prediction results correspond to the clinical and diagnostic experience of cardiologists. Experimental studies have shown that the constructed model meets the requirements put forward by cardiologists and complies with requirements for identifying short-term ECG changes in life-threatening cases.