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<p>The 12-lead electrocardiogram (ECG) plays a crucial role in the initial diagnosis of cardiac conditions. However, reports on ECG monitoring utilizing neuromorphic hardware indicate that monitoring multi-lead ECG signals and generating conclusive assessment results necessitate multiple array circuits and two operational processes, which present challenges regarding device consistency and accuracy. In this study, we propose a neuromorphic parallel computing hardware architecture based on quantum dot synaptic transistors. Leveraging the trap effect and surface electric field effect inherent to quantum dots, our approach enables 12-lead ECG monitoring within a single array circuit, eliminating the need for twelve separate circuits. This system can concurrently process multiple ECG signals and produce final result outputs without reliance on external computing or control circuits. Furthermore, the training accuracy achieved for classifying various ECG signals exceeds 98%.</p>

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Neuromorphic parallel computing hardware based on quantum dots for 12-lead electrocardiogram monitoring

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Hao Chen,
  • Xianghong Zhang,
  • Enping Cheng,
  • Jianxin Wu,
  • Jingwen Huang,
  • Weilong Huang,
  • Yuke Xu,
  • Xiaolong Li,
  • Jing Zhuang,
  • Rongen Guo,
  • Huipeng Chen,
  • Rui Wang,
  • Zeyan Liang

摘要

The 12-lead electrocardiogram (ECG) plays a crucial role in the initial diagnosis of cardiac conditions. However, reports on ECG monitoring utilizing neuromorphic hardware indicate that monitoring multi-lead ECG signals and generating conclusive assessment results necessitate multiple array circuits and two operational processes, which present challenges regarding device consistency and accuracy. In this study, we propose a neuromorphic parallel computing hardware architecture based on quantum dot synaptic transistors. Leveraging the trap effect and surface electric field effect inherent to quantum dots, our approach enables 12-lead ECG monitoring within a single array circuit, eliminating the need for twelve separate circuits. This system can concurrently process multiple ECG signals and produce final result outputs without reliance on external computing or control circuits. Furthermore, the training accuracy achieved for classifying various ECG signals exceeds 98%.