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基于多实例学习和领域知识的多分支心肌梗塞检测和定位框架.

Xinyue Li1, Yangcheng Huang1, Yixin Ning1

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.

Physiological measurement
|April 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了使用心电图 (ECG) 检测和定位心肌梗塞 (MI) 的两个模型. 提出的方法显示高精度,为早期心血管疾病诊断提供了有前途的工具.

关键词:
这是一个ECGECGECGECGECG.注意力机制注意力机制域名知识域名知识域名知识多个实例的学习学习.心肌梗塞的心脏病发作

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科学领域:

  • 心脏病学 心脏病学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 心肌梗塞 (MI) 造成不可逆转的心脏损伤,需要早期检测.
  • 通过心电图 (ECG) 来自动检测和定位心脏病发作是具有挑战性的临床问题.

研究的目的:

  • 开发和评估新的深度学习模型,以准确检测和定位MI.
  • 通过使用心电图数据来提高MI诊断的精度.

主要方法:

  • 提出了两个模型:MFB-SENET用于MI检测和MFB-DMIL用于MI定位.
  • MFB-DMIL集成了多实例学习与域名知识,手工制作的功能,以及一个新的"损失"功能.
  • 使用Grad-CAM可视化模型决策过程.

主要成果:

  • 在PTB数据库中,MI检测准确率达到93.88%,定位准确率达到67.17%.
  • 在PTB-XL数据库中,MI检测精度为94.89%,局部精度为85.83%.
  • 性能与现有的最先进的算法相当或超过.

结论:

  • 结合深度学习和领域知识的方法是有效和可靠的MI诊断.
  • 提出的模型显示了作为有效工具的潜力,以帮助医生诊断心脏病发作.
  • 这项工作推进了用于心血管疾病检测的自动化ECG分析.