基于临床知识的ECG异常检测,使用双视图CNN转换器和外部注意力机制
Hui Li1, Jiyang Han1, Honghao Zhang2
1School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China; Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology, Xi'an, Shaanxi 710072, China.
Computers in biology and medicine
|June 27, 2024
概括
这项研究引入了一种新的深度学习模型,用于自动电心图 (ECG) 异常检测,实现心脏病学家水平的性能. 新模型为心血管疾病诊断提供了更好的解释性和更少的复杂性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 自动心电图 (ECG) 分析对于早期发现和诊断心血管疾病至关重要.
- 深度神经网络被广泛使用,但往往受到糟糕的解释性和高复杂性的困扰.
- 现有的模型在检测心电图异常方面难以达到心脏病学家水平的性能.
研究的目的:
- 开发一种基于临床知识的ECG异常检测模型.
- 在心电图分析中解决传统深度神经网络的局限性.
- 为了提高可解释性和减少自动ECG异常检测的复杂性.
主要方法:
- 提出了一种双视 CNN-Transformer 模型,其中包含了一个外部注意力机制.
- 模仿临床医生诊断,考虑详细的波形和全球记录变化.
- 整合外部关注机制,专注于信息化的ECG区域.
主要成果:
- 在9个类数据集上获得了0.854±0.01的平均F1得分.
- 与最先进的模型相比,表现出卓越的性能.
- 展示了增强的解释性和减少的计算复杂性.
结论:
- 拟议的模型为自动ECG异常检测提供了可信和有效的解决方案.
- 双视图CNN-变压器与外部注意力显示出对临床应用的希望.
- 这种方法在心血管医学中推进了计算机辅助诊断.
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