基于CNN-CBAM-GRU模型的心血管疾病智能诊断方法
Zheng Gong1,2,3,4, Yufeng Chen1,2,3,4, Shirong Lin1,2,3,4
1Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, Fujian, China.
PloS one
|September 2, 2025
概括
这项研究引入了一种新的CNN-CBAM-GRU模型,用于精确的心电图 (ECG) 分类,改善心血管疾病 (CVD) 诊断. 该模型在公共数据集上表现出高性能,为自动化健康监测提供了有效的解决方案.
科学领域:
- 生物医学工程
- 医学的人工智能
- 心脏病学
背景情况:
- 心血管疾病的早期诊断对于患者的结果至关重要.
- 由于信号的复杂性,心电图 (ECG) 信号的分类具有挑战性.
- 自动心血管疾病诊断需要强大而高效的心电图分析方法.
研究的目的:
- 开发和评估一种用于增强心电图信号分类的新型深度学习模型.
- 使用心电图数据提高自动心血管疾病诊断的准确性和效率.
- 提供关于公共心电图数据集的拟议模型的全面性能评估.
主要方法:
- 提出了一种混合深度学习模型,集结了卷积神经网络 (CNN),卷积块注意力模块 (CBAM) 和门式循环单元 (GRU).
- 在两个基准心电图数据集上评估了CNN-CBAM-GRU模型:MIT-BIH和PTB-XL.
- 进行了全面的性能评估,使用准确性,精度,回忆,灵敏度和F1评分进行五类分类.
主要成果:
- 拟议的CNN-CBAM-GRU模型在两个数据集上都实现了高分类性能.
- 在MIT-BIH数据集上实现了98.17%的准确性和98.91%的F1得分.
- 在PTB-XL数据集上获得99.21%的准确性和99.47%的F1分数,参数为245万.
结论:
- CNN-CBAM-GRU模型为智能心电图分类提供了实用且强大的解决方案.
- 该模型显示了预测性能和计算效率之间的强烈平衡.
- 这种方法通过先进的心电图分析来促进心血管疾病的自动诊断.
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