HCTG-Net:一种混合CNN-变压器网络,用于自动ECG心律失常诊断
Ni Xiong1,2,3, Zibo Wei4,5, Xuehua Wang1,2,3
1Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
本研究介绍了HCTG-Net,这是一种用于准确电心图 (ECG) 分析的新型深度学习模型. HCTG-Net有效检测心律不整,通过先进的特征提取改进心血管疾病诊断.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 从心电图 (ECG) 信号中准确检测心律失常对于早期心血管疾病诊断至关重要.
- 电脑心电图波形的复杂,非线性性质对传统的分析方法提出了重大挑战.
- 现有的深度学习模型难以有效地捕捉局部波形形态和远程时间依赖.
研究的目的:
- 提出HCTG-Net,一个带有门融合的混合CNN转换器网络,用于从心电图信号中增强自动心律失常检测.
- 开发一个模型,共同捕捉ECG数据中的局部形态特征和远程时间依赖.
- 通过使用标准心律失常数据库,对HCTG-Net与现有方法的性能进行评估.
主要方法:
- 双分支架构结合了局部特征提取的残余卷积神经网络 (CNN) 和用于全球时间上下文建模的变压器.
- 实现可学习的封闭融合机制,以在每个维度层面上适应性集成来自CNN和变压器分支的特征.
- 使用MIT-BIH心律失常数据库进行实验验证.
主要成果:
- 与现有方法相比,HCTG-Net实现了优越的性能,总精度为0.9946和F1得分为0.9711.
- 可视化结果显示了集群的特征分布,这表明模型具有强大的特征学习能力.
- 废弃研究证实了CNN,变压器和封闭核聚变模块的协同作用和互补作用.
结论:
- HCTG-Net为基于心电图的自动心律失常诊断提供了一个强大而适应性的框架.
- 该模型能够整合本地和全球特征,为实时临床应用和可穿戴医疗设备提供了重大潜力.
- 这种方法通过复杂的信号处理推动了自动心血管健康监测领域的发展.
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