SEResUTer:一种深度学习方法,用于准确地划分心电图信号和检测心房动
Xinyue Li1, Wenjie Cai1, Bolin Xu1
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
Physiological measurement
|October 12, 2023
概括
本研究介绍了SeresUTer,这是一种用于精确电心图 (ECG) 波形划分和心房 (AF) 检测的深度学习模型,在多个数据集上实现了高性能.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 精确的心电图 (ECG) 波形划分对于诊断心脏病状况至关重要.
- 现有的计算机辅助诊断 (CAD) 系统需要强大的心电图分析.
- 前庭动 (AF) 的检测依赖于对心电图信号的精确解释.
研究的目的:
- 推出SEResUTer,这是一个用于ECG划分和AF检测的增强深度学习模型.
- 提高自动化心电图分析的准确性和通用性.
- 在模型培训期间处理不完整的专家注释的挑战.
主要方法:
- 开发基于U-Net架构的SEResUTer,集成ResNet模块和变压器编码器.
- 实施了一个新的掩盖策略,用于不完整的专家注释.
- 在各种ECG数据集上训练和评估模型,包括QT数据库 (QTDB),洛巴切夫斯基大学心电图数据库 (LUDB),CPSC2021和CPSC2018.
主要成果:
- 在QTDB上取得了优异的心电图波形划分F1分数:99.14% (P波),98.48% (QRS波),98.46% (T波).
- 在LUDB上表现出强烈的概括,平均SE (99.05%),积极预测率 (94.59%) 和F1 (94.62%).
- 在CPSC2021上实现了99.20%的AF识别准确度,并在CPSC2018.2018上表现强.
结论:
- SEResUTer显著提升了心电图划分和AF检测能力.
- 模型的增强架构和掩盖策略提高了准确性和概括性.
- 这种方法促进了大型心电图数据集的自动分析,有助于诊断心脏异常.
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