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更快的R-CNN方法用于估计电心图中的全球QRS持续时间,使用有限数量的注释数据

Samir Abdel-Rahman¹, Pavel Antiperovitch², Anthony Tang²

¹Department of Electrical & Computer Engineering, Western University, London, ON, Canada; Robarts Research Institute, Western University, London, ON, Canada.

Computers in biology and medicine

|April 26, 2025

概括

这项研究引入了一种新的深度学习方法,可以使用最小的标记数据从心电图 (ECG) 准确估计QRS持续时间 (QRSd). 这种方法有效地在小型心电图数据库上训练模型,改善了对左捆分支区块等条件的诊断能力.

科学领域:

计算心脏病学和医学成像分析.
深度学习在心电图 (ECG) 信号处理中的应用.

背景情况:

在心电图中精确测量QRS持续时间 (QRSd) 对于诊断心脏疾病至关重要,包括左捆分支阻塞.
现有的ECG数据库往往缺乏足够的QRS划分标签,这阻碍了深度学习模型的训练.
手动标记中位数是耗时的,并限制了心电图分析的可扩展性.

研究的目的:

开发和评估一个方法来估计全球QRSd使用深度学习对象检测模型训练在小的ECG数据库.
通过使用中间节拍来最大限度地减少手工标签的要求.
评估具有不同骨干配置和心电图像格式的Faster R-CNN模型的性能.

主要方法:

电脑心电图记录被细分为个别的心跳,并转化为人工图像.
一个更快的R-CNN物体检测模型被用于QRSd估计.
模型使用二进制和RGB ECG图像格式的VGG-16,VGG-19和RESNET-18骨干进行了测试.

主要成果:

性能最好的模型 (VGG-19与RGB图像) 在内部数据集上实现了根平均平方误差 (RMSE) 10.4 ± 0.8 ms和平均绝对误差 (MAE) 8.2 ± 1.0 ms.
在一个独立的数据集上,该模型实现了7.0 ± 1.1 ms的RMSE和5.3 ± 0.9 ms的MAE.
该方法证明了高QRSd估计精度和有效的概括到未见的数据.

结论:

拟议的方法可以通过心电图进行准确的QRSd估计,而手工标签的努力显著减少.
这种方法在有限的ECG数据集上促进了深度学习模型的高效训练.
这些发现表明,通过可访问和可扩展的心电图分析,改善心脏病的诊断是有希望的.

关键词:

深度学习是一种深度学习.电心电图 (ECG) 是一种心电图.更快的R-CNN 在线复杂的QRS复合体 QRS持续时间 (QRSd)

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