深度学习模型在心电图噪声检测和分类中的稳定性
Saifur Rahman1, Shantanu Pal1, John Yearwood1
1School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
Computer methods and programs in biomedicine
|May 30, 2024
概括
深度学习模型,特别是卷积神经网络 (CNN),有效地分类心电图 (ECG) 噪声. ResNet和VGG架构显示出高精度,ResNet提供与VGG相似的性能,但复杂性降低,以便更好地进行ECG分析.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 信号分析对于心脏病检测至关重要,但受到噪音的阻碍.
- 传统的过方法可以扭曲重要的心电图生物标志物.
- 现有的深度学习 (DL) 对心电图噪声的方法缺乏对循环神经网络 (RNN) 和卷积神经网络 (CNN) 架构的比较分析.
研究的目的:
- 引入基于知识的ECG过系统,使用DL进行噪声分类.
- 在医疗物联网 (IoMT) 框架内比较流行的计算机视觉模型架构 (CNN和RNN) 的性能和复杂性.
- 评估DL模型的选择性ECG过,最大限度地减少信号扭曲.
主要方法:
- 开发了一个基于DL的系统来分类ECG噪声类型.
- 实现并比较各种CNN架构 (AlexNet,VGG,ResNet) 和RNNs.
- 在实用的IoMT框架内对六个数据集进行了评估模型.
主要成果:
- 基于CNN的心电图噪声分类器在性能和训练时间方面表现优于基于RNN的模型.
- 亚历克斯网,VGG和ResNet实现了超过70%的准确性,特异性,敏感性和F1评分.
- VGG和ResNet表现相似,ResNet的复杂性不如VGG.
结论:
- 基于DL的ECG噪声分类器通过启用选择性过来增强知识驱动的ECG过系统.
- 在评估的CNN和RNN模型中,VGG和ResNet表现优越.
- ResNet为VGG提供了一个引人注目的替代方案,为ECG噪声分类提供了可比结果,但模型复杂性降低.
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