DL-QC-fNIRS:一个深度学习工具,用于功能近红外光谱信号的自动化质量控制
Sabino Guglielmini1, Zhuofei Chen1, Martin Wolf1,2
1University Hospital Zurich, University of Zurich, Biomedical Optics Research Laboratory, Department of Neonatology, Zurich, Switzerland.
Neurophotonics
|December 31, 2025
概括
这项研究介绍了DL-QC-fNIRS,这是一种深度学习工具,可以准确评估功能近红外光谱 (fNIRS) 信号质量,提高光学神经成像中的数据可靠性.
科学领域:
- 神经成像是一种神经成像.
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 确保功能近红外光谱 (fNIRS) 信号质量对于可靠的光学神经成像至关重要.
- 像CV和SCI这样的传统指标经常使用任意值,导致fnirs道的错误分类.
研究的目的:
- 开发一个自动化的深度学习框架 (DL-QC-fNIRS),用于精确的,通道智能的fNIRS信号质量评估.
- 克服现有的基于指数的质量控制方法的局限性.
主要方法:
- 生成的连续波束转换氧血球蛋白信号的阴影图.
- 整合了对象特定的心脏频率提取,以提高生理特异性.
- 使用卷积神经网络 (CNN) 分类信号质量,对谷歌LeNet,ResNet-50,SqueezeNet和EfficientNet-B0.0进行比较.
主要成果:
- 在综合数据集上,GoogLeNet的准确性超过93%,显示出高度的敏感性和特异性.
- DL-QC-fNIRS的表现优于传统的CV和SCI指标,显示出更高的F1分数和更好的灵敏度和特异性的平衡.
- 该框架在独立和综合数据集上进行了验证.
结论:
- DL-QC-fNIRS提供了一个可扩展的专家级解决方案,用于提高fNIRS数据的可靠性和可重复性.
- 基于MATLAB的开源接口可以更轻松地集成到现有的fNIRS工作流中.
- 这种深度学习方法在光学神经成像数据预处理方面取得了重大进展.
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