We introduce a rapid and precise analytical approach for analyzing cerebral blood flow (CBF) using Diffuse Correlation Spectroscopy (DCS) with the application of the Extreme Learning Machine (ELM). Our evaluation of ELM and existing algorithms involves a comprehensive set of metrics. We assess these algorithms using synthetic datasets for both semi-infinite and multi-layer models. The results demonstrate that ELM consistently achieves higher fidelity across various noise levels and optical parameters, showcasing robust generalization ability and outperforming iterative fitting algorithms. Through a comparison with a computationally efficient neural network, ELM attains comparable accuracy with reduced training and inference times. Notably, the absence of a back-propagation process in ELM during training results in significantly faster training speeds compared to existing neural network approaches. This proposed strategy holds promise for edge computing applications with online training capabilities.

翻译：我们提出了一种利用漫射相关光谱（DCS）结合极限学习机（ELM）进行脑血流（CBF）分析的快速且精确的方法。我们对ELM及现有算法的评估涵盖了一套全面的指标。使用半无限及多层模型的合成数据集对这些算法进行了评估。结果表明，ELM在不同噪声水平和光学参数下始终能实现更高的保真度，展现出强大的泛化能力，且优于迭代拟合算法。通过与计算高效的神经网络对比，ELM在减少训练和推理时间的同时达到了相当的准确性。值得注意的是，ELM在训练过程中无需反向传播，因此其训练速度显著快于现有神经网络方法。该策略有望为具备在线训练能力的边缘计算应用提供支持。