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.

提出了一种利用扩散相关光谱学（DCS）和极限学习机（ELM）分析脑血流 (cerebral blood flow) 的快速准确分析方法，ELM 在不同噪声水平和光学参数下表现出更高的保真度，相较于迭代拟合算法具有更强大的泛化能力并能够胜过它们，与计算效率高的神经网络进行对比，ELM 在减少训练和推理时间的同时保持了可比较的准确性，ELM 的无反向传播训练过程导致训练速度显著加快，为具备在线训练能力的边缘计算应用提供了希望。