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。

通过极限学习机实现快速脑血流分析

Fast Cerebral Blood Flow Analysis via Extreme Learning Machine

Diffuse correlation spectroscopy (DCS) is an emerging noninvasive technique
that measures the tissue blood flow, by using near-infrared coherent
point-source illumination to detect spectral changes. While machine learning
has demonstrated significant potential for measuring blood flow index (BFi), an
open question concerning the success of this approach pertains to its
robustness in scenarios involving deviations between datasets with varying
Signal-to-Noise Ratios (SNRs) originating from diverse clinical applications
and various setups. This study proposes a transfer learning approach, aims to
assess the influence of SNRs on the generalization ability of learned features,
and demonstrate the robustness for transfer learning. A synthetic dataset with
varying levels of added noise is utilized to simulate different SNRs. The
proposed network takes a 1x64 autocorrelation curve as input and generates BFi
and the correlation parameter beta. The proposed model demonstrates excellent
performance across different SNRs, exhibiting enhanced fitting accuracy,
particularly for low SNR datasets when compared with other fitting methods.
This highlights its potential for clinical diagnosis and treatment across
various scenarios under different clinical setups.

用于血流量指数测量的扩散相关光谱技术和机器学习的结合，通过转移学习的方法提高在不同信噪比和临床环境下对血流指数的鲁棒性。