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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.05578 |
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| _version_ | 1866929231037988864 |
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| author | Chen, Xi Zang, Zhenya Li, Xingda |
| author_facet | Chen, Xi Zang, Zhenya Li, Xingda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05578 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Cerebral Blood Flow Analysis via Extreme Learning Machine Chen, Xi Zang, Zhenya Li, Xingda Machine Learning 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. |
| title | Fast Cerebral Blood Flow Analysis via Extreme Learning Machine |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.05578 |