Feature fusion analysis approach based on synchronous EEG-fNIRS signals: application in etomidate use disorder individuals

依托咪酯 脑电图 特征(语言学) 计算机科学 模式识别(心理学) 神经科学 人工智能 心理学 医学 语言学 药理学 哲学 异丙酚
作者
Tianxin Gao,Chao Chen,Guangyao Liang,Yuchen Ran,Qiuping Huang,Zhenjiang Liao,Bolin He,T. Liu,Xiaoying Tang,Hongxian Chen,Yingwei Fan
出处
期刊:Biomedical Optics Express [Optica Publishing Group]
卷期号:16 (2): 382-382 被引量:2
标识
DOI:10.1364/boe.542078
摘要

Etomidate is commonly used for induction of anesthesia, but prolonged use can affect brain neurovascular mechanisms, potentially leading to use disorders. However, limited research exists on the impact of etomidate on brain function, and accurately and noninvasively extracting and analyzing neurovascular brain features remains a challenge. This study introduces a novel feature fusion approach based on whole-brain synchronous Electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) signals aimed at addressing the difficulty of jointly analyzing neural and hemodynamic signals and features in specific locations, which is critical for understanding neurovascular mechanism changes in etomidate use disorder individuals. To address the challenge of optimizing the accuracy of neurovascular coupling analysis, we proposed a multi-band local neurovascular coupling (MBLNVC) method. This method enhances spatial precision in NVC analysis by integrating multi-modal brain signals. We then mapped the different brain features to the Yeo 7 brain networks and constructed feature vectors based on these networks. This multilayer feature fusion approach resolves the issue of analyzing complex neural and vascular signals together in specific brain locations. Our approach revealed significant neurovascular coupling enhancement in the sensorimotor and dorsal attention networks ( p < 0.05, FDR corrected), corresponding with different frequency bands and brain networks from single-modal features. These features of the intersection of bands and networks showed high sensitivity to etomidate using machine learning classifiers compared to other features (accuracy: support vector machine (SVM) - 82.10%, random forest (RF) - 80.50%, extreme gradient boosting (XGBoost) - 78.40%). These results showed the potential of the proposed feature fusion analysis approach in exploring changes in brain mechanisms and provided new insights into the effects of etomidate on resting neurovascular brain mechanisms.

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