Assess the level of consciousness in patients with disorders of consciousness by combining resting-state and auditory-evoked EEG
Wenjin Zhang, Xiaochu Shi, Meng Li, Lipeng Zhang, Rui Zhang, Xing Wu, Mengjie Xin, Runtao Li, Hui Zhang, Yuxia Hu
TL;DR
This study improves the assessment of consciousness in patients with disorders of consciousness by combining resting-state and auditory-evoked EEG data.
Contribution
A novel method combining resting-state and auditory-evoked EEG features for more accurate consciousness assessment.
Findings
SC-Theta, SC-Alpha, NI-Alpha, and ERP features were significantly correlated with consciousness levels.
The proposed model achieved an average grouping accuracy of 92.4%.
The method shows high effectiveness and reliability for clinical awareness assessments.
Abstract
Electroencephalography (EEG) can provide objective neural marker for assessing the level of consciousness of patients with disorders of consciousness (DoC), but current research mainly focuses on the EEG features of a single modality, such as the resting-state or the evoked state, which results in less than ideal assessment accuracy. To accurately assess the level of consciousness of DoC patients, we proposed a new method by combine with resting-state and auditory-evoked EEG. The EEG data of resting-state and auditory-evoked potential were collected from 157 DoC patients. Then, nonlinear dynamics feature (NDF) include spatiotemporal correlation entropy and neuromodulation intensity of multimodal EEG were extracted. Next, the multi-form feature selection algorithm (MFFS) was adopted to optimize the extracted EEG features. Finally, a diagnosis model was constructed using support vector…
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Taxonomy
TopicsTraumatic Brain Injury Research · EEG and Brain-Computer Interfaces · Epilepsy research and treatment