Hu Yuxia, Wang Yufei, Zhang Rui, Hu Yubo, Fang Mingzhu, Li Zhe, Shi Li, Zhang Yankun, Zhang Zhong, Gao Jinfeng, Zhang Lipeng
School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.
Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China.
Cogn Neurodyn. 2023 Jun;17(3):661-669. doi: 10.1007/s11571-022-09849-4. Epub 2022 Aug 6.
The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR ( < 0.05) among the three groups. Regarding nonlinear parameters, ApEn, SampEn, Lz, and C showed significant differences ( < 0.05). The optimal feature classification combination accuracy rate reached 85.3%. Our research shows that resting-state EEG indicators could be used for stroke rehabilitation evaluation.
运动功能评估对于中风患者的康复至关重要。然而，常用的评估方法基于行为评分，缺乏直接反映大脑运动功能的神经学指标。本研究的目的是调查静息态脑电图指标是否能改善中风康复评估。我们招募了68名参与者并记录了他们的静息态脑电图数据。根据Brunnstrom分期，将参与者分为三组：重度、中度和轻度。计算了10个定量脑电图（QEEG）和5个静息态脑电图的非线性参数以进行进一步分析。进行了统计检验，并使用遗传算法支持向量机选择最佳特征组合进行分类。我们发现QEEG参数在三组之间的Delta、Alpha1、Alpha2、DAR和DTABR中显示出显著差异（<0.05）。关于非线性参数，ApEn、SampEn、Lz和C显示出显著差异（<0.05）。最佳特征分类组合准确率达到85.3%。我们的研究表明，静息态脑电图指标可用于中风康复评估。
