摘要
为了研究重复性亚脑震荡(Repetitive Subconcussion,RS)患者的脑网络时空变异性和转换复杂性,对25名跳伞运动员和25名健康对照的微状态参数以及微状态转换序列的Lempel-Ziv复杂度、样本熵、排列熵进行分析。研究结果发现RS患者的微状态B的覆盖率显著升高、微状态D的覆盖率显著降低、微状态C和微状态D的相互转换概率显著降低,微状态转换序列的Lempel-Ziv复杂度、样本熵、排列熵显著升高。使用微状态参数和非线性特征参数作为特征集,结合特征重要性排序与特征选择,分类准确率、敏感性、特异性最高都能达到80%以上,表明以上特征向量可以作为识别RS人群较好的生物标志物。
To investigate the spatiotemporal variability and transition complexity of brain networks in patients with Repetitive Subconcussion(RS),the microstate parameters and Lempel-Ziv complexity,sample entropy,and permutation entropy of the microstate transition sequences of 25 parachutists and 25 healthy controls were analyzed.The results showed that the coverage rate of microstate B in RS patients was significantly increased,the coverage rate of microstate D was significantly decreased,and the transition probability between microstates C and D was significantly reduced.The Lempel-Ziv complexity,sample entropy,and permutation entropy of the microstate transition sequences were significantly increased.Using microstate parameters and nonlinear feature parameters as the feature set,combined with feature importance ranking and feature selection,the highest classification accuracy,sensitivity,and specificity could all reach over 80%,indicating that the above feature vectors can be used as good biomarkers for identifying the RS population.
作者
张一帆
李响
周慧
田敏
高军峰
ZHANG Yifan;LI Xiang;ZHOU Hui;TIAN Min;GAO Junfeng(South-Central Minzu University,School of Biomedical Engineering,Wuhan 430074,China;South-Central Minzu University,Key Laboratory of Cognitive Science of State Ethnic Affairs Commission,Wuhan 430074,China)
出处
《中南民族大学学报(自然科学版)》
2026年第1期60-68,共9页
Journal of South-Central Minzu University(Natural Science Edition)
基金
国家自然科学基金资助项目(81601586)
中南民族大学中央高校基本科研业务费专项资金资助项目(CZZ24015,PTZ25008,CZZ25009)。
关键词
重复性亚脑震荡
脑电图
微状态分析
微状态转换序列
非线性动力学
Repetitive Subconcussions
electroencephalogram
microstate analysis
microstate transition sequences
nonlinear dynamics
