基于EMD去趋势波动的脑疲劳模糊熵分析

Fuzzy Entropy Analysis of Mental Fatigue Based on EMD Detrended Fluctuation

脑疲劳是由于人们长时间地从事重复单一或高负荷的认知活动所引起的,短时间的脑疲劳会引起注意力下降、工作效率降低,而长时间的脑疲劳则会造成脑功能损伤。提取脑疲劳特征有助于脑疲劳的检测,预防脑疲劳带来的危害。熵能够反映脑疲劳状态下大脑复杂度的变化情况,有望成为评价脑疲劳的指标。但是,熵对脑电信号特征的提取受趋势重叠的影响,无法实现信号动态特性的准确描述,造成不同时间段得到的熵特征不一致。为解决趋势重叠对脑电信号熵特征的影响,将基于经验模式分解(EMD)的去趋势波动分析同熵值计算相结合,以4 h英语科技论文翻译作为脑疲劳诱发任务,记录14名本科生志愿者在正常安静和脑疲劳状态下的脑电信号,对比分析两种状态及3个时间段脑电信号的近似熵、模糊熵和去趋势模糊熵。结果表明,相比传统的近似熵和模糊熵,脑疲劳状态下去趋势模糊熵在左半球脑区的熵值较正常安静状态下显著降低(FC3,P=0.022;P5,P=0.007),且3个时间段有显著性差异的导联基本相同(3个时间段FC3导联P值分别为0.025、0.017、0.012,P5导联P值分别为0.011、0.006、0.017)。结果表明,去趋势模糊熵可以更好地表达两种状态下大脑复杂度的差异,且具有很好的时间稳定性。因此基于EMD的去趋势模糊熵可以更加快速有效地评价脑疲劳对大脑活动复杂度的影响。

Mental fatigue is caused by engaging in repeated single or high-load cognitive activities for long time.Short-term mental fatigue can cause decreased attention and reduced work efficiency,while long-term mental fatigue can cause brain damage.Extraction of mental fatigue characteristics can help detect mental fatigue and prevent the harm.Entropy can reflect changes of dynamic complexity under mental fatigue state and is expected to be an indicator for evaluating mental fatigue.However,entropy’s extraction of EEG signal characteristics is affected by the superimposed trends in signals,makeing it impossible to accurately describe the dynamic characteristics of the signal and resulting in inconsistent entropy characteristics obtained at different time periods.In order to solve the problems,the empirical mode decomposition(EMD)detrended fluctuation analysis was combined with fuzzy entropy to evaluate the dynamic complexity of EEG signal.The four-hour English scientific paper translation was used as a mental fatigue-inducing task and the EEG signal were recorded from 14 undergraduate volunteers in resting-state and mental fatigue state.The approximate entropy,fuzzy entropy and detrended fuzzy entropy of EEG signals in the two status and the three time periods were compared and analyzed.Results showed that compared with the traditional approximate entropy and fuzzy entropy,the detrended fuzzy entropy in the mental fatigue state was significantly different than that in the resting-state in the left hemisphere dominance(FC3,P=0.022;P5,P=0.007),and the electrodes with significant differences were basically consistent in the three time periods(The P-values of the FC3 in the three time periods are0.025,0.017,and 0.012,respectively,and the P-values of the P5 are 0.011,0.006,and 0.017).It was shown that the detrended fuzzy entropy could better express the difference of brain complexity in two status,and had good time stability.Therefore,EMD-based detrended fuzzy entropy can be used to evaluate the impact of mental fatigue on brain dynamic complexity more quickly and effectively.