Ghouse Ameer, Nardelli Mimma, Valenza Gaetano
Bioengineering and Robotics Research Center E Piaggio, Università di Pisa, 56123 Pisa, Italy.
Department of Information Engineering, Università di Pisa, 56123 Pisa, Italy.
Entropy (Basel). 2020 Jul 11;22(7):761. doi: 10.3390/e22070761.
Conventional methods for analyzing functional near-infrared spectroscopy (fNIRS) signals primarily focus on characterizing linear dynamics of the underlying metabolic processes. Nevertheless, linear analysis may underrepresent the true physiological processes that fully characterizes the complex and nonlinear metabolic activity sustaining brain function. Although there have been recent attempts to characterize nonlinearities in fNIRS signals in various experimental protocols, to our knowledge there has yet to be a study that evaluates the utility of complex characterizations of fNIRS in comparison to standard methods, such as the mean value of hemoglobin. Thus, the aim of this study was to investigate the entropy of hemoglobin concentration time series obtained from fNIRS signals and perform a comparitive analysis with standard mean hemoglobin analysis of functional activation. Publicly available data from 29 subjects performing motor imagery and mental arithmetics tasks were exploited for the purpose of this study. The experimental results show that entropy analysis on fNIRS signals may potentially uncover meaningful activation areas that enrich and complement the set identified through a traditional linear analysis.
分析功能近红外光谱（fNIRS）信号的传统方法主要侧重于表征潜在代谢过程的线性动力学。然而，线性分析可能无法充分体现真正的生理过程，而这些生理过程能够全面表征维持脑功能的复杂非线性代谢活动。尽管最近已有在各种实验方案中表征fNIRS信号非线性特征的尝试，但据我们所知，尚无研究评估fNIRS复杂表征相对于标准方法（如血红蛋白平均值）的效用。因此，本研究的目的是研究从fNIRS信号获得的血红蛋白浓度时间序列的熵，并与功能激活的标准平均血红蛋白分析进行对比分析。本研究利用了来自29名执行运动想象和心算任务的受试者的公开可用数据。实验结果表明，对fNIRS信号进行熵分析可能会潜在地揭示有意义的激活区域，这些区域能够丰富并补充通过传统线性分析确定的区域。
