Levels of complexity in scale-invariant neural signals.
Levels of complexity in scale-invariant neural signals.
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DOI：
10.1103/physreve.79.041920
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发表时间：
2009-04
期刊：
影响因子：
--
通讯作者：
Yoneyama M
中科院分区：
文献类型：
--
作者：
Ivanov PCh;Ma QD;Bartsch RP;Hausdorff JM;Nunes Amaral LA;Schulte-Frohlinde V;Stanley HE;Yoneyama M
许多物理和生理信号表现出复杂的标度不变特征，其特征是1/f标度和长程幂定律相关性，这表明可能存在共同的控制机制。具体地说，已经提出，在多个时间尺度上受输入和反馈影响的动态过程可能足以产生1/f标度和标度不变性。作为神经控制下的分级多尺度生理系统的输出的两个生理信号的例子是人类的心跳和人类步态。在这里，我们表明，虽然在健康条件下，心脏搏动间隔和步态间隔时间序列都具有相似的1/f尺度，但它们可能仍属于不同的复杂性类别。我们对这两个信号波动的多重分维标度指数的分析表明，与健康心跳动力学中发现的多重分维行为相比，步态时间序列表现出更少的复杂性，接近于单分叉行为。此外，我们发现在短时间尺度和中等时间尺度上步态波动的幅度在符号上具有很强的反相关性并且接近随机行为，而在符号上的弱反相关性和心跳间期波动幅度的强正相关--表明心脏和步态控制的神经机制表现出不同的线性和非线性特征。这些发现之所以令人感兴趣，是因为它们强调了传统的两点相关方法在充分描述生理和物理动力学方面的局限性。此外，这些结果表明，不同的控制机制可能导致在神经调节下的生理系统和具有类似1/f比例的物理系统中观察到的不同程度的复杂性。
Many physical and physiological signals exhibit complex scale-invariant features characterized by 1/ f scaling and long-range power-law correlations, indicating a possibly common control mechanism. Specifically, it has been suggested that dynamical processes, influenced by inputs and feedback on multiple time scales, may be sufficient to give rise to 1/ f scaling and scale invariance. Two examples of physiologic signals that are the output of hierarchical multiscale physiologic systems under neural control are the human heartbeat and human gait. Here we show that while both cardiac interbeat interval and gait interstride interval time series under healthy conditions have comparable 1/ f scaling, they still may belong to different complexity classes. Our analysis of the multifractal scaling exponents of the fluctuations in these two signals demonstrates that in contrast to the multifractal behavior found in healthy heartbeat dynamics, gait time series exhibit less complex, close to monofractal behavior. Further, we find strong anticorrelations in the sign and close to random behavior for the magnitude of gait fluctuations at short and intermediate time scales, in contrast to weak anticorrelations in the sign and strong positive correlation for the magnitude of heartbeat interval fluctuations—suggesting that the neural mechanisms of cardiac and gait control exhibit different linear and nonlinear features. These findings are of interest because they underscore the limitations of traditional two-point correlation methods in fully characterizing physiological and physical dynamics. In addition, these results suggest that different mechanisms of control may be responsible for varying levels of complexity observed in physiological systems under neural regulation and in physical systems that possess similar 1/ f scaling.