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复杂性、协调性与健康:避免分形分析中的陷阱和错误解读。

Complexity, coordination, and health: avoiding pitfalls and erroneous interpretations in fractal analyses.

机构信息

EA 2991, Movement to Health, Euromov, University of Montpellier I, France.

出版信息

Medicina (Kaunas). 2011;47(7):393-8.

Abstract

BACKGROUND AND OBJECTIVE

The analysis of fractal fluctuation has become very popular because of the close relationships between health, adaptability, and long-range correlations. 1/f noise is considered a "magical" threshold, characterizing optimal functioning, and a decrease or conversely and increase of serial correlations, with respect to 1/f noise, is supposed to sign a kind of disadaptation of the system. Empirical results, however, should be interpreted with caution. In experimental series, serial correlations often present a complex pattern, resulting from the combination of long-range and short-term correlated processes. We show, in the present paper, that an increase in serial correlations cannot be directly interpreted as an increase in long-range correlations.

MATERIAL AND METHODS

Eleven participants performed four walking bouts following 4 individually determined velocities (slow, comfortable, high, and critical). Series of 512 stride intervals were collected under each condition. The strength of serial correlation was measured by the detrended fluctuation analysis. The effective presence of 1/f fluctuation was tested through ARFIMA modeling.

RESULTS

The strength of serial correlations tended to increase with walking velocity. However, the ARFIMA modeling showed that long-range correlations were significantly present only at slow and comfortable velocities.

CONCLUSIONS

The strength of correlations, as measured by classical methods, cannot be considered as predictive of the genuine presence of long-range correlations. Sometimes systems can present the moderate levels of effective long-range correlations, whereas in others cases, series can present high correlation levels without being long-range correlated.

摘要

背景与目的

由于健康、适应性和长程相关性之间的密切关系,分形波动分析变得非常流行。1/f 噪声被认为是一个“神奇”的阈值,表征着最佳功能,而与 1/f 噪声相比,序列相关性的减少或相反的增加被认为是系统的某种不适应。然而,经验结果应该谨慎解释。在实验系列中,序列相关性通常呈现出复杂的模式,这是由于长程和短期相关过程的组合造成的。在本文中,我们表明,序列相关性的增加不能直接解释为长程相关性的增加。

材料与方法

11 名参与者按照 4 种个体确定的速度(慢、舒适、高和临界)进行了 4 次步行。在每种情况下收集了 512 步间隔的系列。通过去趋势波动分析测量序列相关性的强度。通过 ARFIMA 建模测试 1/f 波动的有效存在。

结果

序列相关性的强度随着步行速度的增加而增加。然而,ARFIMA 建模表明,长程相关性仅在缓慢和舒适的速度下显著存在。

结论

经典方法测量的相关性强度不能被认为是长程相关性存在的预测指标。有时系统可以呈现出适度的有效长程相关性水平,而在其他情况下,序列可能呈现出高相关性水平,但没有长程相关性。

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