基于降维的振荡数据动力学与同步
Dynamics and Synchrony from Oscillatory Data via Dimension Reduction
摘要：
复杂的、振荡的数据来自于各种各样的生物、物理和社会系统。然而，时间序列的固有振荡和无处不在的噪声对现有的线性和非线性时间序列分析方法提出了巨大的挑战。我们利用最先进的技术在模式识别，特别是，降维技术，并建议重建的动态准确的循环规模。这是通过导出一个紧凑的表示的周期，通过全局优化，有效地保留了嵌入在一个高维欧氏空间的循环的拓扑结构。我们的方法证明了一个明显的成功捕获的内在动力学和微妙的同步模式从单/双变量振荡数据在传统的方法。应用到人体运动数据揭示了重要的动态信息，这使得健康受试者和那些神经病理之间的临床有前途的歧视。我们的研究结果也为理解人类行走的神经肌肉控制提供了基本的启示。
英文摘要：
Complex, oscillatory data arises from a large variety of biological, physical, and social systems. However, the inherent oscillation and ubiquitous noise pose great challenges to current methodology such as linear and nonlinear time series analysis. We exploit the state of the art technology in pattern recognition and specifically, dimensionality reduction techniques, and propose to rebuild the dynamics accurately on the cycle scale. This is achieved by deriving a compact representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high dimensional Euclidian space. Our approach demonstrates a clear success in capturing the intrinsic dynamics and the subtle synchrony pattern from uni/bivariate oscillatory data over traditional methods. Application to the human locomotion data reveals important dynamical information which allows for a clinically promising discrimination between healthy subjects and those with neural pathology. Our results also provide fundamental implications for understanding the neuromuscular control of human walking.
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