Detecting dynamical change in nonlinear time series

被引：35

作者：

Hively, LM ^{[1
]}

Gailey, PC ^{[1
]}

Protopopescu, VA ^{[1
]}

机构：

[1] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA

来源：

PHYSICS LETTERS A | 1999年 / 258卷 / 2-3期

基金：

美国能源部;

关键词：

D O I：

10.1016/S0375-9601(99)00342-4

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We present a robust, model-independent technique for measuring changes in the dynamics underlying nonlinear time-serial data. After constructing discrete density distributions of phase-space points on the attractor for time-windowed data sets, we measure the dissimilarity between density distributions via L-1-distance and chi(2) statistics. The discriminating power of the new measures is first tested on the Lorenz model and then applied to EEG data to detect the transition between non-seizure and epileptic activity. We find a clear superiority of the new measures in comparison to traditional nonlinear measures as discriminators of changing dynamics. (C) 1999 Published by Elsevier Science B.V. All rights reserved.

引用

页码：103 / 114

页数：12

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