Abstract
Background
Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free.
Methodology/Principal Findings
To facilitate joint chaos and fractal analysis of biosignals, we present an adaptive algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction techniques; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; and (4) offers a new formulation of fractal and multifractal analysis that is better than existing methods when a biosignal contains a strong oscillatory component.
Conclusions
The presented approach is a valuable, versatile tool for the analysis of various types of biological signals. Its effectiveness is demonstrated by offering new important insights into brainwave dynamics and the very high accuracy in automatically detecting epileptic seizures from EEG signals.
Date of this Version
9-6-2011
DOI
10.1371/journal.pone.0024331
Repository Citation
Gao, Jianbo; Hu, Jing; and Tung, Wen-wen, "Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering." (2011). Department of Earth, Atmospheric, and Planetary Sciences Faculty Publications. Paper 159.
http://dx.doi.org/10.1371/journal.pone.0024331
Comments
This is the publisher pdf of Gao J, Hu J, Tung W-w (2011) Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering. PLoS ONE 6(9): e24331 and is available at: 10.1371/journal.pone.0024331.