17-03-2014, 06:15 PM
Abstract
Some nonlinear physiological bio-signals like
Electroencephalograph (EEG) are not intuitive for
analysis. Some mathematic model should be used to
process the EEG data first. In this paper, theories and
methodology of fractal dimension were introduced
first. The enhanced Dynamic Fractal Dimension Model
(DFDM) with sliding window was developed to
analyze the nonlinear physiological bio-signals
allowing tracking minor changes and showing how the
fractal dimensions vary with time.
1. Introduction
Recently, it has been found that the physiological
bio-signals like EEG, heart rate, blood pressure exhibit
fractal-like, nonlinear patterns [1]. Fractal methods have
been proven to be valuable as a tool to quantify the
heterogeneity of physiological time-series and the
underlying regulation processes [1, 2, 3]. Because of the
nonlinear nature of these processes they spontaneously
drive themselves toward some critical points (in which
sudden transition in systems behaviour occurs) in a
deterministic but unpredictable manner, a phenomenon
referred to as self-organized criticality. For the case of
EEG signal, it did match some characteristics of fractal
dimension analysis system. Firstly, the nature of
external excitation forces can normally be qualified
and the amplitude and frequency can be measured.
Secondly, a single input frequency or relatively small
number of input frequencies can generate a continuous
frequency spectrum in the EEG signal. Thirdly, the
fluctuation of EEG signal can not be directly related to
input signal in mathematical equations [4].
Some nonlinear physiological bio-signals like
Electroencephalograph (EEG) are not intuitive for
analysis. Some mathematic model should be used to
process the EEG data first. In this paper, theories and
methodology of fractal dimension were introduced
first. The enhanced Dynamic Fractal Dimension Model
(DFDM) with sliding window was developed to
analyze the nonlinear physiological bio-signals
allowing tracking minor changes and showing how the
fractal dimensions vary with time.
1. Introduction
Recently, it has been found that the physiological
bio-signals like EEG, heart rate, blood pressure exhibit
fractal-like, nonlinear patterns [1]. Fractal methods have
been proven to be valuable as a tool to quantify the
heterogeneity of physiological time-series and the
underlying regulation processes [1, 2, 3]. Because of the
nonlinear nature of these processes they spontaneously
drive themselves toward some critical points (in which
sudden transition in systems behaviour occurs) in a
deterministic but unpredictable manner, a phenomenon
referred to as self-organized criticality. For the case of
EEG signal, it did match some characteristics of fractal
dimension analysis system. Firstly, the nature of
external excitation forces can normally be qualified
and the amplitude and frequency can be measured.
Secondly, a single input frequency or relatively small
number of input frequencies can generate a continuous
frequency spectrum in the EEG signal. Thirdly, the
fluctuation of EEG signal can not be directly related to
input signal in mathematical equations [4].