A hybrid evolutionary approach to segmentation of non-stationary signals

Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varri?s and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm.