Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
School of Information Technology and Computer Engineering, Shahrood University, Iran.
J Adv Res. 2015 Sep;6(5):687-98. doi: 10.1016/j.jare.2014.03.004. Epub 2014 Mar 19.
In numerous signal processing applications, non-stationary signals should be segmented to piece-wise stationary epochs before being further analyzed. In this article, an enhanced segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals, such as electroencephalogram (EEG), magnetoencephalogram (MEG) and electromyogram (EMG), is proposed. In the proposed approach, discrete wavelet transform (DWT) decomposes the signal into orthonormal time series with different frequency bands. Then, the FD of the decomposed signal is calculated within two sliding windows. The accuracy of the segmentation method depends on these parameters of FD. In this study, four EAs are used to increase the accuracy of segmentation method and choose acceptable parameters of the FD. These include particle swarm optimization (PSO), new PSO (NPSO), PSO with mutation, and bee colony optimization (BCO). The suggested methods are compared with other most popular approaches (improved nonlinear energy operator (INLEO), wavelet generalized likelihood ratio (WGLR), and Varri's method) using synthetic signals, real EEG data, and the difference in the received photons of galactic objects. The results demonstrate the absolute superiority of the suggested approach.
在许多信号处理应用中，非平稳信号应该分段成分段平稳的时段，然后再进一步分析。在本文中，提出了一种基于分形维数（FD）和进化算法（EA）的增强的非平稳信号（如脑电图（EEG）、脑磁图（MEG）和肌电图（EMG））分段方法。在提出的方法中，离散小波变换（DWT）将信号分解为具有不同频带的正交时间序列。然后，在两个滑动窗口内计算分解信号的 FD。分段方法的准确性取决于 FD 的这些参数。在这项研究中，使用了四种 EA 来提高分段方法的准确性并选择可接受的 FD 参数。这些包括粒子群优化（PSO）、新型 PSO（NPSO）、带突变的 PSO 和蜜蜂群优化（BCO）。使用合成信号、真实 EEG 数据和星系物体接收到的光子差异，将建议的方法与其他最流行的方法（改进的非线性能量算子（INLEO）、小波广义似然比（WGLR）和 Varri 方法）进行比较。结果表明，所提出的方法具有绝对优势。
