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An intelligent approach for variable size segmentation of non-stationary signals.

Azami, H, Hassanpour, H, Escudero, J and Sanei, S (2015) An intelligent approach for variable size segmentation of non-stationary signals. J Adv Res, 6 (5). pp. 687-698.

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Abstract

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.

Item Type: Article
Authors :
NameEmailORCID
Azami, HUNSPECIFIEDUNSPECIFIED
Hassanpour, HUNSPECIFIEDUNSPECIFIED
Escudero, JUNSPECIFIEDUNSPECIFIED
Sanei, Ss.sanei@surrey.ac.ukUNSPECIFIED
Date : September 2015
Identification Number : https://doi.org/10.1016/j.jare.2014.03.004
Uncontrolled Keywords : Adaptive segmentation, Discrete wavelet transform, Evolutionary algorithm, Fractal dimension, Particle swarm optimization
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:16
Last Modified : 17 May 2017 15:10
URI: http://epubs.surrey.ac.uk/id/eprint/838554

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