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

Azami, H, Hassanpour, H, Escudero, J and Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801, 2015. An intelligent approach for variable size segmentation of non-stationary signals. Journal of Advanced Research, 6 (5), pp. 687-698. ISSN 2090-1232

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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:	Journal article
Publication Title:	Journal of Advanced Research
Creators:	Azami, H., Hassanpour, H., Escudero, J. and Sanei, S.
Publisher:	Elsevier
Date:	September 2015
Volume:	6
Number:	5
ISSN:	2090-1232
Identifiers:	Number Type 10.1016/j.jare.2014.03.004 DOI S2090123214000368 Publisher Item Identifier
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	12 Feb 2018 16:32
Last Modified:	12 Feb 2018 16:33
URI:	https://irep.ntu.ac.uk/id/eprint/32679

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