title: An intelligent approach for variable size segmentation of non-stationary signals.
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.
authors.affiliation: Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
authors.forename: Hamed
authors.initials: H
authors.lastname: Azami
authors.affiliation: School of Information Technology and Computer Engineering, Shahrood University, Iran.
authors.forename: Hamid
authors.initials: H
authors.lastname: Hassanpour
authors.affiliation: Institute for Digital Communications, School of Engineering, The University of Edinburgh, UK.
authors.forename: Javier
authors.initials: J
authors.lastname: Escudero
authors.affiliation: Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, UK.
authors.forename: Saeid
authors.initials: S
authors.lastname: Sanei
keywords: Adaptive segmentation; Discrete wavelet transform; Evolutionary algorithm; Fractal dimension; Particle swarm optimization