An Improved Signal Segmentation Method using Genetic Algorithm

ABSTRACT In many signal processing application, the signal of interest is often divided into epochs. In these applications, the segmented signal is preferred to have no change on the statistical characteristics of the epochs. Modified Varri is among the segmentation methods with an acceptable accuracy. There are three parameters affecting on the accuracy of this method. These parameters are set experimentally. Hence, they may not be optimal for any signal segmentation application. We have used Genetic Algorithm (GA) in this research to choose appropriate values for these parameters in any signal segmentation application. The proposed technique was applied on both synthetic signal and Electroencephalography (EEG) to evaluate its performance. The results indicate superiority of the proposed method in signal segmentation compared to the original approach. General Terms Signal segmentation, Non-stationary Signal, Adaptive Segmentation, Genetic Algorithm (GA) and EEG Signal.

[1]  D. Wang,et al.  Automatic audio segmentation using the Generalized Likelihood Ratio , 2008, 2008 2nd International Conference on Signal Processing and Communication Systems.

[2]  Jean-Yves Potvin,et al.  Genetic Algorithms for the Traveling Salesman Problem , 2005 .

[3]  Tong Li,et al.  Web-Based Application for Traffic Anomaly Detection Algorithm , 2007, Second International Conference on Internet and Web Applications and Services (ICIW'07).

[4]  H Hasanpour,et al.  ADAPTIVE SEGMENTATION WITH OPTIMAL WINDOW LENGTH SCHEME USING FRACTAL DIMENSION AND WAVELET TRANSFORM , 2009 .

[5]  Hamid Hassanpour,et al.  An improved adaptive signal segmentation method using fractal dimension , 2010, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010).

[6]  Jianhua Yue,et al.  A GA and Particle Swarm Optimization based hybrid algorithm , 2008, IEEE Congress on Evolutionary Computation.

[7]  Jean Gotman,et al.  Adaptive segmentation of electroencephalographic data using a nonlinear energy operator , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[8]  A Värri,et al.  Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering. , 1991, International journal of bio-medical computing.

[9]  H. Hassanpour,et al.  Adaptive Segmentation Using Wavelet Transform , 2007, 2007 International Conference on Electrical Engineering.

[10]  Jeffrey D. Scargle.  Studies in Astronomical Time Series Analysis: V. Bayesian Blocks, A New Method to Analyze Structure in , 1998 .

[11]  Adrian Stoica,et al.  A customizable FPGA IP core implementation of a general purpose Genetic Algorithm engine , 2010, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[12]  M. Mohammadi,et al.  Neural Network Ensembles Using Clustering Ensemble and Genetic Algorithm , 2008, 2008 Third International Conference on Convergence and Hybrid Information Technology.

[13]  Jiachen Yang,et al.  A Stereo Matching Algorithm based on Genetic Algorithm with Propagation Stratagem , 2009, 2009 International Workshop on Intelligent Systems and Applications.

[14]  G. Ivanova,et al.  Fractal dimension as a feature for adaptive electroencephalogram segmentation in epilepsy , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Lenka Lhotská,et al.  Classification of Long-Term EEG Recordings , 2004, ISBMDA.