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Spike Detection from EEG Signals with Aid of Morphological Filters and Hybrid GAPSO
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Abstract
Studying the behavior of spikes in EEG is important for detecting brain abnormality. In EEG recorded signal contain large amount of spikes, so the spike detection is a technical challenge one. Morphological filters are normally used to separate this spikes from the recorded EEG signal. In existing technique the Gaussian function is used in morphological filter to find out the optimal structuring element. Using this function, it cannot find the accurate optimal structuring element, for that we have intended to propose a spike detection method using morphological filter with optimization technique. In the proposed method, initially the EEG signals noise is removed by the wavelet technique and this preprocessed EEG signals are given to the spike detection process. Morphological filter is used for the spike detection, in which optimal structuring elements are computed by the hybrid optimization technique as GA-PSO. After that, an amplitude threshold should be set to detect the occurrence of individual spikes. Hence, the spikes can be detected more effectively by achieving more number of correctly detected spikes rather than the conventional spike detection algorithms. Moreover our proposed technique performance is compared with the PSO and GA optimization methods.
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References
Hsun-Hsien Chang and Jos M. F. Moura, “Biomedical Signal Processing”, 2nd Edition of In Biomedical Engineerng and Design Handbook, McGraw Hill, Vol. 1, Chapter 22, pp. 559-579, 2010
Filligoi, “Chaos Theory and Semg”, Journals in Science and Technology, pp. 9-16, 2011
Muhammad Ibn Ibrahimy, “Biomedical Signal Processing and Applications”, In proceedings of the International Conference on Industrial Engineering and Operations Management, Dhaka, Bangladesh, 2010
Marie Chana, Daniel Estevea, Christophe Escribaa, Eric Campo, “A review of smart homes-Present state and future challenges”, Computer Methods and Programs in Biomedicine, Vol. 91, No. 1, pp. 55–81, 2008
Mitul Kumar Ahirwal and Narendra Dlondhe, “Power Spectrum Analysis of EEG Signals for Estimating Visual Attention”, International Journal of Computer Applications, Vol. 42,No. 15, pp. 22-25, 2012
Newton Price, Charles, J. de Sobral Cintra, Renato, T. Westwick, David and Mintchev, Martin, “Classification of Biomedical Signals using the Dynamic of the False Nearest Neighbours (DFNN) Algorithm”, International Journal of Information Theories & Applications, Vol. 12, pp. 18-24, 2005
Zecca, Micera, Carrozza, and Dario “Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal”, Critical Review in Biomedical Engineering, Vol. 30, No. 4, pp. 459-485, 2002
Prathyusha, Sreekanth Rao and Asha, “Extraction Of Respiratory Rate From PPG Signals UsingPca And Emd”, Vol. 1, No. 2, pp. 164-184, 2012
Ampil, “Primer for EEG Signal Processing in Anesthesia”, American Society of Anesthesiologists, Vol. 89, No. 4, pp. 980-1002, 1998
Michael Unser and Akram Aldroubi, “A Review of Wavelets in Biomedical Application”, Proceedings of the IEEE, Vol. 84, No. 4, 1996
Ali Sheikhani, Hamid Behnam, Mohammad Reza Mohammadi, Maryam Noroozian and Pari Golabi “Connectivity analysis of quantitative Electroencephalogram background activity in Autism disorders with short time Fourier transform and Coherence values”, Congress on Image and Signal Processing, pp. 207-212, 2008
Inan Guler, Elif Derya and Ubeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients”, Journal of Neuroscience Methods, Vol. 148, No. 2, pp. 113–121, 2005
Ali S. Al Mejrad, “Human Emotions Detection using Brain Wave Signals: A Challenging”, European Journal of Scientific Research, Vol. 44, No. 4, pp. 640-659, 2010
Scott Makeiga, Klaus Gramanna, Tzyy-Ping Junga, Terrence J. Sejnowskib and Howard Poiznerb, “Linking brain, mind and behavior”, International Journal of Psychophysiology, Vol. 73, No. 2, pp. 95–100, 2009
Gyorgy Buzsaki, Costas A. Anastassiou and Christof Koch, “The origin of extracellular fields and currents - EEG, ECOG, LFP and spikes”, Nature Reviews Neuroscience, Vol. 13, pp. 407-420, 2012
Joydeep Bhattacharya, “Complexity analysis of spontaneous EEG”, Acta Neurobiol., Vol. 60, pp. 495-501, 2000
Mijail Demian Serruyaa and Michael J. Kahana, “Techniques and devices to restorecognition”, Behavioural Brain Research, Vol. 192, No. 2, pp. 149–165, 2008
Tobias Banaschewski and Daniel Brandeis, “Annotation: What electrical brain activity tells us about brain function that other techniques cannot tell us - a child psychiatric perspective”, Journal of Child Psychology and Psychiatry, Vol. 48, No. 5, pp 415–435, 2007
Bao-Liang Lu, Jonghan Shin and Michinori Ichikawa, “Massively Parallel Classification of EEG Signals Using Min-Max Modular Neural Networks”, IEEE Transactions on Biomedical Engineering, Vol. 51 , No. 3, pp. 551- 558, 2004
Koo Hyoung Lee, “Evaluation of Attention and Relaxation Levels of Archers in Shooting Process using Brain Wave Signal Analysis Algorithms”, Korean Journal of the Science of Emotion and Sensibility, Vol. 12, No. 3, pp. 341-350, 2009
Rash Dubey and Pathak “Digital Analysis of EEG Brain Signal”, Web med central Brain, Vol. 1, No. 11, pp. 1-19, 2010
Mohammed A. Al-Manie, “Deconvolution methods for biomedical signals analysis”, Indian Journal of Science and Technology, Vol. 3 No. 2, pp. 105-109, 2010
Janett Walters-Williams and Yan Li “Using Invariant Translation to Denoise Electroencephalogram Signals”, American Journal of Applied Sciences, Vol. 8, No. 11, pp. 1122-1130, 2011
Podgorelec, “Analyzing EEG Signals with Machine Learning for Diagnosing Alzheimer’s Disease”, Elektronika IR Elektrotechnika, Vol. 18, No. 8, pp. 61-64, 2012
Xiaofeng Liu, Xianqiang Yang and Nanning Zheng, "Automatic extracellular spike detection with piecewise optimal morphological filter", Neuro computing, Vol. 79, pp. 132-139, 2012
http://www.physionet.org/pn6/chbmit/shoeb-icml-2010.pdf
Azizi, E., Abedikia, H., Haddadnia, J., Rezaee, K., Ghezelbash, M.R., A novel online EEG-based epileptic seizure onset detection algorithm based on GTDA features and KNN classifier, (2012) International Review on Computers and Software (IRECOS), 7 (6), pp. 2849-2855.
Ratna Babu, K., Sunitha, K.V.N., Enhancing hazy images with the aid of particle swarm optimization (PSO) and morphological operation, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 21-28.
Kazemifar, S., Boostani, R., An efficient adaptive segmentation algorithm on EEG signals to discriminate between subject with epilepsy and normal control, (2013) International Review on Computers and Software (IRECOS), 8 (1), pp. 256-261.
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