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The Investigation of Channel Selection Effects on Epileptic Analysis of EEG Signals

Year 2015, Volume: 3 , 236 - 241, 30.12.2015

Abstract

A great number of methods are used in order to increase the speed of decision units in epileptic analysis of the multi-channel EEG signals. Channel selection is one of the main methods used for the reduction of the processing load. By eliminating the non-distinct channels, the performance of the system can be improved. In this study, the seizure detection performances of EEG signals obtained by 21 different channels were evaluated. This study was carried out patient-specifically for each six patients. The feature set is generated via calculating 26 features from EEG signals. The dimension of feature set for each channel is reduced using Principal Component Analysis. The reduced feature sets were divided as training and testing data using cross-validation method. With Linear Discriminant Analysis, the classification was done for each channel and performances of channel were compared. Depending on the channel selection, almost 9% differences in the classification accuracies have been observed.

References

  • [1] N. Sivasankari, K. Thanushkodi, "Automated Epileptic Seizure Detection in EEG Signals Using FastICA and Neural Network", Int. J. Advance, Soft Comput. Appl., Vol. 1, No. 2, pp. 1-14, 2009. [2] A. Shoeb, J. Guttag, "Application of Machine Learning To Epileptic Seizure Detection", The 27th International Conference on Machine Learning ICML 2010, 21-24 June 2010, Haifa-Israel. [3] H. Vavadi, A. Ayatollahi, A. Mirzaei, "A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands", J. Biomedical Science and Engineering, Vol. 3, No. 2010, pp. 1182-1189,2010. [4] G. Ouyang, X. Li, Y. Li, X. Guan, "Application of wavelet-based similarity analysis to epileptic seizures prediction", Computers in Biology and Medicine, Vol. 37, No. 2007, pp. 430-437, 2007. [5] M. Arvaneh, C. Guan, K.K. Ang, H.C. Quek, "Optimizing the channel selection and classification accuracy in EEG-Based BCI", IEEE Transactions on Biomedical Engineering, Vol. 58, No. 6, pp. 1865-1873, 2011. [6] M. Qaraqe, M. Ismail, Q. Abbasi, E. Serpedin, "Channel Selection and Feature Enhancement for Improved Epileptic Seizure Onset Detector", 4th International Conference on Wireless Mobile Communication and Healthcare MOBIHEALTH 2014, 03-05 November 2014, Athens, Greece. [7] J. Duun-Henriksen, T.W. Kjaer, R.E. Madsen, L.S. Remvig, C.E. Thomsen, H.B.D. Sorensen, "Channel selection for automatic seizure detection", Clinical Neurophysiology, Vol. 123 No. 2012, pp. 84–92,2012. [8] S. Faul, W. Marnane, "Dynamic, location-based channel selection for power consumption reduction in EEG analysis", Computer Methods and Programs in Biomedicine, Vol. 108, No. 3, pp. 1206-1215,2012. [9] H. Tekgul, B.F.D. Bourgeois, K. Gauvreau, A.M. Bergin, "Electroencephalography in neonatal seizures: Comparison of reduced and a full 10-20 montage", Pediatric Neurology, Vol. 32, No. 3, pp. 155-161, 2005. [10] PhysioNet International database, 6.12.2011 http://www.physionet.org/physiobank/database/chbmit/ [11] Meyer-Baese, Pattern Recognition for Medical Imaging, Elsevier Academic Pres, California, 2004. [12] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, Wiley-Interscience, New York, 2001. [13] [14] [15] [16] [17] A.K. Junoh, M.N. Mansor, "Safety System Based on Linear Discriminant Analysis", 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 25-28 Augost 2012, Sanya, China. N.Panahi, M.G. Shayesteh, S. Mihandoost, B.Z. Varghahan, "Recognition of Different Datasets Using PCA, LDA, and Various Classifiers", 5th International Conference on Application of Information and Communication Technologies (AICT), 12-14 October 2011, Baku, Azerbaijan. A. ÜNSAL, "Diskriminant analizi ve uygulaması üzerine bir örnek", Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi (G.Ü.İ.İ.B.F.) Dergisi, Vol.2, No. 3, pp. 19-35, 2000. A. Fielding, "Cluster and Classification Techniques for the Biosciences”, Cambridge University Press, New York, 2000. M.M. Fraz, P. Remagnino, A. Hoppe, S.A. Barman, "Retinal image analysis aimed at extraction of vascular structure using linear discriminant analysis", 2013 International Conference on Computer Medical Applications (ICCMA), 20-22 January 2013, Sousse, Tunisia.
Year 2015, Volume: 3 , 236 - 241, 30.12.2015

Abstract

References

  • [1] N. Sivasankari, K. Thanushkodi, "Automated Epileptic Seizure Detection in EEG Signals Using FastICA and Neural Network", Int. J. Advance, Soft Comput. Appl., Vol. 1, No. 2, pp. 1-14, 2009. [2] A. Shoeb, J. Guttag, "Application of Machine Learning To Epileptic Seizure Detection", The 27th International Conference on Machine Learning ICML 2010, 21-24 June 2010, Haifa-Israel. [3] H. Vavadi, A. Ayatollahi, A. Mirzaei, "A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands", J. Biomedical Science and Engineering, Vol. 3, No. 2010, pp. 1182-1189,2010. [4] G. Ouyang, X. Li, Y. Li, X. Guan, "Application of wavelet-based similarity analysis to epileptic seizures prediction", Computers in Biology and Medicine, Vol. 37, No. 2007, pp. 430-437, 2007. [5] M. Arvaneh, C. Guan, K.K. Ang, H.C. Quek, "Optimizing the channel selection and classification accuracy in EEG-Based BCI", IEEE Transactions on Biomedical Engineering, Vol. 58, No. 6, pp. 1865-1873, 2011. [6] M. Qaraqe, M. Ismail, Q. Abbasi, E. Serpedin, "Channel Selection and Feature Enhancement for Improved Epileptic Seizure Onset Detector", 4th International Conference on Wireless Mobile Communication and Healthcare MOBIHEALTH 2014, 03-05 November 2014, Athens, Greece. [7] J. Duun-Henriksen, T.W. Kjaer, R.E. Madsen, L.S. Remvig, C.E. Thomsen, H.B.D. Sorensen, "Channel selection for automatic seizure detection", Clinical Neurophysiology, Vol. 123 No. 2012, pp. 84–92,2012. [8] S. Faul, W. Marnane, "Dynamic, location-based channel selection for power consumption reduction in EEG analysis", Computer Methods and Programs in Biomedicine, Vol. 108, No. 3, pp. 1206-1215,2012. [9] H. Tekgul, B.F.D. Bourgeois, K. Gauvreau, A.M. Bergin, "Electroencephalography in neonatal seizures: Comparison of reduced and a full 10-20 montage", Pediatric Neurology, Vol. 32, No. 3, pp. 155-161, 2005. [10] PhysioNet International database, 6.12.2011 http://www.physionet.org/physiobank/database/chbmit/ [11] Meyer-Baese, Pattern Recognition for Medical Imaging, Elsevier Academic Pres, California, 2004. [12] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, Wiley-Interscience, New York, 2001. [13] [14] [15] [16] [17] A.K. Junoh, M.N. Mansor, "Safety System Based on Linear Discriminant Analysis", 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 25-28 Augost 2012, Sanya, China. N.Panahi, M.G. Shayesteh, S. Mihandoost, B.Z. Varghahan, "Recognition of Different Datasets Using PCA, LDA, and Various Classifiers", 5th International Conference on Application of Information and Communication Technologies (AICT), 12-14 October 2011, Baku, Azerbaijan. A. ÜNSAL, "Diskriminant analizi ve uygulaması üzerine bir örnek", Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi (G.Ü.İ.İ.B.F.) Dergisi, Vol.2, No. 3, pp. 19-35, 2000. A. Fielding, "Cluster and Classification Techniques for the Biosciences”, Cambridge University Press, New York, 2000. M.M. Fraz, P. Remagnino, A. Hoppe, S.A. Barman, "Retinal image analysis aimed at extraction of vascular structure using linear discriminant analysis", 2013 International Conference on Computer Medical Applications (ICCMA), 20-22 January 2013, Sousse, Tunisia.
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Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Articlessi
Authors

Murat Yıldız

Erhan Bergil

Publication Date December 30, 2015
Published in Issue Year 2015 Volume: 3

Cite

APA Yıldız, M., & Bergil, E. (2015). The Investigation of Channel Selection Effects on Epileptic Analysis of EEG Signals. Balkan Journal of Electrical and Computer Engineering, 3, 236-241.

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