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Erschienen in:
Electroencephalogram (EEG) and Its Background Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

7. Injecting Principal Component Analysis with the OA Scheme in the Epileptic EEG Signal Classification

verfasst von : Siuly Siuly, Yan Li, Yanchun Zhang

Erschienen in: EEG Signal Analysis and Classification

Verlag: Springer International Publishing

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Abstract

This chapter presents a different design for reliable feature extraction for the classification of epileptic seizures from multiclass EEG signals. In this chapter, we introduce a principal component analysis (PCA) method with the optimum allocation (OA) scheme, named as OA_PCA for extracting reliable characteristics from EEG signals.

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Vorheriges Kapitel A Statistical Framework for Classifying Epileptic Seizure from Multi-category EEG Signals
Nächstes Kapitel Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications
Literatur
Acharjee, P.P., and Shahnaz, C. Multiclass Epileptic Seizure Classification Using Time-Frequency Analysis of EEG Signals, 2012 7th International Conference on Electrical and Computer Engineering, 20–22 December, 2012, Dhaka, Bangladesh, pp. 260–263.
Acharya, U.R., Sree, S.V., Swapna, G., Martis, R.J., and Suri, J.S. Automated EEG analysis of epilepsy: A review, Knowledge-Based Systems 45 (2013) 147–165.
Bronzino, J.D. Principles of Electroencephalography (2nd ed.), The Biomedical Engineering Handbook: Second Edition, Boca Raton: CRC Press LLC, 2000.
Bajaj, V., and Pachori, R.B. Classification of seizure and nonseizure EEG signals using empirical mode decomposition, IEEE Transactions on Information Technology in Biomedicine 16 (6) (2012) 1135–1142.
Bajaj, V., and Pachori, R.B. Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals, Biomedical Engineering Letters, Springer 3 (1) (2013) 17–21.
Cochran, W.G. Sampling Techniques, Wiley, New York, 1977.
Cao, L. J., Chua, K. S., Chong, W. K., Lee, H. P., and Gu, Q. M. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine, Neurocomputing 55 (2003) 321–336.
Chawla, M., Verma, H., and Vinod, K. ECG modeling and QRS detection using principal component analysis, In: Proceedings of IET international conference, paper no. 04, MEDSIP06, Glas-gow, UK, 2006.
Cover, T., Hart, P. Nearest neighbor pattern classification, IEEE Transactions in Information Theory, IT-13 (1) (1967) 21–27.
Duda, R.O., Hart, P.E., and Strok, D.G. Pattern classification (2nd ed.), John, Wiley & Sons, 2001.
Friedman, N., Geiger, D., and Goldszmidt, M. Bayesian network classifiers, Machine Learning 29 (1997) 131–163.
Gestel, TV, Suykens, J.A.K., Lanckriet, G., Lambrechts, A., De Moor, B., and Vandewalle, J. Multiclass LS-SVMs: Moderated Outputs and Coding-Decoding Schemes, Neural processing letters 15(2002) 45–58.
Guler, I., and Ubeyli, E.D. Multiclass support vector machines for EEG-signal classification, IEEE Transactions on Information Technology in Biomedicine 11(2) (2007) 117–126.
Hope, R.M., Wang, Z., Wang, Z., Ji, Q., and Gray, W.D. Workload classification across subjects using EEG, In: Proceedings of the human factors and ergonomics society 55th annual meeting - 2011, pp. 202–206.
Han, J., Kamper, M, and Pei, J. Data mining: Concepts and techniques, Morgan Kaufmann, 2005.
Islam, M.N. An introduction to sampling methods: theory and applications, revised ed., Book World, Dhaka New Market & P.K. Roy road, Bangla Bazar, Dhaka-1100, 2007.
Kumar, S.P., Sriraam, N., Benakop, P.G., Jinaga, B.C. Entropies based detection of epileptic seizures with artificial neural network classifiers, Expert Systems with Applications 37 (2010) 3284–3291.
Lee, S.H., Lim, J.S., Kim, J.K., Yang, J, and Lee, Y. Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance, Computer Methods and Programs in Biomedicine 116 (1) (2014) 10–25.
Li, S., Zhou, W., Yuan, Q., Geng, S., Cai, D. Feature extraction and recognition of ictal EEG using EMD and SVM, Computers in Biology and Medicine 43 (7) (2013) 807–816.
Murugavel, A.S.M, Ramakrishnan, S., Balasamy, K., and Gopalakrishnan, T. Lyapunov features based EEG signal classification by multi-class SVM, 2011 World Congress on Information and Communication Technologies, pp. 197–201.
Oweis, R.J., and Abdulhay, E.W. Seizure classification in EEG signals utilizing Hilbert-Huang transform, Biomedical Engineering Online 10 (2011) 38–52.
Pachori, R.B., and Patidar, S. Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions, Computer Methods and Programs in Biomedicine 113 (2) (2014) 494–502.
Sharma, R and Pachori, R.B. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions, Expert Systems with Applications42 (3) (2015) 1106–1117.
Siuly, Li, Y., Wu, J., and Yang, J. Developing a Logistic Regression Model with Cross-Correlation for Motor Imagery Signal Recognition, In: IEEE/ICME International Conference on Complex Medical Engineering (ICME 2011), 22–25 May 2011, Harbin, China, pp. 502–507.
Shen, C.P., Chen, C.C., Hsieh, S.L., Chen, W.H., Chen, J.M., Chen, C.M., Lai, F., and Chiu, M.J. High-performance seizure detection system using a wavelet-approximate entropy-fSVM cascade with clinical validation, Clinical EEG and Neuroscience DOI:10.​1177/​1550059413483451​, 2013.
Ripley, B. Pattern recognition and neural networks, Cambridge: Cambridge university press, 1996.
Siuly, Li, Y., Wen, P. EEG signal classification based on simple random sampling technique with least square support vector machines, International journal of Biomedical Engineering and Technology 7(4) (2011a) 390–409.
Siuly, Li, Y., Wen, P. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain computer interface, Computer Methods and programs in Biomedicine 113 (3) (2014a) 767–780.
Siuly and Li, Y. Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach, Neural Computing & Applications DOI 10.​1007/​s00521-014-1753-3, 2014c.
Suykens, J.A.K., and Vandewalle, J. Least Square Support Vector Machine classifier, Neural Processing Letters 9 (3) 293–300.
Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J. Least Square Support Vector Machine, World Scientific, Singapore, 2002.
Sengur, A. Multiclass least-square support vector machines for analog modulation classification, Expert System with Applications 36(2009) 6681–6685.
Siuly, H. Wang and Y. Zhang (2016), ‘Detection of motor imagery EEG signal employing Naive Bayes based learning process’, Measurement 86, 148–158.
Siuly and Y. Li, (2014b), ‘A novel statistical framework for multiclass EEG signal classification’, Engineering Applications of Artificial Intelligence, Vol. 34, pp. 154–167.
Siuly and Y. Li, (2015), ‘Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification’, Computer Methods and programs in Biomedicine, Computer Methods and programs in Biomedicine, Vol. 119, pp. 29–42.
Siuly, and Li, Y. (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain computer interface, IEEE Transactions on Neural Systems and Rehabilitation Engineering 20 (4) pp. 526–538.
Siuly, Li, Y., and Wen, P. Clustering technique-based least square support vector machine for EEG signal classification, Computer Methods and Programs in Biomedicine 104 (3) (2011b) 358–372.
Siuly, Li, Y., and Wen, P. Identification of Motor Imagery Tasks through CC-LR Algorithm in Brain Computer Interface, International Journal of Bioinformatics Research and Applications 9 (2) (2013)156–172.
Siuly, Li, Y., Wen, P. Analysis and classification of EEG signals using a hybrid clustering technique, In: The proceedings of the 2010 IEEE/ICME International Conference on Complex Medical Engineering (CME2010), 2010, pp. 34–39.
Siuly, Li, Y., Wen, P. Classification of EEG signals using Sampling Techniques and Least Square Support Vector Machines, In: The proceedings of Fourth International Conference on Rough Sets and Knowledge Technology (RSKT 2009), LNCS 5589 (2009), pp. 375–382.
Turk, M., and Pentl, A. Eigenfaces for recognition, Journal of Cognitive Neuroscience 3 (1991) 71–86.
Ubeyli, E.D. Decision support systems for time-varying biomedical signals: EEG signals classification, Expert Systems with Applications 36 (2009) 2275–2284.
Ubeyli, E.D. Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines, Computer in Biology and Medicine 38 (2008)14–22.
Xing, Y., Wu, X, Xu, Z. Multiclass least squares auto-correlation wavelet support vector machine, ICIC Express Letters 2 (4) (2008) 345–350.
Youping, F., Yungping, C., Wansheng, S., Yu, L. Multi-classification algorithm and its realization based on least square support vector machine algorithm, Journal of Systems Engineering and Electronics 16 (4) (2005) 901–907.
Zhang, H. The optimality of naive bayes, The international flairs conference 17 (2004).
Metadaten
Titel
Injecting Principal Component Analysis with the OA Scheme in the Epileptic EEG Signal Classification
verfasst von
Siuly Siuly
Yan Li
Yanchun Zhang
Copyright-Jahr
2016
Buch
EEG Signal Analysis and Classification

Print ISBN: 978-3-319-47652-0

Electronic ISBN: 978-3-319-47653-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47653-7_7

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