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2015 | OriginalPaper | Buchkapitel

Automatic Seizure Detection in EEG Based on Sparse Representation and Wavelet Transform

verfasst von : Shanshan Chen, Qingfang Meng, Yuehui Chen, Dong Wang

Erschienen in: Intelligent Computing Theories and Methodologies

Verlag: Springer International Publishing

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Abstract

Sparse representation has been widely applied to pattern classification in recent years. In the framework of sparse representation based classification (SRC), the test sample is represented as a sparse linear combination of the training samples. Due to the epileptic EEG signals are non-stationary and transitory, wavelet transform as a time-frequency analysis method is widely used to analyze EEG signals. In this work, a novel EEG signal classification method based on sparse representation and wavelet transform was proposed to detect the epileptic EEG from EEG recordings. The frequency subbands decomposed by wavelet transform provided more information than the entire EEG. The experimental results showed that the proposed method could classify the ictal EEG and interictal EEG with accuracy of 98 %.

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Literatur
1.
Iasemidis, L.D.: Epileptic seizure prediction and control. IEEE Trans. Biomed. Eng. 50(5), 549–558 (2003)CrossRef
2.
Khan, Y.U., Gotman, J.: Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114(5), 898–908 (2003)CrossRef
3.
Saab, M.E., Gotman, J.: A system to detect the onset of epileptic seizure in scalp EEG. Clin. Europhysiol. 116(2), 427–442 (2005)CrossRef
4.
Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32(4), 1084–1093 (2007)CrossRef
5.
Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123, 69–87 (2003)CrossRef
6.
Adeli, H., Ghosh, D.S., Dadmehr, N.: A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54(2), 205–211 (2007)CrossRef
7.
Altunaya, S., Telatarb, Z., Erogulc, O.: Epileptic EEG detection using the linear prediction error energy. Expert Syst. Appl. 37(8), 5661–5665 (2010)CrossRef
8.
Wang, X.Y., Meng, J., Qiu, T.S.: Research on chaotic behavior of epilepsy electroencephalogram of ehildren based on independent component analysis algorithm. J. Biomed. Engin. 24, 835–841 (2007)
9.
Swiderski, B., Osowski, S., Rysz, A.: Lyapunov exponent of EEG signal for epileptic seizure characterization. Chaos. 5, 82–87 (1995)CrossRef
10.
Hasan, O.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36, 2027–2036 (2009)CrossRef
11.
Achary, U.R., Molinari, F., Sree, S.V., Chattopadhyay, S., Ng, K.H., Suri, J.S.: Automated diagnosis of epileptic eeg using entropies. Biomed. Signal Process. Control 7, 401–408 (2012)CrossRef
12.
Meng, Q., Chen, S., Zhou, W., Yang, X.: Seizure detection in clinical EEG based on entropies and EMD. In: Guo, C., Hou, Z.-G., Zeng, Z. (eds.) ISNN 2013, Part II. LNCS, vol. 7952, pp. 323–330. Springer, Heidelberg (2013)CrossRef
13.
Thomasson, N., Hoeppner, T.J., Webber, C.L., Zbilut, J.P.: Recurrence quantification in epileptic EEGs. Phys. Lett. A 279, 94–101 (2001)CrossRef
14.
Acharya, U.R., Sree, S.V., Chattopadhyay, S., Yu, W.W., Alvin, P.C.: Application of recurrence quantification analysis for the automatic EEG signals. Int. J. Neural Syst. 21, 199–211 (2011)CrossRef
15.
Acharya, U.R., Sree, S.V., Suri, J.S.: Automatic detection of epileptic EEG signals using higher order cumulant features. Int. J. Neural Syst. 21(5), 403–414 (2011)CrossRefMATH
16.
Guo, L., Rivero, D., Seoane, J., Pazos, A.: Classification of EEG signals using relative wavelet energy and artificial neural networks. In: The First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 177–183 (2009)
17.
Zhao, J.L., Zhou, W.D., Liu, K., Cai, D.M.: EEG signal classification based on SVM and wavelet analysis. Comput. Appl. Softw. 28, 114–116 (2011)
18.
Clodoaldo, A., Lima, M., André, L.V., Coelho, S.C.: Automatic EEG signal classification for epilepsy diagnosis with relevance vector machines. Expert Syst. Appl. 36, 10054–10059 (2009)CrossRef
19.
Yuan, Q., Zhou, W.D., Li, S.F., Cai, D.M.: Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. 96, 29–38 (2011)CrossRefMATH
20.
Candes, E., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math. 59(8), 1207–1223 (2006)MathSciNetCrossRefMATH
21.
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRef
22.
Yang, M., Zhang, L., Feng, X.C., Zhang, D.: Fisher discrimination dictionary learning for sparse representation. In: Proceedings of the 13th IEEE International Conference on Computer Vision (ICCV), pp. 543–550 (2011)
23.
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw patches. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. (2008)
24.
Ren, Y.F., Wu, Y., Ge, Y.B.: A cotraining algorithm for EEG classification with biomimetic pattern recognition and sparse representation. Neurocomputing. 137, 212–222 (2014)CrossRef
25.
Wu, M., Wei, Z.H., Tang, L.M., Sun, Y.B., Xiao, L.: The reconstruction study of EEG signal based on sparse approximation and compressive sensing. Chinese J. Medi. Instrumentation. 34(4), 241–245 (2010)
26.
27.
Majumdar, K.K., Vardhan, P.: Automatic seizure detection in ECoG by differential operator and windowed variance. IEEE Trans. Neural Syst. Rehabil. Eng. 19(4), 356–365 (2011)CrossRef
28.
Ubeyli, E.D.: Combined neural network model employing wavelet coefficients for EEG signals classification. Digit. Sig. Proc. 19(2), 297–308 (2009)CrossRef
Metadaten
Titel
Automatic Seizure Detection in EEG Based on Sparse Representation and Wavelet Transform
verfasst von
Shanshan Chen
Qingfang Meng
Yuehui Chen
Dong Wang
Copyright-Jahr
2015
Buch
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22179-3

Electronic ISBN: 978-3-319-22180-9

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-22180-9_20