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

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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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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References

  1. Iasemidis, L.D.: Epileptic seizure prediction and control. IEEE Trans. Biomed. Eng. 50(5), 549–558 (2003)

    Article  Google Scholar 

  2. Khan, Y.U., Gotman, J.: Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114(5), 898–908 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  4. Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst. Appl. 32(4), 1084–1093 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  7. Altunaya, S., Telatarb, Z., Erogulc, O.: Epileptic EEG detection using the linear prediction error energy. Expert Syst. Appl. 37(8), 5661–5665 (2010)

    Article  Google Scholar 

  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)

    Google Scholar 

  9. Swiderski, B., Osowski, S., Rysz, A.: Lyapunov exponent of EEG signal for epileptic seizure characterization. Chaos. 5, 82–87 (1995)

    Article  Google Scholar 

  10. Hasan, O.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36, 2027–2036 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  13. Thomasson, N., Hoeppner, T.J., Webber, C.L., Zbilut, J.P.: Recurrence quantification in epileptic EEGs. Phys. Lett. A 279, 94–101 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  20. Candes, E., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math. 59(8), 1207–1223 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  26. Faust, O., Acharya, U.R., Adeli, H., Adeli, A.: Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. SEIZURE: european. J. Epilepsy (2015). doi:10.1016/j.seizure.2015.01.012

    Google Scholar 

  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)

    Article  Google Scholar 

  28. Ubeyli, E.D.: Combined neural network model employing wavelet coefficients for EEG signals classification. Digit. Sig. Proc. 19(2), 297–308 (2009)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 61201428, 61302090), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2010FQ020, ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. BS2009SW003, BS2014DX015), the Graduate Innovation Foundation of University of Jinan (Grant No. YCX13011).

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Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, 250022, China

    Shanshan Chen, Qingfang Meng, Yuehui Chen & Dong Wang

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China

    Shanshan Chen, Qingfang Meng, Yuehui Chen & Dong Wang

Authors
  1. Shanshan Chen
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  2. Qingfang Meng
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  3. Yuehui Chen
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  4. Dong Wang
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Corresponding author

Correspondence to Qingfang Meng .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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© 2015 Springer International Publishing Switzerland

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Chen, S., Meng, Q., Chen, Y., Wang, D. (2015). Automatic Seizure Detection in EEG Based on Sparse Representation and Wavelet Transform. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_20

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

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