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Robust Epileptic Seizure Classification

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Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9772))

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Abstract

A lot of feature vectors and sub-band signals are considered for Epileptic seizure classification. Unfortunately, not all the feature vectors and sub-band signals contribute to the final result. In view of this limitation, we propose a modified Differential Evolution Feature Selection algorithm (MDEFS), which searches the best feature vector subset and the sub-band signals to distinguish three groups of subjects (healthy, ictal and interictal). From the experiment results, it is observed that the bagging method based on the optimal feature subset (the standard deviation attribute in the delta sub-band signal, the time-lag attribute in the delta sub-band signal, fractal dimension in the alpha sub-band signal, the correlation dimension attribute in the alpha sub-band signal and the standard deviation attribute in the beta sub-band signal) selected by MDEFS results in highest classification accuracy of 98.67 %.

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References

  1. Leppik, I.: Contemporary Diagnosis and Management of the Patient with Epilepsy. Handbooks in Health Care, Newton (2000)

    Google Scholar 

  2. Fisher, R., Van Boas, W.E., Blume, W., Elger, C., Genton, P., Lee, P., Engel, J.: Special article epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46(4), 470–472 (2005)

    Article  Google Scholar 

  3. Shoeb, A.: Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment, Massachusetts (2009)

    Google Scholar 

  4. Samanwoy, G.-D., Adeli, H., Dadmehr, N.: Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Biomed. Eng. 55(2), 512–518 (2008)

    Article  Google Scholar 

  5. Ocak, H.: Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Process. 88, 1858–1867 (2008)

    Article  MATH  Google Scholar 

  6. Shoeb, A., Edwards, H., Connolly, J., Bourgeois, B., Treves, S., Guttag, J.: Patient-specific seizure onset detection. Epilepsy Behav. 5, 483–498 (2004)

    Article  Google Scholar 

  7. Panda, R., Khobragade, P., Jambhule, P., Jengthe, S., Pal, P., Gandhi, T.: Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction. In: Proceedings of 2010 International Conference on Systems in Medicine and Biology, pp. 405–408 (2010)

    Google Scholar 

  8. Nageswari, M., Banu, U., Kumar, K., Sujith, S.: Feature extraction of ECG using Daubechies wavelet and classification based on fuzzy c-means clustering technique. In: Proceeding of National Conference on Control, Communication and Information Technology, pp. 43–47 (2013)

    Google Scholar 

  9. Guler, I., Ubeyli, E.: Multiclass support vector machines for EEG-signals classification. IEEE Trans. Inf. Technol. Biomed. 11(2), 117–126 (2007)

    Article  Google Scholar 

  10. Garg, S., Narvey, R.: Denoising & feature extraction of EEG signal using wavelet transform. Int. J. Eng. Sci. Technol. (IJEST) 5(6), 1249–1253 (2013)

    Google Scholar 

  11. Samanwoy, G.-D., Adeli, H., Dadmehr, N.: Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans. Biomed. Eng. 54(9), 1545–1551 (2007)

    Article  Google Scholar 

  12. Hsu, K.-C., Yu, S.-N.: Detection of seizures in EEG using subband non linear parameters and genetic algorithm. Comput. Biol. Med. 40, 823–830 (2010)

    Article  Google Scholar 

  13. Guo, L., Rivero, D., Pazos, A.: Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 193, 156–163 (2010)

    Article  Google Scholar 

  14. Song, Y., Crowcroft, J., Zhang, J.: Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J. Neurosci. Methods 210, 132–146 (2012)

    Article  Google Scholar 

  15. Han, L., Wang, H., Liu, C., Li, C.: Epileptic seizure detection using wavelet transform based sample entropy and support vector machine. In: Proceeding of the IEEE International Conference on Information and Automation, pp. 759–762 (2012)

    Google Scholar 

  16. Easwaramoorthy, D., Uthayakumar, R.: Analysis of biomedical EEG signals using wavelet transforms and multifractal analysis. In: Communication Control and Computing Technologies (ICCCCT), pp. 544–549 (2010)

    Google Scholar 

  17. Khoa, T., Ha, V., Toi, V.: Higuchi fractal properties of onset epilepsy electroencephalogram. Comput. Math. Methods Med. 2012, 1–6 (2012)

    MathSciNet  MATH  Google Scholar 

  18. Khushaba, R., Al-Ani, A., Al-Jumaily, A.: Feature subset selection using differential evolution and a statistical repair mechanism. Expert Syst. Appl. 38, 11515–11526 (2011)

    Article  Google Scholar 

  19. EEG time series download page. http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html. Accessed 2001

  20. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27:1–27:27 (2011)

    Google Scholar 

  21. Suratgar, A., Tavakoli, M., Hoseinabadi, A.: Modified Levenberg-Marquardt method for neural networks training. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 1(6), 1745–1747 (2007)

    Google Scholar 

  22. Kononenko, I., Sikonja, M.: Non-Myopic Feature Quality Evaluation with (R)ReliefF. Computational Methods of Feature Selection, pp. 169–191. Chapman & Hall/CRC, Boca Raton (2008)

    Google Scholar 

  23. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)

    Article  Google Scholar 

  24. Nabeel, A., Thasneem, F., Paul, J.: Detection of epileptic seizure event and onset using EEG. BioMed Res. Int. 2014, 1–7 (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Farrikh Alzami, Daxing Wang, Zhiwen Yu & Guoqiang Han

  2. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

    Jane You

  3. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Hau-San Wong

Authors
  1. Farrikh Alzami
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  2. Daxing Wang
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  3. Zhiwen Yu
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  4. Jane You
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  5. Hau-San Wong
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  6. Guoqiang Han
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Corresponding author

Correspondence to Zhiwen Yu .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. University of Ulsan , Ulsan, Korea (Republic of)

    Kang-Hyun Jo

Glossary of Terms/Acronyms

Glossary of Terms/Acronyms

DWT:

Discrete Wavelet Transform

Ictal:

State or event epileptic seizure

Interictal:

State or event between epileptic seizures

SZN:

The dataset that seizure is denoted S, healthy Z and interictal is N

LMBPNN:

Levenberg-Marquadt Back Propagation Neural Network

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© 2016 Springer International Publishing Switzerland

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Alzami, F., Wang, D., Yu, Z., You, J., Wong, HS., Han, G. (2016). Robust Epileptic Seizure Classification. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-42294-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42293-0

  • Online ISBN: 978-3-319-42294-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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