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Classification and Analysis of EEG Using SVM and MRE

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Computational Intelligence Techniques in Diagnosis of Brain Diseases

Abstract

Electroencephalogram (EEG) is the brain signal processing system that tolerates gaining the appreciation of the multipart internal mechanisms of the brain and irregular brain waves are exposed to be associated through exact brain syndromes.

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References

  1. Wang J, Du EY, Chang CI (2002) Relative entropy–based methods for image thresholding. In: Proceedings of IEEE international conference on image processing, pp II 265–II 268

    Google Scholar 

  2. Rabbi AF, Fazel-Rezai R (2012) Fuzzy logic system for seizure onset detection in intracranial EEG pp 1–7

    Google Scholar 

  3. Joel J (2004) Detection of seizure precursors from depth eeg using a sign periodogram transform. IEEE-Trans Biomed Eng 51(4):449–458

    Google Scholar 

  4. De Clereq W et al (2003) Characterization of interictal and ictal scalp EEG signals with the Hilbert Transform. In: Proceedings of the 25th annual international conference of the IEEE EMBS, Cancun 17–21 Sep, pp 2459–2462

    Google Scholar 

  5. Zumsteg D, Wieser HG (2000) Presurgical evaluation: current role of invasive EEG. Epilepsia 41(3):55–60

    Google Scholar 

  6. Adlassnig KP (1986) Fuzzy set theory in medical diagnosis. IEEE-Trans Sys Man Cybern 16:260–265

    Google Scholar 

  7. Dingle AA et al (1993) A multistage system to detect epileptic form activity in the EEG. IEEE Trans Biomed Eng 40(12):1260–1268

    Google Scholar 

  8. Haoq, Gotman J (1997) A patient specific algorithm for detection onset in long-term EEG monitoring-possible use as warning device. IEEE-Trans Biomed Eng 44(2):115–122

    Google Scholar 

  9. Harikumar R, Narayanan BS (2003) Fuzzy techniques for classification of epilepsy risk level from EEG Signals. In: Proceedings of IEEE-Tencon–2003, Bangalore, 14–17 Oct, pp 209–213

    Google Scholar 

  10. Aexei A (2004) Algorithms for estimating information distance with application to bioinformatics and linguistics In: Proceedings of IEEE, CCECE. pp 47–57

    Google Scholar 

  11. Sathish Kumar (2004) Neural networks, a classroom approach. McGraw-Hill, New York, pp 1–10

    Google Scholar 

  12. Vapnik V (1998) Statistical learning theory. Wiely Chichester, pp 66–73

    Google Scholar 

  13. Shah J, Salim bt N (2006) Neural networks and support vector machines based bio-activity classification. In: Proceedings of the 1st conference on natural resources engineering & technology 2006, Putra Jaya, July 24–25, pp 484–491

    Google Scholar 

  14. Song Q, Hu W, Xie W (2002) Robust support vector machine with bullet hole image classification. IEEE-Trans SMC Part C 32(4):440–448

    Google Scholar 

  15. Yager RR (2000) Hierarchical aggregation functions generated from belief structures. IEEE Trans Fuzzy Sys (8)5:481–490

    Google Scholar 

  16. Gupta CB, Vijay Gupta (2001) An introduction to statistical methods, 22nd edn. Vikas Publishing House Lt, New Delhi, pp 78–83

    Google Scholar 

  17. Su MC, Chou CH (2001) A modified version of the k-means clustering algorithm with a distance based on cluster symmetry. IEEE-Trans on Pattern Anal Mach Intell 23(6): 674–680

    Google Scholar 

  18. Duda RO, Stroke DG, Peter E (2003) Hart-pattern classification, 2nd edn. A Wiley-Interscience Publication, New Jersey, pp 44–55

    Google Scholar 

  19. Harikumar R, Sukanesh R, Bharathi PA (2005) Genetic algorithm optimization of fuzzy outputs for classification of epilepsy risk levels from EEG signals I. E. India J Interdiscip Panels 86(1)

    Google Scholar 

Download references

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Correspondence to Sasikumar Gurumoorthy .

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Gurumoorthy, S., Muppalaneni, N.B., Gao, XZ. (2018). Classification and Analysis of EEG Using SVM and MRE. In: Computational Intelligence Techniques in Diagnosis of Brain Diseases. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-10-6529-3_3

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  • DOI: https://doi.org/10.1007/978-981-10-6529-3_3

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  • Print ISBN: 978-981-10-6528-6

  • Online ISBN: 978-981-10-6529-3

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