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EEG Signal Classification for Epileptogenic Zone and Seizure Zone

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Proceedings of the International Conference on Intelligent Systems and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 671))

Abstract

ElectroEncephaloGram (EEG) signals play an important role to identify epileptic disorders. Epilepsy is a neurological disorder that is an unexpected electrical disruption of the brain, because the activity of nerve cells in the brain becomes disrupted, causing people to experience “seizures.” Nowa-day, researcher works and focuses on automatic analysis of EEG signals to classify epilepsy. The EEG signal recording system produces very long data. Thus, the classification of epileptic seizures requires a time-consuming process. This paper proposes a Support Vector Machine (SVM)-based automated seizure classification system using Approximation Entropy (ApEn). ApEn reduces patient data size without loss of information. ApEn is a statistical parameter that measures the amplitude value of an EEG signal current based on its previous amplitude value. In this paper, we measure sensitivity, specificity, and accuracy using SVM classifiers. The overall score as high as 98.62% can be achieved by using the proposed system to distinguish the epilepsy state (seizure class) from the normal state (non-seizure class) using the time domain method.

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References

  1. Klaus Lehnertz, Florian Mormann, Thomas Kreuz, Ralph G. Andrzejak, Christoph Rieke, Peter David, And Christian E. Elger, “Seizure prediction by nonlinear EEG analysis. IEE Eng Med BiolMag”, article in IEEE engineering in medicine and biology magazine, Research gate January 2003.

    Google Scholar 

  2. N. Mc Grogan (1999). Neural network detection of epileptic seizures in the electroencephalogram, [Online]. Available: http://www.new.ox.ac.uk/˜nmcgroga/work/transfer

  3. Vairavan Srinivasan, “Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks” IEEE Transactions On Information Technology In Biomedicine, Vol. 11, No. 3, May 2007.

    Google Scholar 

  4. N.F. Güler, E.D. Übeyli, I. Güler, Recurrent neural networks employing Lyapunov exponents for EEG signal classification, Expert Systems with Applications 29 (3) (2005) 506–514, October 5. Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology of the Grid: an Open Grid Services Architecture for Distributed Systems Integration. Technical report, Global Grid Forum (2002).

    Google Scholar 

  5. E.D. Übeyli, Lyapunov exponents/probabilistic neural networks for analysis of EEG signals, Expert Systems with Applications 37 (2) (2010), pp. 985–992, March.

    Google Scholar 

  6. A. Accardo, M. Affinito, M. Carrozzi, F. Bouquet, Use of the fractal dimension for the analysis of electroencephalographic time series, Biological Cybernetics 77 (5) (1997),pp. 339–350, November 2.

    Google Scholar 

  7. S.F. Liang, H.C. Wang, W.L. Chang, Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection, EURASIP Journal on Advances in Signal Processing (2010), vol. 2010, Article ID 853434.

    Google Scholar 

  8. R.B. Pachori, Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition, Research Letters in Signal Processing 2008 (2008), Article ID 293056.

    Google Scholar 

  9. S. Li, et al., Feature extraction and recognition of ictal EEG using EMD and SVM, Computers in Biology and Medicine 43(7) (2013), pp. 807–816.

    Google Scholar 

  10. R.J. Oweis, E.W. Abdulhay, Seizure classification in EEG signals utilizing Hilbert–Huang transform, Bio Medical Engineering On Line 10 (2011) 38, December.

    Google Scholar 

  11. R.B. Pachori, V. Bajaj, Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition, Computer Methods and Programs in Biomedicine 104 (3) (2011), pp. 373–381, December 7.

    Google Scholar 

  12. V. Bajaj, R.B. Pachori, EEG signal classification using empirical mode decomposition and support vector machine, in: Proceedings International Conference on Soft Computing for Problem Solving, AISC 131, 20–22 December, 2011, Roorkee, India, 2011, pp. 623–635.

    Google Scholar 

  13. Ram Bilas Pachori, Shivnarayan Patidar, “Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode function,” in Elsevier Transactions on Computer methods and programming in biometric(2014), vol. 9, pp. 494–502.

    Google Scholar 

  14. Varun Bajaj and Ram Bilas Pachori, “Classification of Seizure and Non seizure EEG Signals Using Empirical Mode Decomposition”, IEEE transactions on information technology in biomedicine (2012), vol. 16, no. 6, November.

    Google Scholar 

  15. Nicoletta Nicolaou, Julius Georgiou, “Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines,” in Elsevier Transactions on Expert Systems with Applications (2012), vol. 8, pp. 202–209.

    Google Scholar 

  16. R.G. Andrzejak, et al., Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state, Physical Review E (2001), vol. 64, Article ID 061907.

    Google Scholar 

  17. Guohun Zhu, Yan Li, Peng (Paul) Wen, “Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm,” in Elsevier Transactions on Computer method in programming biometric(2014), vol. 12, pp. 64–75.

    Google Scholar 

  18. Siulya, Yan Li a, Peng (Paul) Wenb, “Clustering technique-based least square support vector machine for EEG signal classification,” in Elsevier Transactions on Computer method in programming biometric(2011), vol. 15, pp. 358–372.

    Google Scholar 

  19. Giorgos Giannakakis, Vangelis Sakkalis, Matthew Pediaditis, And Manolis Tsiknakis “Methods For Seizure Detection And Prediction: An Overview” Springer Science+business Media New York 2014.

    Google Scholar 

  20. A.S. Muthanantha Murugavel And S. Ramakrishnan “Multi-class SVM For EEG Signal Classification Using Wavelet Based Approximate Entropy” Institute For Computer Sciences, Social Informatics And Telecommunications Engineering 2012.

    Google Scholar 

  21. U. Rajendra Acharya “Automatic Detection Of Epileptic Eeg Signals Using Higher Order Cumulant Features” International Journal Of Neural Systems, Vol. 21, No. 5 (2011).

    Google Scholar 

  22. Ling Guo, Daniel Rivero, Alejandro Pazos “Epileptic Seizure Detection Using Multiwavelet Transform Based Approximate Entropy And Artificial Neural Networks” Journal Of Neuroscience Methods 193 (2010) 156–163.

    Google Scholar 

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Authors and Affiliations

  1. Dr. R.K. Desai Marg, Opp. Mission Hospital, Athwalines, Surat, Gujarat, India

    Hardika B. Gabani & Chirag N. Paunwala

Authors
  1. Hardika B. Gabani
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  2. Chirag N. Paunwala
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Corresponding author

Correspondence to Hardika B. Gabani .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar, Gujarat, India

    Rahul Kher

  2. Department of Information Technology, G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar, Gujarat, India

    Dr. Nikhil Gondaliya

  3. Department of Electrical Engineering, G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar, Gujarat, India

    Mukesh Bhesaniya

  4. SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Latif Ladid

  5. University of Oklahoma, Norman, Oklahoma, USA

    Mohammed Atiquzzaman

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Gabani, H.B., Paunwala, C.N. (2018). EEG Signal Classification for Epileptogenic Zone and Seizure Zone. In: Kher, R., Gondaliya, D., Bhesaniya, M., Ladid, L., Atiquzzaman, M. (eds) Proceedings of the International Conference on Intelligent Systems and Signal Processing . Advances in Intelligent Systems and Computing, vol 671. Springer, Singapore. https://doi.org/10.1007/978-981-10-6977-2_5

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  • DOI: https://doi.org/10.1007/978-981-10-6977-2_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6976-5

  • Online ISBN: 978-981-10-6977-2

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