A Survey on Epilepsy Detection and Classifications Using Automated Approaches

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    This paper discusses various methods for the automatic detection and classification of focal and non-focal EEG signals for the detection of Epilepsy disease. The feature extraction and classification methods which were used in many conventional Epilepsy classification methods are discussed in this paper. The machine learning algorithms requires number of input features from the images for improving the classification rate. The deep learning algorithms do not require any extracted features from the source EEG signals. This classification algorithm takes the signal as input features and produces the classification result. The classification rates of these deep learning algorithms are high due to its stability with input sources.

     


  • Keywords


    Epilepsy, Features, Classifications, Deep learning, Machine learning.

  • References


      [1] Jessica J.Falco, WalteraIngrid, E.SchefferbRobert , S.Fisher, “The new definition and classification of seizures and epilepsy”, Epilepsy Research, Volume 139, January 2018, Pages 73-79.

      [2] Y. Li, X. Wang, M. Luo, K. Li, X. Yang and Q. Guo, "Epileptic Seizure Classification of EEGs Using Time–Frequency Analysis Based Multiscale Radial Basis Functions," in IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 2, pp. 386-397, March 2018.

      [3] S. Priyanka, D. Dema and T. Jayanthi, "Feature selection and classification of Epilepsy from EEG signal," 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 2017, pp. 2404-2406.

      [4] Gupta, P. Singh and M. Karlekar, "A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals," in IEEE Transactions on Neural Systems and. Rehabilitation Engineering, vol. 26, no. 5, pp. 925-935, May 2018

      [5] L. D. Iasemidis, "Epileptic seizure prediction and control", IEEE Trans. Biomed. Eng., vol. 50, no. 5, pp. 549-558, May 2003.

      [6] J. Corsini, L. Shoker, S. Sanei, G. Alarcon, "Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation", IEEE Trans. Biomed. Eng., vol. 53, no. 5, pp. 790-799, May 2006.

      [7] M. Li, W. Chen, T. Zhang, "Automatic epileptic EEG detection using DT-CWT-based non-linear features", Biomed. Signal Process. Control, vol. 34, pp. 114-125, Apr. 2017.

      [8] P. Shivnarayan, P. Trilochan, "Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals", Biomed. Signal Process. Control, vol. 34, pp. 74-80, Apr. 2017.

      [9] M. Diykh, Y. Li, P. Wen, "EEG sleep stages classification based on time domain features and structural graph similarity", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 11, pp. 1159-1168, Nov. 2016.

      D. G. Manolakis, V. K. Ingle, S. M. Kogon, Statistical and Adaptive Signal Processing: Spectral Estimation Signal Modeling Adaptive Filtering and Array Processing, Boston, MA, USA:McGraw-Hill, 2000.

 

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Article ID: 28679
 
DOI: 10.14419/ijet.v7i4.6.28679




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