AUTOMATED PATIENT-SPECIFIC SEIZURE DETECTION SYSTEM WITH SELF-PARAMETERS ADAPTATION

Sabrina Ammar, Omar Trigui, and Mohamed Senouci

References

  1. [1] F. Ben Taher, N. Ben Amor, and M. Jallouli, A multisourceselectric wheelchair control based on EEG signals and fuzzy eyetracking fusion, Control and Intelligent Systems, 44(2), 2016,67–74.
  2. [2] H.L. Hamburger, A battery approach to clinical utilisationof topographic brain mapping, in K. Maurer (ed.), Topographic brain mapping of EEG and evoked potentials, (Berlin-Heidelberg, New York: Springer-Verlag, 1989), 167–184.
  3. [3] L. Orosco, A.G. Correa, P. Diez, and E. Laciar, Patientnon-specific algorithm for seizures detection in scalp EEG,Computers in Biology and Medicine, 71, 2016, 128–134.
  4. [4] Y.U. Khan, N. Rafiuddin, and O. Farooq, Automated seizuredetection in scalp EEG using multiple wavelet scales, Int. Conf.on Signal Processing, Computing and Control, WaknaghatSolan, India, 2012, 5.
  5. [5] L. Orosco, A.G. Correa, and E. Laciar, Multiparametric detection of epileptic seizures using empirical mode decompositionof EEG records, 32nd Annual Int. Conf. IEEE EMBS, BuenosAires, Argentina, August 31–September 4, 2010, 951–954.
  6. [6] R. Sharma and R.B. Pachori, Classification of epileptic seizuresin EEG signals based on phase space representation of intrinsicmode functions, Expert Systems with Applications, 42(2), 2015,1106–1117.
  7. [7] K. Rai, V. Bajaj, and A. Kumar, Features extraction forclassification of focal and non-focal EEG signals, in K.J. Kim(ed.), Information science and applications, Volume 339 of theSeries Lecture Notes in Electrical Engineering, Springer-VerlagBerlin Heidelberg, 18 February 2015, 599–605.
  8. [8] O. Trigui, W. Zouch, and M. Ben Messaoud, Anti-noise capa-bility improvement of minimum energy combination methodfor SSVEP detection, International Journal of Advanced Computer Science and Applications, 7(9), 2016, 393–401.
  9. [9] S. Kiranyaz, T. Ince, M. Zabihi, and D. Ince, Automatedpatient-specific classification of long-term Electroencephalography, Journal of Biomedical Informatics, 49, 2015, 16–31.
  10. [10] M.T. Kostas, S. Konitsiotis, S. Markoula, D.D. Koutsouris,I.A. Sakellarios, and D.I. Fotiadis, An unsupervised methodology for the detection of epileptic seizures in long-term EEGsignals, Int. Conf. on Bioinformatics and Bioengineering,Belgrade, Serbia, 2015, 4.
  11. [11] P. Fergus, D. Hignett, A. Hussain, D. Al-Jumeily, and K. Abdel-Aziz, Automatic epileptic seizure detection using scalp EEGand advanced artificial intelligence, techniques, Hindawi Pub-lishing Corporation BioMed Research International, 2015,2015, 17 (Article ID 986736).
  12. [12] A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorf,P. ChIvanov, R.G. Mark, et al., PhysioBank, PhysioToolkit,and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation, 101(23), 2000, e215–e220[Circulation Electronic Pages: http://circ.ahajournals.org/cgi/con)ent/full/101/23/e215]. Available from: http://physionet.org/physiobank/database/ chbmit/.
  13. [13] S. Chia-Ping, W. Zhou, L. Feng-Seng, S. Hsiao-Ya, L. Yan-Yu,W. Chen, et al. Epilepsy analytic system with cloud computing,Int. Conf. on IEEE EMBS, Osaka, Japan, 3–7 July, 2013,1644–1647.
  14. [14] N.E. Huang and S.S.P. Shen, Hilbert-Huang transform and itsapplications, Interdisciplinary Mathematical Sciences, 5, 2005,2–14.
  15. [15] O. Trigui, W. Zouch, and M. Ben Messaoud, Brain-computerinterface: frequency domain approach using the linear andthe quadratic discriminant analysis, Int. Conf. on AdvancedTechnologies for Signal and Image Processing, Sousse, Tunisia,2014, 346–349.
  16. [16] A.W. Wu, Y. Mallet, B. Walczak, W. Penninckx, D.L. Massart,S. Heuerding, et al., Comparison of regularized discriminantanalysis linear discriminant analysis and quadratic discriminantanalysis applied to NIR data, Analytica Chimica Acta, 329,1996, 257–265.
  17. [17] R. Corralejo, R. Hornero, and D. ´Alvarez, Feature selectionusing a genetic algorithm in a motor imagery based braincomputer interface, Int. Conf. IEEE EMBS, Boston, MA,USA, August 30–September 3, 2011, 7703–7706.
  18. [18] A. Choucha, A. Hellal, L. Mokrani, and S. Arif, New approach to the optimization of power system stabilizers: Geneticalgorithm with dynamic constraints, Control and IntelligentSystems, 40(3), 2012 129–143.
  19. [19] H. Ocak, Optimal classification of epileptic seizures in EEGusing wavelet analysis and genetic algorithm, Signal Processing,88, 2008, 1858–1867.
  20. [20] J. Yang, H. Singh, E.L. Hines, F. Schlaghecken, D.D. Iliescu,M.S. Leeson, et al., Channel selection and classification ofelectroencephalogram signals: An artificial neural network andgenetic algorithm-based approach, Artificial Intelligence inMedicine, 55, 2012, 117–126.
  21. [21] K.H. Cheng and S. YNien, Detection of seizures in EEGusing subband nonlinear parameters and genetic algorithm,Computers in Biology and Medicine, 40, 2010, 823–830.
  22. [22] R. Izabela, Genetic algorithms for feature selection for brain–computer interface, International Journal of Pattern Recognition and Artificial Intelligence, 29(5), 2015, 27.
  23. [23] R. Bose, A. Khasnobish, S. Bhaduri, and D.N. Tibarewala,Performance analysis of left and right lower limb movementclassification from EEG, Int. Conf. on Signal Processing andIntegrated Networks, Noida, India, 2016, 174–179.
  24. [24] E. Kabir, Siuly, and Y. Zhang, Epileptic seizure detection fromEEG signals using logistic model trees, Brain Informatics,3(2), 2016, 93–100.
  25. [25] M. Mera-Gaona, R. Vargas-Canas, and D.M. Lopez, Towards aselection mechanism of relevant features for automatic epilepticseizures detection, Studies in Health Technology and Informatics. Exploring Complexity in Health: An InterdisciplinarySystems Approach, 228, 2016, 722–726.

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