Empirical mode decomposition based classification of focal and non-focal seizure EEG signals

Proceedings - 2014 International Conference on Medical Biometrics, ICMB 2014

Volumn , Issue , 2014, Pages 135-140

  Sharma, Rajeev ^a   Pachori, Ram Bilas ^a   Gautam, Shreya ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY INDORE   (India)

^b JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ENTROPY; IMAGE SEGMENTATION; RADIAL BASIS FUNCTION NETWORKS; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; ELECTROENCEPHALOGRAM SIGNALS; EMPIRICAL MODE DECOMPOSITION; EPILEPTIC SEIZURES; INSTANTANEOUS FREQUENCY; INTRINSIC MODE FUNCTIONS; LEAST SQUARE SUPPORT VECTOR MACHINES; RADIAL BASIS FUNCTION(RBF);

ELECTROENCEPHALOGRAPHY;

EID: 84904640580     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMB.2014.31     Document Type: Conference Paper

References (35)

1. S. Pati and A.V. Alexopoulos, "Pharmacoresistant epilepsy: From pathogenesis to current and emerging therapies," Cleveland Clinic Journal of Medicine, vol. 77, no. 7, pp. 457-467, 2010.
2. R.G. Andrzejak, K. Schindler, and C. Rummel, "Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients," Phys. Rev. E, vol. 86, 046206, 2012.
3. S. Mukhopadhyay and G.C. Ray, "A new interpretation of nonlinear energy operator and its efficacy in spike detection," IEEE Transactions on Biomedical Engineering, vol. 45, no. 2, pp. 180-187, 1998.
4. L.D. Iasemidis, et al., "Adaptive epileptic seizure prediction system," IEEE Transactions on Biomedical Engineering, vol. 50, no. 5, pp. 616-627, 2003.
5. V. Srinivasan, C. Eswaran, and N. Sriraam, "Artificial neural network based epileptic detection using time-domain and frequency-domain features," Journal of Medical Systems, vol. 29, pp. 647-660, 2005.
6. K. Polat and S. Gunes, "Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform," Applied Mathematics and Computation, vol. 187, no. 2, pp. 1017-1026, 2007.
7. S. Altunay, Z. Telatar, and O. Erogul, "Epileptic EEG detection using the linear prediction error energy," Expert Systems with Applications, vol. 37, no. 8, pp. 5661-5665, 2010.
8. V. Joshi, R.B. Pachori, and A. Vijesh, "Classification of ictal and seizurefree EEG signals using fractional linear prediction," Biomedical Signal Processing and Control, vol. 9, pp. 1-5, 2014.
9. A.T. Tzallas, M.G. Tsipouras, and D.I. Fotiadis, "Automatic seizure detection based on time-frequency analysis and artificial neural networks," Computational Intelligence and Neuroscience, vol. 2007, article ID 80510, 18 pages, 2007.
10. A.T. Tzallas, M.G. Tsipouras, and D.I. Fotiadis, "Epileptic seizure detection in EEGs using time-frequency analysis," IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 5, pp. 703-710, 2009.
11. H. Adeli, Z. Zhou, and N. Dadmehr, "Analysis of EEG records in an epileptic patient using wavelet transform," Journal of Neuroscience Methods, vol. 123, no. 1, pp. 69-87, 2003.
12. Y.U. Khan and J. Gotman, "Wavelet based automatic seizure detection in intracerebral electroencephalogram," Clinical Neurophysiology, vol. 114, no. 5, pp. 898-908, 2003.
13. S.G. Dastidar, H. Adeli, and N. Dadmehr, "Mixed-band wavelet-chaosneural network methodology for epilepsy and epileptic seizure detection," IEEE Transactions on Biomedical Engineering, vol. 54, no. 9, pp. 1545-1551, 2007.
14. H. Ocak, "Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy," Expert Systems with Applications, vol. 36, no. 2, pp. 2027-2036, 2009
15. H. Adeli, S.G. Dastidar, and N. Dadmehr, "A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy," IEEE Transactions on Biomedical Engineering, vol. 54, no. 2, pp. 205-211, 2007.
16. A. Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model," Expert Systems with Applications, vol. 32, no. 4, pp. 1084-1093, 2007.
17. R. Uthayakumar and D. Easwaramoorthy, "Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform," Fractals, vol. 21, no. 2, 2013.
18. L. Guo, D. Rivero, and A. Pazos, "Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks," Journal of Neuroscience Methods, vol. 193, no. 1, pp. 156-163, 2010.
19. N.E. Huang et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903-995, 1998.
20. R.B. Pachori, "Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition," Research Letters in Signal Processing, vol. 2008, article ID 293056, 5 pages, 2008.
21. R.J. Oweis and E.W. Abdulhay, "Seizure classification in EEG signals utilizing HilbertHuang transform," BioMedical Engineering OnLine, vol. 10, no. 38, 2011.
22. R.B. Pachori and V. Bajaj, "Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition," Computer Methods and Programs in Biomedicine, vol. 104, no. 3, pp. 373-381, 2011.
23. V. Bajaj and R.B. Pachori, "EEG signal classification using empirical mode decomposition and support vector machine," in: Proceedings International Conference on Soft Computing for Problem Solving, 20-22 December, 2011, Roorkee, India, 2011, pp. 623-635.
24. S. Li et al., "Feature extraction and recognition of ictal EEG using EMD and SVM," Computers in Biology and Medicine, vol. 43, no. 7, pp. 807-816, 2013.
25. V. Bajaj and R.B. Pachori, "Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals," Biomedical Engineering Letters, vol. 3, no. 1, pp 17-21, 2013.
26. V. Bajaj and R.B. Pachori, "Classification of seizure and nonseizure EEG signals using empirical mode decomposition," IEEE transactions on Information Technology in Biomedicine, vol. 16, no. 6, pp. 1135-1142, 2012.
27. Y. Xia, and D.P. Mandic, "Application of multivaritate empirical mode decomposition for seizure detection in EEG signals," in: Proceedings Annual International Conference of IEEE Engineering in Medicine and Biology Society, August 31, 2010-September 04, 2010, Buenos Aires, Argentina, 2010, pp. 1650-1653.
28. R.B. Pachori and S. Patidar, "Epileptic seizure classification in EEG signal using second-order difference plot of intrinsic mode function," Computer Methods and programs in Biomedicine, vol. 113, issue 2, pp. 494-502, 2014.
29. J.S. Richman and J.R. Moorman, "Physiological time-series analysis using approximate entropy and sample entropy," American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039-H2049, 2000.
30. Y. Song and P. Lio, "A new approach for epileptic seizure detection: Sample entropy based feature extraction and extreme learning machine," Journal of Biomedical Science and Engineering, vol. 3, no. 5, pp. 556-567, 2010.
31. V. Bajaj and R.B. Pachori, "Application of the sample entropy for discrimination between seizure and seizure-free EEG Signals," in: Indian International Conference on Artificial Intelligence, 2011, pp. 1232-1247.
32. C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, no. 3, 1995, pp. 273-297.
33. J.A.K. Sukens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Process. Lett., vol. 9, no. 3, pp. 293-300, 1999.
34. A.H. Khandoker, D.T.H. Lai, R.K. Begg, and M. Palaniswami, "Wavelet-based feature extraction for support vector machines for screening balance impairments in the elderly," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 15, no. 4, pp. 587-597, 2007.
35. G. Zhu, Y. Li, P. Paul Wen, S. Wang, and M. Xi, "Epileptogenic focus detection in intracranial EEG based on delay permutation entropy," in: Conference Proceedings, American Institute of Physics, 2013, vol. 1559, pp. 31-36.