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Articles

Total Variation Based Multi Feature Model for Epilepsy Detection Using Support Vector Machine

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REFERENCES

  • J. SatheeshKumar, S. Arumugaperumal, R. Rajesh, and C. Kesavdas, “On experimenting with functional magnetic resonance imaging on lip movement,” Neuroradiol. J., Vol. 21, pp. 23–30, Feb. 2008.
  • S. S. Spencer, “The relative contributions of MRI, SPECT and PET imaging in epilepsy,” Epilepsia, Vol. 36, no. 6, pp. 72–89, Dec. 1994.
  • J. Robert, M. S. Koester, and D. E. Stooksbury, “Alzheimers research paper behavioral profile of possible Alzheimers disease patients in Virginia search and rescue incidents,” Wilderness Environ. Med., Vol. 6, pp. 34–43, Feb. 1995.
  • J. S. Duncan, J. W. Sander, S. M. Sisodiya, and M. C. Walker, “Adult epilepsy,” Lancet, Vol. 367, no. 9516, pp. 1087–1100, Apr. 2006.
  • S. A. Russ, K. Larson, and N. Halfon, “A national profile of childhood epilepsy and seizure disorder,” Pediatrics, Vol. 129, no. 2, pp. 256–264, Feb. 2012.
  • A. Liu, J. S. Hahn, G. P. Heldt, and R. W. Coen, “Detection of neonatal seizures through computerized EEG analysis,” Electroenceph. Clin. Neurophysiol., Vol. 82, no. 1, pp. 30–7, Jan. 1992.
  • J. Gotman, D. Flanagah, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the new born: Methods and initial evaluation,” Electroenceph. Clin. Neurophysiol., Vol. 103, pp. 356–62, Sept. 1997.
  • H. Adeli, Z. Zhou, and N. Dadmehr, “Analysis of EEG records in an epileptic patient using wavelet transform,” J. Neurosci. Methods, Vol. 123, pp. 69–87, Feb. 2003.
  • N. Kannathal, Min Lim Choo, U. Rajendra Acharya, and P. K. Sadasivan, “Entropies for detection of epilepsy in EEG” Comput. Methods Programs Biomed., Vol. 80, no. 3, pp. 187–94, Oct. 2005.
  • H. Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximation entropy,” Expert Syst. Appl., Vol. 36, no. 2, pp. 2027–36, Mar. 2009.
  • S. Pravin Kumar, N. Sriram, P. G. Benakop, and B. C. Jinaga, “Entropies based detection of epileptic seizures with artificial neural network classifiers,” Expert Syst. Appl., Vol. 37, no. 4, pp. 3284–91, Apr. 2010.
  • N. Nicolaou, and J. Georgiou, “Detection of epileptic encephalogram based permutation entropy and Support Vector Machine,” Expert Syst. Appl., Vol. 39, no. 1, pp. 202–9, Jan. 2012.
  • A. Mirzae, A. Ayatollahi, and A. M. Nasrabadi, “Automated detection of epileptic seizures using mixed methodology: Wavelet-choas-KNN classifier Mutual Information,” Electr. Rev., Vol. 87, pp. 220–23, Apr. 2011.
  • L. Guo, D. Rivero, J. A. Seoane, and A. Pazos, “Classification of EEG signals using relative wavelet energy and artificial neural networks,” in Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, New York, NY, 2009, pp. 177–83.
  • S. Sanei, and J. A. Chambers, EEG Signal Processing. London: Wiley, 2007.
  • E. Niedermeyer, and E. L. Silva, Electroencephalography- Basic Principles, Clinical Applications and Related Fields. Philadelphia, PA: Lippincott Williams Wilkins, 2005.
  • J. SatheeshKumar, and P. Bhuvaneswari, “Analysis of Electroencephalography (EEG) signals and its categorization – a study,” Procedia Eng., Vol. 38, pp. 2525–36, Sept. 2012.
  • P. Bhuvaneswari, and J. Satheesh Kumar, Methods Used for Identifying EEG Signal Artifacts. New Delhi: Elsevier, 2012, pp. 375–79.
  • U. Rajendra Acharyaa, H. Fujitad, V. K. Sudarshana, S. Bhate, and J. E. W. Koha, “Application of entropies for automated diagnosis of epilepsy using EEG signals: A review,” Knowl.-Based Syst., Vol. 88, pp. 85–96, Nov. 2015.
  • P. Bhuvaneswari, and J. Satheesh Kumar, “Feature extraction methods used in EEG signal analysis,” Int. J. Res. Comput. Sci. Inf. Technol., Vol. 2, no. 2(A), pp. 12–19, Mar. 2014.
  • C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., Vol. 27, pp. 379–423, Jul. 1948.
  • R. Ferents, and A. Anier, “Comparison of entropy and complexity measures for the assessment of depth of sedation,” IEEE Trans. Biomed. Eng., Vol. 53, no. 6, pp. 1067–70, Jun. 2006.
  • S. P. Patil, N. R. Phadnis, and S. A. Patil, “Power spectrum analysis,” IETE Tech. Rev., Vol. 17, no. 3, pp. 119–21, Mar. 2000.
  • M. Picnus, “Approximation entropy as a measure of system complexity,” Proc. Natl. Acad. Sci., Vol. 88, no. 6, pp. 2297–2301, 1991.
  • R. Alcaraz, and J. J. Rieta, “Bidomain sample entropy to predict termination of atrial arrhythmias,” IEEE Int. Symp. Intell. Signal Process., Vol. 5, pp. 188–200, Oct. 2007.
  • J. R. Huang, S. Z. Fan, M. F. Abbod, K. K. Jen, J. F. Wu, and J. S. Shieh, “Application of multivariate empirical mode decomposition and sample entropy in EEG signals via artificial neural networks for interpreting depth of anesthesia,” Entropy, Vol. 15, no. 9, pp. 3325–39, Aug. 2013.
  • C. Bandt, and B. Pompe, “Permutation entropy: A natural complexity measure for time series,” Phys. Rev. Lett., Vol. 88, no. 17, pp. 1–4, Apr. 2002.
  • S. Altunaya, Z. Telatarb, and O. Erogulc, “Epileptic EEG detection using the linear prediction error energy,” Expert Syst. Appl., Vol. 37, no. 8, pp. 5661–65, Aug. 2010.
  • V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1999.
  • P. Bhuvaneswari, and J. Satheesh Kumar, “Support vector machine techniques for EEG signal,” Int. J. Comput. Appl., Vol. 63, no. 13, pp. 1–5, Feb. 2013.
  • C. Brian, and L. C. J. Walder, “Support vector machine for business applications,” Bus. Appl. Comput. Intell., Vol. 14, pp. 267–90, Jan. 2006.
  • C. Campbell, “Algorithmic approaches to training support vector machine – a survey,” in Proceedings of ESANN2000, Brussels, 2000, pp. 27–36.
  • L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D, Vol. 60, no. 1, pp. 259–68, Nov. 1992.
  • R. G. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Phys. Rev. E, Vol. 64, pp. 061907-1–8, Nov. 2001.
  • C. A. Lima, A. L. Coelho, and M. Eisencraft, “Tackling EEG signal classification with least squares support vector machines: A sensitivity analysis study,” Comput. Biol. Med., Vol. 40(8, pp. 705–14, Aug. 2010.
  • L. Guo, D. Rivero, J. Dorado, C. R. Munteanu, and A. Pazos, “Automatic feature extraction using genetic programming: An application to epileptic EEG classification,” Expert Syst. Appl., Vol. 38(8, pp. 10425–36, Aug. 2011.
  • D. Wang, D. Miao, and C. Xie, “Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection,” Expert Syst. Appl., Vol. 38, no. 11, pp. 14314–20, Oct. 2011.
  • Z. Iscan, Z. Dokur, and D. Tamer, “Classification of electroencephalogram signals with combined time and frequency features,” Expert Syst. Appl., Vol. 38, no. 8, pp. 10499–10505, Aug. 2011.
  • U. Orhan, M. Hekim, and M. Ozer, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model,” Expert Syst. Appl., Vol. 38, no. 10, pp. 13475–81, Sept. 2011.
  • U. R. Acharya, S. VinithaSree, A. P. C. Alvin, and J. S. Suri, “Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals,” Int. J. Neural Syst., Vol. 22, no. 2, 1250002, Apr. 2012.
  • S. Ghosh-Dastidar, and H. Adeli, “Improved spiking neural networks for EEG classification and epilepsy and seizure detection,” Integr. Comput.-Aided Eng., Vol. 14, no. 3, pp. 187–212, Aug. 2007.
  • N. F. Guler, E. D. Ubey, and I. Guler, “Recurrent neural network employing Lyapunov exponents for EEG signals classification,” Expert Syst. Appl., Vol. 29, no. 3, pp. 506–14, Oct. 2005.

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