Table of Contents
ISRN Biomedical Engineering
Volume 2013 (2013), Article ID 358108, 14 pages
http://dx.doi.org/10.1155/2013/358108
Research Article

A New Approach to Detect Epileptic Seizures in Electroencephalograms Using Teager Energy

Electronics and Communication Department, Manipal Institute of Technology, Manipal 576104, India

Received 6 April 2013; Accepted 4 May 2013

Academic Editors: L. Faes and R. Grebe

Copyright © 2013 Chandrakar Kamath. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. D. Iasemidis, D.-S. Shiau, W. Chaovalitwongse et al., “Adaptive epileptic seizure prediction system,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 5, pp. 616–627, 2003. View at Google Scholar · View at Scopus
  2. F. Lopes da Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski, and D. N. Velis, “Epilepsies as dynamical diseases of brain systems: basic models of the transition between normal and epileptic activity,” Epilepsia, vol. 44, no. 12, pp. 72–83, 2003. View at Google Scholar · View at Scopus
  3. N. Mc Grogan, Neural network detection of epileptic seizures in the electroencephalogram [Ph.D. thesis], Oxford University, Oxford, UK, 1999.
  4. F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lehnertz, “Seizure prediction: the long and winding road,” Brain, vol. 130, no. 2, pp. 314–333, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Gotman, “Automatic detection of seizures and spikes,” Journal of Clinical Neurophysiology, vol. 16, no. 2, pp. 130–140, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Gotman, “Automatic recognition of epileptic seizures in the EEG,” Electroencephalography and Clinical Neurophysiology, vol. 54, no. 5, pp. 530–540, 1982. View at Publisher · View at Google Scholar · View at Scopus
  7. A. M. Murro, D. W. King, J. R. Smith, B. B. Gallagher, H. F. Flanigin, and K. Meador, “Computerized seizure detection of complex partial seizures,” Electroencephalography and Clinical Neurophysiology, vol. 79, no. 4, pp. 330–333, 1991. View at Google Scholar · View at Scopus
  8. H. Qu and J. Gotham, “A patient-specific algorithm for the detection of seizure onset in long- term EEG monitoring: possible use as a warning device,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 2, pp. 115–122, 1997. View at Publisher · View at Google Scholar · View at Scopus
  9. N. Radhakrishnan and B. N. Gangadhar, “Estimating regularity in epileptic seizure time-series data: a complexity-measure approach,” IEEE Engineering in Medicine and Biology Magazine, vol. 17, no. 3, pp. 89–94, 1998. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Hu, J. Gao, and J. Principe, “Analysis of biomedical signals by the Lempel-Ziv complexity: the effect on finite data size,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2606–2609, 2006. View at Google Scholar
  11. L. D. Iasemidis, H. P. Zaveri, J. C. Sackellares, W. J. Williams, and T. W. Hood, “Nonlinear dynamics of electrocorticographic data,” Journal of Clinical Neurophysiology, vol. 5, p. 339, 1988. View at Google Scholar
  12. L. D. Iasemidis, J. C. Sackellares, H. P. Zaveri, and W. J. Williams, “Phase space topography and the lyapunov exponent of electrocorticograms in partial seizures,” Brain Topography, vol. 2, no. 3, pp. 187–201, 1990. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Gigola, F. Ortiz, C. E. D'Attellis, W. Silva, and S. Kochen, “Prediction of epileptic seizures using accumulated energy in a multiresolution framework,” Journal of Neuroscience Methods, vol. 138, no. 1-2, pp. 107–111, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. O. A. Rosso, S. Blanco, J. Yordanova et al., “Wavelet entropy: a new tool for analysis of short duration brain electrical signals,” Journal of Neuroscience Methods, vol. 105, no. 1, pp. 65–75, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Li, G. Ouyang, and D. A. Richards, “Predictability analysis of absence seizures with permutation entropy,” Epilepsy Research, vol. 77, no. 1, pp. 70–74, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. O. A. Rosso, “Entropy changes in brain function,” International Journal of Psychophysiology, vol. 64, no. 1, pp. 75–80, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. V. Srinivasan, C. Eswaran, and N. Sriraam, “Approximate entropy-based epileptic EEG detection using artificial neural networks,” IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 3, pp. 288–295, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Gao, C. Yinhe, W. Ten, and J. Hu, Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond, John Wiley & Sons, Hoboken, NJ, USA, 2007.
  19. J. Gao, J. Hu, and W. Tung, “Complexity measures of brain wave dynamics,” Cognitive Neurodynamics, vol. 5, pp. 171–182, 2011. View at Google Scholar
  20. E. I. Plotkin and M. N. S. Swamy, “Multistage implementation of parameterinvariant null filter and its application to discrimination of closely spaced sinusoids,” in Proceedings of the IEEE International Symposium on Circuits and Systems, vol. 1, pp. 767–770, 1988.
  21. E. I. Plotkin and M. N. S. Swamy, “Parameter-free structural modeling: a contribution to the solution of the separation of highly correlated AR-signals,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '98), pp. 1–4, June 1998. View at Scopus
  22. E. I. Plotkin and M. N. S. Swamy, “Signal processing based on parameter structural modeling and separation of highly correlated signals of known structure,” Circuits, Systems, and Signal Processing, vol. 17, no. 1, pp. 51–68, 1998. View at Google Scholar · View at Scopus
  23. B. Litt, R. Esteller, J. Echauz et al., “Epileptic seizures may begin hours in advance of clinical onset: a report of five patients,” Neuron, vol. 30, no. 1, pp. 51–64, 2001. View at Publisher · View at Google Scholar · View at Scopus
  24. T. F. Quatieri, Discrete-Time Speech Signal Processing, Principles and Practice, Pearson Education Pte., Singapore, 2004.
  25. J. F. Kaiser, “On a simple algorithm to calculate the “energy” of a signal,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 381–384, April 1990. View at Scopus
  26. T. L. Nwe, S. W. Foo, and L. C. De Silva, “Classification of stress in speech using linear and nonlinear features,” in Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing, pp. 9–12, April 2003. View at Scopus
  27. L. Atlas and J. Fang, “Quadratic detectors for general nonlinear analysis of speech,” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 9–12, 1992. View at Google Scholar
  28. D. A. Cairns and J. H. L. Hansen, “Nonlinear analysis and classification of speech under stressed conditions,” Journal of the Acoustical Society of America, vol. 96, no. 6, pp. 3392–3400, 1994. View at Publisher · View at Google Scholar · View at Scopus
  29. C. Kamath, “A new approach to detect congestive heart failure using Teager energy nonlinear scatter plot of R-R interval series,” Medical Engineering and Physics, vol. 34, no. 7, pp. 841–848, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Kamath, “ECG beat classification using features extracted from Teager energy functions in time and frequency domains,” IET Signal Processing, vol. 5, no. 6, pp. 575–581, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. H. P. Zaveri, W. J. Williams, and J. C. Sackellares, “Energy based detection of seizures,” in Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 363–364, October 1993. View at Scopus
  32. R. Esteller, Detection of seizure onset in epileptic patients from intracranial EEG signals [Ph.D. thesis], Georgia Institute of Technology, Department of Electrical and Computer Engineering, Atlanta, Ga, USA, 2000.
  33. A. B. Gardner, A. M. Krieger, G. Vachtsevanos, and B. Litt, “One-class novelty detection for seizure analysis from intracranial EEG,” Journal of Machine Learning Research, vol. 7, pp. 1025–1044, 2006. View at Google Scholar · View at Scopus
  34. R. Agarwal and J. Gotman, “Adaptive segmentation of electroencephalographic data using a nonlinear energy operator,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '99), pp. V-199–V-202, June 1999. View at Scopus
  35. M. D'Alessandro, R. Esteller, G. Vachtsevanos, A. Hinson, J. Echauz, and B. Litt, “Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 5, pp. 603–615, 2003. View at Google Scholar · View at Scopus
  36. R. Yadav, R. Agarwal, and M. N. S. Swamy, “Detection of epileptic seizures in stereo-EEG using frequency-weighted energy,” in Proceedings of the 50th Midwest Symposium on Circuits and Systems (MWSCAS '07), pp. 77–80, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. W. Klonowski, “Application of new non-linear dynamics methods in biosignal analysis,” in World Medical Conference, vol. 5975 of Proceedings of SPIE, pp. 335–344, 2006.
  38. W. Klonowski, “From conformons to human brains: an informal overview of nonlinear dynamics and its applications in biomedicine,” Nonlinear Biomedical Physics, vol. 1, article 5, 2007. View at Publisher · View at Google Scholar · View at Scopus
  39. W. Klonowski, “Personalized neurological diagnostics from biomedical Physicist’s point of view and application of new non-linear dynamics methods in biosignal analysis,” International Journal of Biology and Biomedical Engineering, vol. 5, no. 4, pp. 190–200, 2011. View at Google Scholar
  40. T. Q. D. Khoa, V. Q. Ha, and V. V. Toi, “Higuchi fractal properties of onset epilepsy electroencephalogram,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 461426, 6 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. S. M. Pincus and A. L. Goldberger, “Physiological time-series analysis: what does regularity quantify?” American Journal of Physiology, vol. 266, no. 4, pp. H1643–H1656, 1994. View at Google Scholar · View at Scopus
  42. J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate and sample entropy,” American Journal of Physiology, vol. 278, no. 6, pp. H2039–H2049, 2000. View at Google Scholar · View at Scopus
  43. S. Ramdani, B. Seigle, J. Lagarde, F. Bouchara, and P. L. Bernard, “On the use of sample entropy to analyze human postural sway data,” Medical Engineering and Physics, vol. 31, no. 8, pp. 1023–1031, 2009. View at Publisher · View at Google Scholar · View at Scopus
  44. R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, 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,” Physical Review E, vol. 64, no. 6, Article ID 061907, 8 pages, 2001. View at Google Scholar · View at Scopus
  45. L. Guo, D. Rivero, J. Dorado, J. R. Rabuñal, and A. Pazos, “Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks,” Journal of Neuroscience Methods, vol. 191, no. 1, pp. 101–109, 2010. View at Publisher · View at Google Scholar · View at Scopus
  46. F. Amor, S. Baillet, V. Navarro, C. Adam, J. Martinerie, and M. Le Van Quyen, “Cortical local and long-range synchronization interplay in human absence seizure initiation,” NeuroImage, vol. 45, no. 3, pp. 950–962, 2009. View at Publisher · View at Google Scholar · View at Scopus
  47. A. Aarabi, F. Wallois, and R. Grebe, “Does spatiotemporal synchronization of EEG change prior to absence seizures?” Brain Research, vol. 1188, no. 1, pp. 207–221, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. W. Klonowski, “Everything you wanted to ask about EEG but were afraid to get the right answer,” Nonlinear Biomedical Physics, vol. 3, article 2, 2009. View at Publisher · View at Google Scholar · View at Scopus
  49. T. Higuchi, “Approach to an irregular time series on the basis of the fractal theory,” Physica D, vol. 31, no. 2, pp. 277–283, 1988. View at Google Scholar · View at Scopus
  50. H. Hinrikus, M. Bachmann, D. Karai et al., “Higuchi's fractal dimension for analysis of the effect of external periodic stressor on electrical oscillations in the brain,” Medical and Biological Engineering and Computing, vol. 49, no. 5, pp. 585–591, 2011. View at Publisher · View at Google Scholar · View at Scopus
  51. M. H. Zweig and G. Campbell, “Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine,” Clinical Chemistry, vol. 39, no. 4, pp. 561–577, 1993. View at Google Scholar · View at Scopus