Annals of Biomedical Engineering

, Volume 37, Issue 10, pp 2129–2145 | Cite as

Seizure Detection Using Seizure Probability Estimation: Comparison of Features Used to Detect Seizures

  • Levin Kuhlmann
  • Anthony N. Burkitt
  • Mark J. Cook
  • Karen Fuller
  • David B. Grayden
  • Linda Seiderer
  • Iven M. Y. Mareels


This paper analyses seizure detection features and their combinations using a probability-based scalp EEG seizure detection framework developed by Marc Saab and Jean Gotman. Our method was evaluated on 525 h of data, including 88 seizures in 21 patients. The individual performances of the three features used by Saab and Gotman were compared to six alternative features, and combinations of these nine features were analyzed in order to find a superior detector. On a testing set with the combination of their three features, Saab and Gotman reported a sensitivity of 0.78, a false positive rate of 0.86/h, and a median detection delay of 9.8 s. Based on 10-fold cross-validation the testing performance of our implementation of their method achieved a sensitivity of 0.79, a false positive rate of 0.62/h, and a median detection delay of 21.3 s. A detector based on an alternative combination of features achieved sensitivity of 0.81, a false positive rate of 0.60/h, and a median detection delay of 16.9 s. By including filtering techniques, it was possible to achieve performance levels similar to Saab and Gotman using our implementation of their method, although this involved increases in detection delays. Of the seizure detection measures investigated, relative average amplitude, relative power, relative derivative, and coefficent of variation of amplitude provided the best performing combinations. These better-performing features can be employed together to make robust and reliable seizure detectors.


EEG Epilepsy Seizure detection Seizure onset 



This work was supported by an Australian Research Council Linkage Project Grant (LP0560684), The Bionic Ear Institute and St. Vincent’s Hospital Melbourne. We are grateful for the EEG data provided by the patients, and to the St. Vincent’s Hospital Melbourne Neurophysiology Clinic for collecting the data. EEG data collection was approved by the St. Vincent’s Hospital Melbourne Ethics Committee. We also thank Michael Eager for helping to format the manuscript.

Supplementary material

10439_2009_9755_MOESM1_ESM.pdf (694 kb)
PDF (693 KB)


  1. 1.
    Acir, N., I. Öztura, M. Kuntalp, B. Baklan, and C. Gützelis. Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks. IEEE Trans. Biomed. Eng. 52(1):30–40, 2005.CrossRefPubMedGoogle Scholar
  2. 2.
    Bayes, T. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions, Giving Some Account of the Present Undertakings, Studies and Labours of the Ingenious in Many Considerable Parts of the World 53:370–418, 1763.Google Scholar
  3. 3.
    Blume, W., and S. Wiebe. “Periodic” seizures. Epilepsia 38(12):1355–1358, 1997.CrossRefPubMedGoogle Scholar
  4. 4.
    Burrus, C., R. Gopinath, and H. Guo, editors. Introduction to Wavelets and Wavelet Transforms: A Primer. Englewood Cliffs, NJ: Prentice-Hall, 1998.Google Scholar
  5. 5.
    Daubechies, I., editor. Ten Lectures on Wavelets. Montepelier, VT: Capital City Press, 1992.Google Scholar
  6. 6.
    Duda, R., P. Hart, and D. Stork. Pattern Classification. New York, NY: Wiley, 2001.Google Scholar
  7. 7.
    Feichtinger, M., H. Eder, A. Holl, E. Korner, G. Zmugg, R. Aigner, F. Fazekas, and E. Ott. Automatic and remote controlled ictal spect injection for seizure focus localization by use of a commercial contrast agent application pump. Epilepsia 48(7):1409–1413, 2007.CrossRefPubMedGoogle Scholar
  8. 8.
    Firpi, H., E. Goodman, and J. Echauz. On prediction of epileptic seizures by means of genetic programming artificial features. Ann. Biomed. Eng. 34(3):515–529, 2006.CrossRefPubMedGoogle Scholar
  9. 9.
    Firpi, H., E. Goodman, and J. Echauz. Epileptic seizure detection using genetically programmed artificial features. IEEE Trans. Biomed. Eng. 54(2):212–224, 2007.CrossRefPubMedGoogle Scholar
  10. 10.
    Gabor, A. Seizure detection using a self-organizing neural network: validation and comparison with other detection strategies. Electroencephal. Clin. Neurophysiol. 107:27–32, 1998.CrossRefGoogle Scholar
  11. 11.
    Ghosh-Dastidar, S., H. Adeli, and N. Dadmehr. Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans. Biomed. Eng. 54(9):1545–1551, 2007.CrossRefPubMedGoogle Scholar
  12. 12.
    Ghosh-Dastidar, S., H. Adeli, and N. Dadmehr. Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Biomed. Eng. 55(2):512–518, 2008.CrossRefPubMedGoogle Scholar
  13. 13.
    Gotman, J. Automatic recognition of epileptic seizures in the EEG. Electroencephal. Clin. Neurophysiol. 54:530–540, 1982.CrossRefGoogle Scholar
  14. 14.
    Gotman, J. Automatic seizure detection: improvements and evaluation. Electroencephal. Clin. Neurophysiol. 76:317–324, 1990.CrossRefGoogle Scholar
  15. 15.
    Gotman, J., and P. Gloor. Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. Electroencephal. Clin. Neurophysiol. 49:513–529, 1976.CrossRefGoogle Scholar
  16. 16.
    Gotman, J., J. Ives, and P. Gloor. Frequency content of EEG and EMG at seizure onset: possibility of removal of EMG artifact by digital filtering. Electroencephal. Clin. Neurophysiol. 52(2):626–639, 1981.CrossRefGoogle Scholar
  17. 17.
    Greene, B., S. Faula, W. Marnanea, G. Lightbodya, I. Korotchikova, and G. Boylan. A comparison of quantitative EEG features for neonatal seizure detection. Clin. Neurophysiol. 119:1248–1261, 2008.CrossRefPubMedGoogle Scholar
  18. 18.
    Grewal, S., and J. Gotman. An automatic warning system for epileptic seizures recorded on intracerebral EEGs. Clin. Neurophysiol. 116:2460–2472, 2005.CrossRefPubMedGoogle Scholar
  19. 19.
    Guye, M., J. Regis, F. Tamura, M. Wendling, A. Gonigal, P. Chauvel, and F. Bartolomei. The role of corticothalamic coupling in human temporal lobe epilepsy. Brain 129:1917–1928, 2006.CrossRefPubMedGoogle Scholar
  20. 20.
    Haas, S., M. Frei, and I. Osorio. Strategies for adapting automated seizure detection algorithms. Med. Eng. Phys. 29:895–909, 2007.CrossRefPubMedGoogle Scholar
  21. 21.
    Haut, S., S. Shinnar, and S. Moshé. Seizure clustering: risks and outcomes. Epilepsia 41(1):146–149, 2005.CrossRefGoogle Scholar
  22. 22.
    Hilfiker, P., and M. Egli. Detection and evolution of rhythmic components in ictal EEG using short segment spectra and discriminant-analysis. Electroencephal. Clin. Neurophysiol. 82:255–265, 1992.CrossRefGoogle Scholar
  23. 23.
    Iasemidis, L., D.-S. Shiau, P. Pardalos, W. Chaovalitwongse, K. Narayanan, A. Prasad, K. Tsakalis, P. Carney, and J. Sackellares. Long-term prospective on-line real-time seizure prediction. Clin. Neurophysiol. 116:532–544, 2005.CrossRefPubMedGoogle Scholar
  24. 24.
    Khan, Y., and J. Gotman. Wavelet-based automatic seizure detection in intracerebral electroencephalogram. Clin. Neurophysiol. 114(5):898–908, 2003.CrossRefPubMedGoogle Scholar
  25. 25.
    Kuhlmann, L., A. Burkitt, M. Cook, K. Fuller, D. Grayden, and I. Mareels. Correlation analysis of seizure detection features. In: 4th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 898–908, 2008.Google Scholar
  26. 26.
    Lai, A., T. Nelson, A. Halliday, D. Freestone, A. Burkitt, and M. Cook. Synchronisation in intracranial electrical activity recordings from rats with the tetanus toxin model of temporal lobe epilepsy. In: Proceedings of the 22nd Annual Scientific Meeting of the Epilepsy Society of Australia, pp. 36, P57, 2007.Google Scholar
  27. 27.
    Le Van Quyen, M., J. Martinerie, M. Baulac, F. Varela. Anticipating epileptic seizures in real time by a non-linear analysis of similarity between eeg recordings. NeuroReport 10:2149–2155, 1999.CrossRefGoogle Scholar
  28. 28.
    Lehnertz, K., and C. Elger. Can epileptic seizures be predicted? Evidence from nonlinear time series analysis of brain electrical activity. Phys. Rev. Lett. 80:5019–5022, 1998.CrossRefGoogle Scholar
  29. 29.
    Ocak, H. Automatic detection of epileptic seizures in eeg using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 36:2027–2036, 2009.CrossRefGoogle Scholar
  30. 30.
    Osorio, I., M. Frei, J. Giftakis, T. Peters, J. Ingram, M. Turnbull, M. Herzog, M. Rise, S. Schaffner, R. Wennberg, T. Walczak, M. Risinger, and C. Ajmone-Marsan. Performance reassessment of a real-time seizure-detection algorithm for long ECoG series. Epilepsia 43(12):1522–1535, 2002.CrossRefPubMedGoogle Scholar
  31. 31.
    Osorio, I., M. Frei, and S. Wilkinson. Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset. Epilepsia 39(6):615–627, 1998.CrossRefPubMedGoogle Scholar
  32. 32.
    Päivinen, N., S. Lammi, A. Pitkänen, J. Nissinen, M. Penttonen, and T. Grönfors. Epileptic seizure detection: a nonlinear viewpoint. Comp. Meth. Prog. Biomed. 79:151–159, 2005.CrossRefGoogle Scholar
  33. 33.
    Pauri, F., F. Pierelli, G. Chartrian, and W. Erdly. Long-term EEG-video-audio monitoring: computer detection of focal EEG seizure patterns. Electroencephal. Clin. Neurophysiol. 82:1–9, 1992.CrossRefGoogle Scholar
  34. 34.
    Proakis, J., and D. Manolakis. Digital Signal Processing: Principles, Algorithms and Applications, 3rd edn. Upper Saddle River, NJ: Prentice-Hall, 1996.Google Scholar
  35. 35.
    Qu, H., and J. Gotman. A seizure warning system for long-term epilepsy monitoring. Neurology 45(12):2250–2254, 1995.PubMedGoogle Scholar
  36. 36.
    Rabiner, L., and B. Gold. Theory and Application of Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1975.Google Scholar
  37. 37.
    Rangayyan, R. Biomedical Signal Analysis: A Case-Study Approach. IEEE Press Series on Biomedical Engineering. New York, NY: Wiley, 2002.Google Scholar
  38. 38.
    Rosso, O., M. Martin, and A. Plastino. Brain electrical activity analysis using wavelet-based informational tools. Physica A 313(3–4):587–608, 2002.CrossRefGoogle Scholar
  39. 39.
    Saab, M., and J. Gotman. A system to detect the onset of epileptic seizures in scalp EEG. Clin. Neurophysiol. 116:427–442, 2005.CrossRefPubMedGoogle Scholar
  40. 40.
    Schindler, K., H. Leung, C. Elger, and K. Lehnertz. Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG. Brain 130:65–77, 2007.CrossRefPubMedGoogle Scholar
  41. 41.
    Schuyler, R., A. White, K. Staley, and J. Krzysztof. Epileptic seizure detection: identification of ictal and pre-ictal states using rbf networks with wavelet-decomposed eeg data. IEEE EMBS Mag. March/April:74–81, 2007.Google Scholar
  42. 42.
    Shoeb, A., H. Edwards, J. Connolly, B. Bourgeois, S. Treves, and J. Guttag. Patient-specific seizure onset detection. Epil. Beh. 5:483–498, 2004.CrossRefGoogle Scholar
  43. 43.
    Srinivasan, V., C. Eswaran, and N. Sriraam. Approximate entropy-based epileptic eeg detection using artificial neural networks. IEEE Trans. Biomed. Eng. 11(3):288–295, 2007.Google Scholar
  44. 44.
    Tezel, G., and Y. Özbay. A new approach for epileptic seizure detection using adaptive neural network. Expert Syst. Appl. 36:172–180, 2009.CrossRefGoogle Scholar
  45. 45.
    Varsavsky, A., and I. Mareels. Patient un-specific detection of epileptic seizures through changes in variance. In: Engineering in Medicine and Biology Society, 2006. EMBS ’06. 28th Annual International Conference of the IEEE, pp. 3747–3750, 2006.Google Scholar
  46. 46.
    Wilson, S. Algorithm architectures for patient dependent seizure detection. Clin. Neurophysiol. 117(6):1204–1216, 2006.CrossRefPubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2009

Authors and Affiliations

  • Levin Kuhlmann
    • 1
  • Anthony N. Burkitt
    • 1
    • 2
  • Mark J. Cook
    • 2
    • 3
  • Karen Fuller
    • 3
  • David B. Grayden
    • 1
    • 2
  • Linda Seiderer
    • 3
  • Iven M. Y. Mareels
    • 1
  1. 1.Department of Electrical and Electronic EngineeringThe University of MelbourneParkvilleAustralia
  2. 2.The Bionic Ear InstituteEast MelbourneAustralia
  3. 3.Department of NeurologySt. Vincent’s Hospital MelbourneFitzroyAustralia

Personalised recommendations