Skip to main content
SpringerLink
Log in

On predicting epileptic seizures from intracranial electroencephalography

  • Original Article
  • Published:
Biomedical Engineering Letters Aims and scope Submit manuscript

Abstract

This study investigates the sensitivity and specificity of predicting epileptic seizures from intracranial electroencephalography (iEEG). A monitoring system is studied to generate an alarm upon detecting a precursor of an epileptic seizure. The iEEG traces of ten patients suffering from medically intractable epilepsy were used to build a prediction model. From the iEEG recording of each patient, power spectral densities were calculated and classified using support vector machines. The prediction results varied across patients. For seven patients, seizures were predicted with 100% sensitivity without any false alarms. One patient showed good sensitivity but lower specificity, and the other two patients showed lower sensitivity and specificity. Predictive analytics based on the spectral feature of iEEG performs well for some patients but not all. This result highlights the need for patient-specific prediction models and algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Jeong S, Kim S, Kim D, Youn CH, Kim YW. A personalized healthcare system for chronic disease care in home-hospital cloud environments. In: International conference on ICT convergence. 2013. p. 371–76.

  2. Gandhi M, Singh S. Predictions in heart disease using techniques of data mining. In: International conference on futuristic trends on computational analysis and knowledge management. 2015. p. 520–25.

  3. Fisher RS, Boas WvE, Blume W, Elger C, Genton P, Lee P, Engel J. Epileptic seizures and epilepsy: definitions proposed by the international league against epilepsy and the international bureau for epilepsy. Epilepsia. 2005;46(4):470–2.

    Article  Google Scholar 

  4. Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain. 2007;130(2):314–33.

    Article  Google Scholar 

  5. Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V, Held H, van Nes EH, Rietkerk M, Sugihara G. Early-warning signals for critical transitions. Nature. 2009;461(7260):53–9.

    Article  Google Scholar 

  6. Andrzejak RG, Chicharro D, Elger CE, Mormann F. Seizure prediction: any better than chance? Clin Neurophysiol. 2009;120(8):1465–78.

    Article  Google Scholar 

  7. Iasemidis LD, Sackellares JC, Zaveri HP, Wlliams WJ. Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures. Brain Topogr. 1990;2:187–201.

    Article  Google Scholar 

  8. Le Van Quyen ML, Martinerie J, Baulac M, Varela F. Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. NeuroReport. 1999;10:2149–55.

    Article  Google Scholar 

  9. Mormann F, Lehnertz K, David P, Elger C E. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Phys D: Nonlinear Phenom. 2000;144(3–4):358–69.

    Article  MATH  Google Scholar 

  10. Iasemidis L, Shiau DS, Chaovalitwongse W, Sackellares J, Pardalos P, Principe J, Carney P, Prasad A, Veeramani B, Tsakalis K. Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng. 2003;50(5):616–27.

    Article  Google Scholar 

  11. Freiburg seizure prediction project. http://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database.

  12. Park Y, Luo L, Parhi KK, Netoff T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia. 2011;52(10):1761–70.

    Article  Google Scholar 

  13. Bandarabadi M, Teixeira CA, Rasekhi J, Dourado A. Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol. 2015;126(2):237–48.

    Article  Google Scholar 

  14. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–97.

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by 2016 Hongik University Research Fund.

Author information

Authors and Affiliations

  1. Department of Computer and Information Communications, Hongik University, D407, 2639 Sejong-ro, Sejong-si, 30016, Korea

    Yongseok Yoo

Authors
  1. Yongseok Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongseok Yoo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yoo, Y. On predicting epileptic seizures from intracranial electroencephalography. Biomed. Eng. Lett. 7, 1–5 (2017). https://doi.org/10.1007/s13534-017-0008-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13534-017-0008-5

Keywords

Search

Navigation