Medical & Biological Engineering & Computing

, Volume 57, Issue 11, pp 2461–2469 | Cite as

Real-time epileptic seizure prediction based on online monitoring of pre-ictal features

  • Hoda Sadeghzadeh
  • Hossein Hosseini-NejadEmail author
  • Sina Salehi
Original Article


Reliable prediction of epileptic seizures is of prime importance as it can drastically change the quality of life for patients. This study aims to propose a real-time low computational approach for the prediction of epileptic seizures and to present an efficient hardware implementation of this approach for portable prediction systems. Three levels of feature extraction are performed to characterize the pre-ictal activities of the EEG signal. In the first-level, the line length algorithm is applied to the pre-ictal region. The features obtained in the first-level are mathematically integrated to extract the second-level features and then the line lengths of the second-level features are calculated to obtain our third-level feature. The third-level information is compared with predefined threshold levels to make a decision on whether the extracted characteristics are relevant to a seizure occurrence or not. The validity of this algorithm was tested by EEG recordings in the CHB-MIT database (97 seizures, 834.224 h) for 19 epileptic patients. The results showed that the average sensitivity was 90.62%, the specificity was 88.34%, the accuracy was 88.76% with the average false prediction rate as low as 0.0046 h−1, and the average prediction time was 23.3 min. The low computational complexity is the superiority of the proposed approach, which provides a technologically simple but accurate way of predicting epileptic seizures and enables hardware implantable devices.

Graphical abstract

Proposed seizure prediction algorithm and its features


Epileptic seizure prediction EEG Low computational complexity Hardware implementation Line length 



  1. 1.
    Bandarabadi M, Teixeira CA, Rasekhi J, Dourado A (2015) Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol 126:237–248. CrossRefPubMedGoogle Scholar
  2. 2.
    Chien J-H, Shiau D-S, Halford J, Kelly K, Kern R, Yang M, Zhang J, Sackellares JC, Pardalos P (2011) A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings. Cybern Syst Anal 47:586–597CrossRefGoogle Scholar
  3. 3.
    Chisci L, Mavino A, Perferi G, Sciandrone M, Anile C, Colicchio G, Fuggetta F (2010) Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Trans Biomed Eng 57:1124–1132CrossRefGoogle Scholar
  4. 4.
    Freestone DR, Karoly PJ, Peterson AD, Kuhlmann L, Lai A, Goodarzy F, Cook MJ (2015) Seizure prediction: science fiction or soon to become reality? Curr Neurol Neurosci Rep 15:73. CrossRefPubMedGoogle Scholar
  5. 5.
    Ghaderyan P, Abbasi A, Sedaaghi MH (2014) An efficient seizure prediction method using KNN-based undersampling and linear frequency measures. J Neurosci Methods 232:134–142. CrossRefPubMedGoogle Scholar
  6. 6.
    Giannakakis G, Sakkalis V, Pediaditis M, Tsiknakis M (2014) Methods for seizure detection and prediction: an overview. In: Modern electroencephalographic assessment techniques. Springer, pp 131–157Google Scholar
  7. 7.
    Guo L, Rivero D, Dorado J, Rabunal JR, Pazos A (2010) Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J Neurosci Methods 191:101–109. CrossRefPubMedGoogle Scholar
  8. 8.
    Joshi V, Pachori RB, Vijesh A (2014) Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed Signal Process Control 9:1–5CrossRefGoogle Scholar
  9. 9.
    Kolarijani MAS, Amirsalari S, Haidari MR (2017) Analysis of variations of correlation dimension and nonlinear interdependence for the prediction of pediatric myoclonic seizures—a preliminary study. Epilepsy Res 135:102–114. CrossRefPubMedGoogle Scholar
  10. 10.
    Kuhlmann L, Freestone D, Lai A, Burkitt AN, Fuller K, Grayden DB, Seiderer L, Vogrin S, Mareels IM, Cook MJ (2010) Patient-specific bivariate-synchrony-based seizure prediction for short prediction horizons. Epilepsy Res 91:214–231. CrossRefPubMedGoogle Scholar
  11. 11.
    Li X, Ouyang G, Richards DA (2007) Predictability analysis of absence seizures with permutation entropy. Epilepsy Res 77:70–74. CrossRefPubMedGoogle Scholar
  12. 12.
    Li S, Zhou W, Yuan Q, Liu Y (2013) Seizure prediction using spike rate of intracranial EEG. IEEE Trans Neural Syst Rehabil Eng 21:880–886CrossRefGoogle Scholar
  13. 13.
    Liu D, Pang Z, Wang Z (2009) Epileptic seizure prediction by a system of particle filter associated with a neural network. EURASIP J Adv Signal Process 2009.
  14. 14.
    Logesparan L, Casson AJ, Rodriguez-Villegas E (2012) Optimal features for online seizure detection. Med Biol Eng Comput 50:659–669. CrossRefPubMedGoogle Scholar
  15. 15.
    Ouyang CS, Chen B-J, Cai Z-E, Lin L-C, Wu R-C, Chiang C-T, Yang R-C (2018) Feature extraction of EEG signals for epileptic seizure prediction. In: International conference on smart vehicular technology, transportation, communication and applications. Springer, pp 298–303Google Scholar
  16. 16.
    Özbeyaz A, Gürsoy Mİ, Çoban R Regularization and kernel parameters optimization based on PSO algorithm in EEG signals classification with SVM. In: Signal processing and communications applications (SIU), 2011 IEEE 19th conference on, 2011. IEEE, pp 399–402Google Scholar
  17. 17.
    Park Y, Luo L, Parhi KK, Netoff T (2011) Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 52:1761–1770. CrossRefPubMedGoogle Scholar
  18. 18.
    Parvez MZ, Paul M (2016) Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation. IEEE Trans Neural Syst Rehabil Eng 24:158–168. CrossRefPubMedGoogle Scholar
  19. 19.
    Rajdev P, Ward MP, Rickus J, Worth R, Irazoqui PP (2010) Real-time seizure prediction from local field potentials using an adaptive Wiener algorithm. Comput Biol Med 40:97–108. CrossRefPubMedGoogle Scholar
  20. 20.
    Rasekhi J, Mollaei MR, Bandarabadi M, Teixeira CA, Dourado A (2013) Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. J Neurosci Methods 217:9–16. CrossRefPubMedGoogle Scholar
  21. 21.
    Sareen S, Sood SK, Gupta SK (2016) An automatic prediction of epileptic seizures using cloud computing and wireless sensor networks. J Med Syst 40:226. CrossRefPubMedGoogle Scholar
  22. 22.
    Shoeb A CHB-MIT scalp EEG database. Accessed 25 Oct 2013
  23. 23.
    Taran S, Bajaj V, Siuly S (2017) An optimum allocation sampling based feature extraction scheme for distinguishing seizure and seizure-free EEG signals. Health Inf Sci Syst 5:7CrossRefGoogle Scholar
  24. 24.
    Williamson JR, Bliss DW, Browne DW, Narayanan JT (2012) Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy Behav 25:230–238. CrossRefPubMedGoogle Scholar
  25. 25.
    Zandi AS, Tafreshi R, Javidan M, Dumont GA (2013) Predicting epileptic seizures in scalp EEG based on a variational Bayesian Gaussian mixture model of zero-crossing intervals. IEEE Trans Biomed Eng 60:1401–1413CrossRefGoogle Scholar
  26. 26.
    Zhang Y, Zhou W, Yuan Q, Wu Q (2014) A low computation cost method for seizure prediction. Epilepsy Res 108:1357–1366. CrossRefPubMedGoogle Scholar
  27. 27.
    Zhang Z, Chen Z, Zhou Y, Du S, Zhang Y, Mei T, Tian X (2014) Construction of rules for seizure prediction based on approximate entropy. Clin Neurophysiol 125:1959–1966. CrossRefPubMedGoogle Scholar
  28. 28.
    Zheng Y, Wang G, Li K, Bao G, Wang J (2014) Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition. Clin Neurophysiol 125:1104–1111. CrossRefPubMedGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  • Hoda Sadeghzadeh
    • 1
  • Hossein Hosseini-Nejad
    • 1
    • 2
    Email author
  • Sina Salehi
    • 3
  1. 1.Faculty of Computer EngineeringK. N. Toosi University of TechnologyTehranIran
  2. 2.Faculty of Electrical EngineeringK. N. Toosi University of TechnologyTehranIran
  3. 3.Shiraz Neuroscience Research CenterShiraz University of Medical SciencesShirazIran

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