A novel quick seizure detection and localization through brain data mining on ECoG dataset


Epilepsy is a common neurological disorder, and epileptic seizure detection is a scientific challenge since sometimes patient do not experience any alert. The objective of this research is to reduce the seizure detection time while maintaining high accuracy, and locate the brain hemisphere that is mostly affected by seizure. We argue that by using a decision forest (i.e., an ensemble of carefully built decision trees), instead of a single classifier such as a decision tree, we can afford to reduce epoch lengths (used for converting the ECoG and EEG signal into datasets) without compromising accuracy. This will allow us to build the future records in a shorter time resulting in a quicker seizure detection. In this paper, we apply two decision forest classifiers, called SysFor and Forest CERN, on an ECoG brain dataset. Our initial experiments on the dataset of a single patient indicate that decision forest algorithms such as SysFor and Forest CERN can reduce the seizure detection time significantly while maintaining 100% accuracy. They can also be used to identify the region of the brain of a patient that is mostly affected by seizure.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    de Boer HM, Mula M, Sander JW (2008) The global burden and stigma of epilepsy. Epilepsy Behav 12(4):540–546

    Article  Google Scholar 

  2. 2.

    Jacoby A, Snape D, Baker G (2005) Epilepsy and social identity: the stigma of a chronic neurological disorder. The Lancet Neurol 4(3):171–178

    Article  Google Scholar 

  3. 3.

    Dorai A, Ponnambalam K (2010) Automated epileptic seizure onset detection. In: 2010 International conference on autonomous and intelligent systems (AIS). IEEE, pp 1–4

  4. 4.

    WHO (2015) Media center epilepsy, (fact sheet n999). http://www.who.int/mediacentre/factsheets/fs999/en/. Accessed 15 July 2015

  5. 5.

    Chaovalitwongse WA (2009) Optimization and data mining in epilepsy research: a review and prospective. In: Handbook of optimization in medicine. Springer, Boston, MA., pp 1–32

    Google Scholar 

  6. 6.

    Neurology Now (2015) Types of seizures. http://journals.lww.com/neurologynow/Fulltext/2008/04060/TypesofSeizures.21.aspx. Accessed 15 July 2015

  7. 7.

    Macleod S, Appleton RE (2007) Neurological disorders presenting mainly in adolescence. Arch Dis Child 92(2):170175

    Google Scholar 

  8. 8.

    Chiang C-Y, Chang N-F, Chen T-C, Chen H-H, Chen L-G (2011) Seizure prediction based on classification of eeg synchronization patterns with on-line retraining and post-processing scheme. In: 2011 Annual international conference of the IEEE engineering in medicine and biology society, EMBC. IEEE, pp 7564–7569

  9. 9.

    Hill NJ, Gupta D, Brunner P, Gunduz A, Adamo MA, Ritaccio A, Schalk G (2012) Recording human electrocorticographic (ecog) signals for neuroscientific research and real-time functional cortical mapping. JoVE J Vis Exp 64:e3993–e3993

    Google Scholar 

  10. 10.

    Kramer MA, Kolaczyk ED, Kirsch HE (2008) Emergent network topology at seizure onset in humans. Epilepsy Res 79(2):173–186

    Article  Google Scholar 

  11. 11.

    Fakhraei S, Soltanian-Zadeh H, Fotouhi F, Elisevich K (2011) Confidence in medical decision making: application in temporal lobe epilepsy data mining. In: Proceedings of the 2011 workshop on data mining for medicine and healthcare, DMMH ’11. ACM, New York, NY, USA, pp 60–63

  12. 12.

    Almazyad AS, Ahamad MG, Siddiqui MK, Almazyad AS (2010) Effective hypertensive treatment using data mining in saudi arabia. J Clin Monit Comput 24(6):391–401

    Article  Google Scholar 

  13. 13.

    Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) (1996) Advances in knowledge discovery and data mining. American Association for Artificial Intelligence, Menlo Park

  14. 14.

    Islam MZ, D’Alessandro S, Furner M, Johnson L, Gray D, Carter L (2016) Brand switching pattern discovery by data mining techniques for the telecommunication industry in Australia. Australas J Inf Syst 20. https://doi.org/10.3127/ajis.v20i0.1420

  15. 15.

    Aljumah AA, Ahamad MG, Siddiqui MK (2013) Application of data mining: diabetes health care in young and old patients. J King Saud Univ Comput Inf Sci 25(2):127–136

    Google Scholar 

  16. 16.

    Aljumah AA, Siddiqui MK (2016) Data mining perspective: prognosis of life style on hypertension and diabetes. Int Arab J Inf Technol 13(1):93–99

    Google Scholar 

  17. 17.

    Fu T-c (2011) A review on time series data mining. Eng Appl Artif Intell 24(1):164–181

    Article  Google Scholar 

  18. 18.

    Gorunescu Florin (2011) Data mining: concepts, models and techniques, vol 12. Springer, Berlin

    Book  Google Scholar 

  19. 19.

    Islam MdZ, Giggins H (2011) Knowledge discovery through sysfor: a systematically developed forest of multiple decision trees. In: Proceedings of the Ninth Australasian data mining conference volume 121. Australian Computer Society, Inc, pp 195–204

  20. 20.

    Adnan MdN, Islam MdZ (2016) Forest CERN: a new decision forest building technique. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 304–315

  21. 21.

    Casson AJ, Lojini L, Rodriguez-Villegas E (2012) Optimal features for online seizure detection. Med Biol Eng Comput 50(7):659–669

    Article  Google Scholar 

  22. 22.

    Donos C, Dümpelmann M, Schulze-Bonhage A (2015) Early seizure detection algorithm based on intracranial eeg and random forest classification. Int J Neural Syst 25(05):1550023

    Article  Google Scholar 

  23. 23.

    Zhang Y, Zhang Y, Wang J, Zheng X (2014) Comparison of classification methods on EEG signals based on wavelet packet decomposition. Neural Comput Appl 26(5):1217–1225

    Article  Google Scholar 

  24. 24.

    Kharbouch A, Shoeb A, Guttag J, Cash SS (2011) An algorithm for seizure onset detection using intracranial eeg. Epilepsy Behav 22:S29–S35

    Article  Google Scholar 

  25. 25.

    Rajendra Acharya U, Molinari F, Vinitha Sree S, Chattopadhyay S, Ng K-H, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7(4):401–408

    Article  Google Scholar 

  26. 26.

    Lahmiri S (2018) An accurate system to distinguish between normal and abnormal electroencephalogram records with epileptic seizure free intervals. Biomed Signal Process Control 40:312–317

    Article  Google Scholar 

  27. 27.

    Lahmiri S (2018) Generalized hurst exponent estimates differentiate eeg signals of healthy and epileptic patients. Phys A Stat Mech Its Appl 490:378–385

    Article  Google Scholar 

  28. 28.

    Fergus P, Hussain A, Hignett D, Al-Jumeily D, Abdel-Aziz K, Hamdan H (2016) A machine learning system for automated whole-brain seizure detection. Appl Comput Inf 12(1):70–89

    Google Scholar 

  29. 29.

    Chen C, Liu J, Syu J (2012) Application of chaos theory and data mining to seizure detection of epilepsy. In: Proceedings of the conf. IPCSIT/Hong Kong, vol 25, pp 23–28

  30. 30.

    Gao J, Xu L (2015) An efficient method to solve the classification problem for remote sensing image. AEU Int J Electron Commun 69(1):198–205

    Article  Google Scholar 

  31. 31.

    Gao J, Xu L (2016) A novel spatial analysis method for remote sensing image classification. Neural Process Lett 43(3):805–821

    Article  Google Scholar 

  32. 32.

    Gao J, Xu L, Huang F (2016) A spectral–textural kernel-based classification method of remotely sensed images. Neural Comput Appl 27(2):431–446

    Article  Google Scholar 

  33. 33.

    Li L, Ge H, Gao J (2017) A spectral-spatial kernel-based method for hyperspectral imagery classification. Adv Space Res 59(4):954–967

    Article  Google Scholar 

  34. 34.

    Li L, Ge H, Tong Y, Zhang Y (2017) Face recognition using gabor-based feature extraction and feature space transformation fusion method for single image per person problem. Neural Process Lett. https://doi.org/10.1007/s11063-017-9693-4

    Article  Google Scholar 

  35. 35.

    Jurcak V, Tsuzuki D, Dan I (2007) 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. NeuroImage 34(4):1600–1611

    Article  Google Scholar 

  36. 36.

    Trans-Cranial-Technologies (2015) 10/20 system positioning. https://www.trans-cranial.com/local/manuals/10_20_pos_man_v1_0_pdf.pdf. Accessed 7 Oct 2015

  37. 37.

    Acar E, Bingöl CA, Bingöl H, Yener B (2006) Computational analysis of epileptic focus localization. In: Proceedings of the 24th IASTED international conference on biomedical engineering, BioMed’06. ACTA Press, Anaheim, CA, USA, pp 317–322

  38. 38.

    Ghannad-Rezaie M, Soltanain-Zadeh H, Siadat MR, Elisevich KV (2006) Medical data mining using particle swarm optimization for temporal lobe epilepsy. In: 2006 IEEE international conference on evolutionary computation. pp 761–768

  39. 39.

    Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc, San Francisco

    Google Scholar 

  40. 40.

    Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  41. 41.

    Epilepsy dataset (2015) http://math.bu.edu/people/kolaczyk/datasets/epilepsy.zip. Accessed 7 Apr 2015

  42. 42.

    Ihle M, Feldwisch-Drentrup H, Teixeira CA, Witon A, Schelter B, Timmer J, Schulze-Bonhage A (2012) EPILEPSIAE—a european epilepsy database. Comput Methods Programs Biomed 106(3):127–138

    Article  Google Scholar 

  43. 43.

    Siddiqui MK, Islam MdZ (2016) Data mining approach in seizure detection. In: 2016 IEEE region 10 conference (TENCON), Singapore. Institute of Electrical and Electronics Engineers (IEEE), pp 3579–3583

  44. 44.

    Gevins AS, Rémond A (1987) Methods of analysis of brain electrical and magnetic signals (handbook of electroencephalography and clinical neurophysiology). New Ser. Elsevier, Amsterdam

    Google Scholar 

  45. 45.

    Li J, Liu H (2003) Ensembles of cascading trees. In: Third IEEE international conference on data mining, 2003. ICDM 2003. IEEE, pp 585–588

  46. 46.

    Al-Saggaf Y, Islam MdZ (2015) Data mining and privacy of social network sites users: implications of the data mining problem. Sci Eng Ethics 21(4):941–966

    Article  Google Scholar 

  47. 47.

    Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844

    Article  Google Scholar 

  48. 48.

    Breiman Leo, Friedman Jerome H, Olshen Richard A, Stone Charles J (1984) Classification and regression trees. Wadsworth & Brooks, Monterey

    MATH  Google Scholar 

  49. 49.

    Arlot S, Celisse A et al (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79

    MathSciNet  Article  Google Scholar 

  50. 50.

    Kurgan LA, Cios KJ (2004) CAIM discretization algorithm. IEEE Trans Knowl Data Eng 16(2):145–153

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Mohammad Khubeb Siddiqui.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Siddiqui, M.K., Islam, M.Z. & Kabir, M.A. A novel quick seizure detection and localization through brain data mining on ECoG dataset. Neural Comput & Applic 31, 5595–5608 (2019). https://doi.org/10.1007/s00521-018-3381-9

Download citation


  • Decision tree
  • Decision forest
  • Classification
  • Brain data mining
  • Data mining
  • Quick seizure detection
  • Seizure localization
  • Epilepsy