Abstract
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.
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References
de Boer HM, Mula M, Sander JW (2008) The global burden and stigma of epilepsy. Epilepsy Behav 12(4):540–546
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
Dorai A, Ponnambalam K (2010) Automated epileptic seizure onset detection. In: 2010 International conference on autonomous and intelligent systems (AIS). IEEE, pp 1–4
WHO (2015) Media center epilepsy, (fact sheet n999). http://www.who.int/mediacentre/factsheets/fs999/en/. Accessed 15 July 2015
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
Neurology Now (2015) Types of seizures. http://journals.lww.com/neurologynow/Fulltext/2008/04060/TypesofSeizures.21.aspx. Accessed 15 July 2015
Macleod S, Appleton RE (2007) Neurological disorders presenting mainly in adolescence. Arch Dis Child 92(2):170175
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
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
Kramer MA, Kolaczyk ED, Kirsch HE (2008) Emergent network topology at seizure onset in humans. Epilepsy Res 79(2):173–186
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
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
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
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
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
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
Fu T-c (2011) A review on time series data mining. Eng Appl Artif Intell 24(1):164–181
Gorunescu Florin (2011) Data mining: concepts, models and techniques, vol 12. Springer, Berlin
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
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
Casson AJ, Lojini L, Rodriguez-Villegas E (2012) Optimal features for online seizure detection. Med Biol Eng Comput 50(7):659–669
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
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
Kharbouch A, Shoeb A, Guttag J, Cash SS (2011) An algorithm for seizure onset detection using intracranial eeg. Epilepsy Behav 22:S29–S35
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
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
Lahmiri S (2018) Generalized hurst exponent estimates differentiate eeg signals of healthy and epileptic patients. Phys A Stat Mech Its Appl 490:378–385
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
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
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
Gao J, Xu L (2016) A novel spatial analysis method for remote sensing image classification. Neural Process Lett 43(3):805–821
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
Li L, Ge H, Gao J (2017) A spectral-spatial kernel-based method for hyperspectral imagery classification. Adv Space Res 59(4):954–967
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
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
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
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
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
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc, San Francisco
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Epilepsy dataset (2015) http://math.bu.edu/people/kolaczyk/datasets/epilepsy.zip. Accessed 7 Apr 2015
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
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
Gevins AS, Rémond A (1987) Methods of analysis of brain electrical and magnetic signals (handbook of electroencephalography and clinical neurophysiology). New Ser. Elsevier, Amsterdam
Li J, Liu H (2003) Ensembles of cascading trees. In: Third IEEE international conference on data mining, 2003. ICDM 2003. IEEE, pp 585–588
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
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844
Breiman Leo, Friedman Jerome H, Olshen Richard A, Stone Charles J (1984) Classification and regression trees. Wadsworth & Brooks, Monterey
Arlot S, Celisse A et al (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79
Kurgan LA, Cios KJ (2004) CAIM discretization algorithm. IEEE Trans Knowl Data Eng 16(2):145–153
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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
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DOI: https://doi.org/10.1007/s00521-018-3381-9