A review of epileptic seizure detection using machine learning classifiers

Organization WH (2006) Neurological disorders: public health challenges. World Health Organization, New York Chaudhary UJ, Duncan JS, Lemieux L (2011) A dialogue with historical concepts of epilepsy from the babylonians to hughlings jackson: persistent beliefs. Epilepsy Behav 21(2):109–114 Reynolds EH (2009) Milestones in epilepsy*. Epilepsia 50(3):338–342. https://doi.org/10.1111/j.1528-1167.2009.02050.x WHO (2005) Atlas: Epilepsy care in the world. World Health Organization, Geneva Alarcón G, Valentín A (2012) Introduction to Epilepsy. Cambridge University Press, Cambridge Qu H, Gotman J (1993) Improvement in seizure detection performance by automatic adaptation to the eeg of each patient. Electroencephalogr Clin Neurophysiol 86(2):79–87 Sazgar M, Young MG (2019) Seizures and Epilepsy. In: Absolute Epilepsy and EEG rotation review, pp. 9–46. Springer, New York Schachter S, Shafer P, Sirven J (2013) What causes epilepsy and seizures. Epilepsy Foundation Delanty N, Vaughan CJ, French JA (1998) Medical causes of seizures. Lancet 352(9125):383–390 WHO: Media Center Epilepsy, (Fact sheet N999). http://www.who.int/mediacentre/factsheets/fs999/en/ (2015) Accessed 15 July 2019 Shafer PO, Sirven JI (2014) Epilepsy statistics. Epilepsy Foundation Hannah JA, Brodie MJ (1998) Epilepsy and learning disabilities—a challenge for the next millennium? Seizure 7(1):3–13 Thurman DJ, Beghi E, Begley CE, Berg AT, Buchhalter JR, Ding D, Hesdorffer DC, Hauser WA, Kazis L, Kobau R et al (2011) Standards for epidemiologic studies and surveillance of epilepsy. Epilepsia 52(s7):2–26 Fisher RS (2017) The new classification of seizures by the international league against epilepsy 2017. Curr Neurol Neurosci Rep 17(6):48 Mahmud M, Kaiser MS, Hussain A, Vassanelli S (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Networks Learn Syst 29(6):2063–2079 Mahmud M, Kaiser MS, Hussain A (2020) Deep learning in mining biological data. arXiv:2003.00108 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, CA, USA Yin Y, Kaku I, Tang J, Zhu J (2011) Data mining: concepts, methods and applications in management and engineering design. Springer, New York 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. Aust J Inform Syst. 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 Inform Sci 25(2):127–136 Aljumah A, Siddiqui M (2016) Data mining perspective: prognosis of life style on hypertension and diabetes. Int Arab J Inf Technol 13(1) Siddiqui MK, Morales-Menendez R, Gupta PK, Iqbal HM, Hussain F, Khatoon K, Ahmad S (2020) Correlation between temperature and COVID-19 (suspected, confirmed and death) cases based on machine learning analysis. J Pure Appl Microbiol 14(Spl Edn.) Aljumah AA, Siddiqui MK (2014) Hypertension interventions using classification based data mining. Res J Appl Sci Eng Technol 7(17):3593–3602 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 Singh GA, Gupta PK (2019) Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Comput Appl 31(10):6863–6877 Fu T-c (2011) A review on time series data mining. Eng Appl Artif Intell 24(1):164–181 Tzallas AT, Tsipouras MG, Tsalikakis DG, Karvounis EC, Astrakas L, Konitsiotis S, Tzaphlidou M (2012) Automated epileptic seizure detection methods: a review study. In: Epilepsy-histological, electroencephalographic and psychological aspects. InTech Abbasi B, Goldenholz DM (2019) Machine learning applications in epilepsy. Epilepsia Siddiqui MK. Brain data mining for epileptic seizure-detection. Doctoral Dissertation, Charles Sturt University, Australia Paul Y (2018) Various epileptic seizure detection techniques using biomedical signals: a review. Brain Inform 5(2):6 Boonyakitanont P, Lek-uthai A, Chomtho K, Songsiri J (2020) A review of feature extraction and performance evaluation in epileptic seizure detection using eeg. Biomed Signal Process Control 57:101702 Sharmila A, Geethanjali P (2019) A review on the pattern detection methods for epilepsy seizure detection from EEG signals. Biomed Eng /Biomedizinische Technik. 64(5):507–17 van Westrhenen A, De Cooman T, Lazeron RH, Van Huffel S, Thijs RD (2019) Ictal autonomic changes as a tool for seizure detection: a systematic review. Clin Auton Res 29(2):161–181 Logesparan AJC Lojini, Rodriguez-Villegas E (2012) Optimal features for online seizure detection. Med Biol Eng Comput 50(7):659–669 Ramgopal S, Thome-Souza S, Jackson M, Kadish NE, Fernández IS, Klehm J, Bosl W, Reinsberger C, Schachter S, Loddenkemper T (2014) Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav 37:291–307 Quintero-Rincón A, D’Giano C, Batatia H (2019) Seizure onset detection in eeg signals based on entropy from generalized gaussian pdf modeling and ensemble bagging classifier. In: Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine, pp. 1–10. Springer, New York Greene B, Faul S, Marnane W, Lightbody G, Korotchikova I, Boylan G (2008) A comparison of quantitative eeg features for neonatal seizure detection. Clin Neurophysiol 119(6):1248–1261 Gotman J, Ives J, Gloor P (1979) Automatic recognition of inter-ictal epileptic activity in prolonged eeg recordings. Electroencephalogr Clin Neurophysiol 46(5):510–520 Gotman J (1982) Automatic recognition of epileptic seizures in the eeg. Electroencephalogr Clin Neurophysiol 54(5):530–540 Koffler D, Gotman J (1985) Automatic detection of spike-and-wave bursts in ambulatory eeg recordings. Electroencephalogr Clin Neurophysiol 61(2):165–180 Shoeb A, Guttag J (2010) Application of machine learning to epileptic seizure detection. In: 2010 the 27th International Conference on Machinelearning, Haifa, Israel Dorai A, Ponnambalam K (2010) Automated epileptic seizure onset detection. In: Autonomous and Intelligent Systems (AIS), 2010 International Conference On, pp. 1–4 . IEEE Lahmiri S (2018) An accurate system to distinguish between normal and abnormal electroencephalogram records with epileptic seizure free intervals. Biomed Sign Process Control 40:312–317 Donos C, Dümpelmann M, Schulze-Bonhage A (2015) Early seizure detection algorithm based on intracranial EEG and random forest classification. Int J Neur Syst 25(05):1550023 Ocak H (2009) Automatic detection of epileptic seizures in eeg using discrete wavelet transform and approximate entropy. Exp Syst Appl 36(2):2027–2036 Thomas E, Temko A, Lightbody G, Marnane W, Boylan G (2011) Advances in automated neonatal seizure detection. New Adv Intell Sign Process 1:93–113 Al Ghayab HR, Li Y, Abdulla S, Diykh M, Wan X (2016) Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain Inform 3(2):85–91 Herwig U, Satrapi P, Schönfeldt-Lecuona C (2003) Using the international 10–20 eeg system for positioning of transcranial magnetic stimulation. Brain Topogr 16(2):95–99 Misulis KE (2013) Atlas of EEG, seizure semiology, and management. Oxford University Press, Oxford Sammut C, Webb GI (2017) Encyclopedia of machine learning and data mining. Springer, New York Adnan MN, Islam MZ (2016) Forest CERN: A new decision forest building technique. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 304–315. Springer, New York Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297 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. https://doi.org/10.1023/A:1010933404324 Arlot S, Celisse A et al (2010) A survey of cross-validation procedures for model selection. Statistics surveys 4:40–79 Kurgan LA, Cios KJ (2004) CAIM discretization algorithm. IEEE Trans Knowl Data Eng 16(2):145–153 Li J, Liu H (2003) Ensembles of cascading trees. In: Data Mining, 2003. ICDM 2003. Third IEEE International Conference On, pp. 585–588. IEEE Powers DM (2011) Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov PC, Mark R, Mietus J, Moody G, Peng C, Stanley H (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220 CHB-MIT Scalp EEG Database. https://physionet.org/pn6/chbmit/. Accessed 20 Jun 2015 Shoeb A, Edwards H, Connolly J, Bourgeois B, Treves ST, Guttag J (2004) Patient-specific seizure onset detection. Epilepsy Behav 5(4):483–498 Kramer MA, Kolaczyk ED, Kirsch HE (2008) Emergent network topology at seizure onset in humans. Epilepsy Res 79(2):173–186 Siddiqui MK, Islam MZ, Kabir MA (2018) A novel quick seizure detection and localization through brain data mining on ecog dataset. Neural Computing and Applications 1–14 Freiburg seizure prediction project. Freiburg, Germany. http://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database (2003) Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys Rev E 64(6):061907 Andrzejak RG, Schindler K, Rummel C (2012) Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys Rev E 86(4):046206 Lee H, Kim S (2016) Black-box classifier interpretation using decision tree and fuzzy logic-based classifier implementation. Int J Fuzzy Logic Intell Syst 16(1):27–35 Adnan MN, Islam MZ (2017) Forex++: A new framework for knowledge discovery from decision forests. Aust J Inform Syst. https://doi.org/10.3127/ajis.v21i0.1539 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(1):101–109 Koolen N, Jansen K, Vervisch J, Matic V, De Vos M, Naulaers G, Van Huffel S (2014) Line length as a robust method to detect high-activity events: automated burst detection in premature eeg recordings. Clin Neurophysiol 125(10):1985–1994 Logesparan L, Rodriguez-Villegas E, Casson AJ (2015) The impact of signal normalization on seizure detection using line length features. Med Biol Eng Comput 53(10):929–942 Song Y, Liò P (2010) A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J Biomed Sci Eng 3(06):556 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. https://doi.org/10.1007/s00521-014-1786-7 Ahmad MA, Khan NA, Majeed W (2014) Computer assisted analysis system of electroencephalogram for diagnosing epilepsy. In: Pattern Recognition (ICPR), 2014 22nd International Conference On, pp. 3386–3391 . IEEE Gill AF, Fatima SA, Akram MU, Khawaja SG, Awan SE (2015) Analysis of eeg signals for detection of epileptic seizure using hybrid feature set. In: Theory and Applications of Applied Electromagnetics, pp. 49–57. Springer Orellana MP, Cerqueira F (2016) Personalized epilepsy seizure detection using random forest classification over one-dimension transformed EEG data. bioRxiv, 070300 Birjandtalab J, Pouyan MB, Cogan D, Nourani M, Harvey J (2017) Automated seizure detection using limited-channel eeg and non-linear dimension reduction. Comput Biol Med 82:49–58 Yan A, Zhou W, Yuan Q, Yuan S, Wu Q, Zhao X, Wang J (2015) Automatic seizure detection using stockwell transform and boosting algorithm for long-term eeg. Epilepsy Behav 45:8–14 Amin HU, Malik AS, Ahmad RF, Badruddin N, Kamel N, Hussain M, Chooi W-T (2015) Feature extraction and classification for eeg signals using wavelet transform and machine learning techniques. Austr Phys Eng Sci Med 38(1):139–149 Mursalin M, Zhang Y, Chen Y, Chawla NV (2017) Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing 241:204–214 Zabihi M, Kiranyaz S, Ince T, Gabbouj M (2013) Patient-specific epileptic seizure detection in long-term eeg recording in paediatric patients with intractable seizures Truong ND, Kuhlmann L, Bonyadi MR, Yang J, Faulks A, Kavehei O (2017) Supervised learning in automatic channel selection for epileptic seizure detection. Exp Syst Appl 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 Inform 12(1):70–89 Chen D, Wan S, Xiang J, Bao FS (2017) A high-performance seizure detection algorithm based on discrete wavelet transform (dwt) and eeg. PLoS ONE 12(3):0173138 Satapathy SK, Jagadev AK, Dehuri S (2017) Weighted majority voting based ensemble of classifiers using different machine learning techniques for classification of eeg signal to detect epileptic seizure. Informatica 41(1):99 Yuan Q, Zhou W, Zhang L, Zhang F, Xu F, Leng Y, Wei D, Chen M (2017) Epileptic seizure detection based on imbalanced classification and wavelet packet transform. Seizure Subasi A, Kevric J, Canbaz MA (2019) Epileptic seizure detection using hybrid machine learning methods. Neural Comput Appl 31(1):317–325 Al Ghayab HR, Li Y, Siuly S, Abdulla S (2019) Epileptic seizures detection in eegs blending frequency domain with information gain technique. Soft Comput 23(1):227–239 Selvakumari RS, Mahalakshmi M, Prashalee P (2019) Patient-specific seizure detection method using hybrid classifier with optimized electrodes. J Med Syst 43(5):121 Chen S, Zhang X, Chen L, Yang Z (2019) Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features. IEEE Access 7:61046–61056 Birjandtalab J, Jarmale VN, Nourani M, Harvey J (2018) Imbalance learning using neural networks for seizure detection. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4 . IEEE Lahmiri S, Shmuel A (2018) Accurate classification of seizure and seizure-free intervals of intracranial eeg signals from epileptic patients. IEEE Trans Instrum Meas 68(3):791–796 Tzimourta KD, Tzallas AT, Giannakeas N, Astrakas LG, Tsalikakis DG, Angelidis P, Tsipouras MG (2019) A robust methodology for classification of epileptic seizures in eeg signals. Health Technol 9(2):135–142 Wang X, Gong G, Li N, Qiu S (2019) Detection analysis of epileptic EEG using a novel random forest model combined with grid search optimization. Front Human Neurosci 13:12 Raghu S, Sriraam N (2018) Classification of focal and non-focal eeg signals using neighborhood component analysis and machine learning algorithms. Exp Syst Appl 113:18–32 Alickovic E, Kevric J, Subasi A (2018) Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Sign Process Contr 39:94–102 Fasil O, Rajesh R (2019) Time-domain exponential energy for epileptic eeg signal classification. Neurosci Lett 694:1–8 Manzouri F, Heller S, Dümpelmann M, Woias P, Schulze-Bonhage A (2018) A comparison of machine learning classifiers for energy-efficient implementation of seizure detection. Front Syst Neurosci 12: 43 Parvez MZ, Paul M (2014) Epileptic seizure detection by analyzing eeg signals using different transformation techniques. Neurocomputing 145:190–200 Hussein R, Elgendi M, Wang ZJ, Ward RK (2018) Robust detection of epileptic seizures based on l1-penalized robust regression of EEG signals. Exp Syst Appl 104:153–167 Mursalin M, Islam SS, Noman MK (2019) Epileptic seizure classification using statistical sampling and a novel feature selection algorithm. arXiv preprint arXiv:1902.09962 Sharma RR, Varshney P, Pachori RB, Vishvakarma SK (2018) Automated system for epileptic EEG detection using iterative filtering. IEEE Sens Lett 2(4):1–4 Tzallas AT, Tsipouras MG (2007) Fotiadis DI (2007) Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intell Neurosci 2007:80510 Siddiqui MK, Islam MZ, Kabir MA (2017) Analyzing performance of classification techniques in detecting epileptic seizure. In: International Conference on Advanced Data Mining and Applications, pp. 386–398. Springer, New York Sanei S, Chambers JA (2013) EEG signal processing. Wiley, New York Chaovalitwongse WA, Prokopyev OA, Pardalos PM (2006) Electroencephalogram (eeg) time series classification: applications in epilepsy. Ann Ope Res 148(1):227–250 Moselhy HF (2011) Psychosocial and cultural aspects of epilepsy. In: Novel Aspects on Epilepsy. InTech Esteller R, Echauz J, Tcheng T, Litt B, Pless B (2001) Line length: an efficient feature for seizure onset detection. In: Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, vol. 2, pp. 1707–1710 . IEEE Guerrero-Mosquera C, Trigueros AM, Franco JI, Navia-Vázquez Á (2010) New feature extraction approach for epileptic eeg signal detection using time-frequency distributions. Med Biol Eng Comput 48(4):321–330 Olsen DE, Lesser RP, Harris JC, Webber WRS, Cristion JA (1994) Automatic detection of seizures using electroencephalographic signals. Google Patents. US Patent 5,311, 876 Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng K-H, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Sign Process Contr 7(4):401–408 Kannathal N, Choo ML, Acharya UR, Sadasivan P (2005) Entropies for detection of epilepsy in eeg. Comput Meth Progr Biomed 80(3):187–194 Kumar Y, Dewal M (2011) Complexity measures for normal and epileptic eeg signals using apen, sampen and sen. IJCCT 2(7):6–12 Shimizu M, Iiya M, Fujii H, Kimura S, Suzuki M, Nishizaki M (2019) Left ventricular end-systolic contractile entropy can predict cardiac prognosis in patients with complete left bundle branch block. Journal of Nuclear Cardiology 1–10 Pal PR, Panda R (2010) Classification of eeg signals for epileptic seizure evaluation. In: Students’ Technology Symposium (TechSym), 2010 IEEE, pp. 72–76. IEEE Cepukenas J, Lin C, Sleeman D (2015) Applying rule extraction and rule refinement techniques to (blackbox) classifiers. In: Proceedings of the 8th international conference on knowledge capture, p. 27. ACM Birjandtalab J, Pouyan MB, Nourani M (2016) Unsupervised eeg analysis for automated epileptic seizure detection. In: Proceedings of the first international workshop on pattern recognition, international society for optics and photonics, pp. 100110–100110 Abualsaud K, Mahmuddin M, Saleh M, Mohamed A (2015) Ensemble classifier for epileptic seizure detection for imperfect eeg data. Sci World J 2015:945689 Chen C, Liu J, Syu J (2012) Application of chaos theory and data mining to seizure detection of epilepsy. Proc Conf. IPCSIT/Hong Kong 25:23–28 Polat K, Güneş S (2007) Classification of epileptiform eeg using a hybrid system based on decision tree classifier and fast fourier transform. Appl Math Comput 187(2):1017–1026 Quinlan R (2004) Data mining tools see5 and c5. 0 Cios KJ, Pedrycz W, Swiniarski RW, Kurgan LA (2007) Data mining: a knowledge discovery approach. Springer, New York Islam MZ, 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, pp. 195–204. Australian Computer Society, Inc Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Patt Analy Mach Intell 20(8):832–844 Siddiqui MK, Islam MZ (2016) Data mining approach in seizure detection. In: 2016 IEEE Region 10 Conference (TENCON), Singapore, pp. 3579–3583. Institute of Electrical and Electronics Engineers (IEEE), https://doi.org/10.1109/tencon.2016.7848724 Hosseini M-P, Pompili D, Elisevich K, Soltanian-Zadeh H (2018) Random ensemble learning for eeg classification. Artif Intell Med 84:146–158 Hassan AR, Siuly S, Zhang Y (2016) Epileptic seizure detection in eeg signals using tunable-q factor wavelet transform and bootstrap aggregating. Comput Meth Prog Biomed 137:247–259 Hosseini M-P, Hajisami A, Pompili D (2016) Real-time epileptic seizure detection from eeg signals via random subspace ensemble learning. In: Autonomic Computing (ICAC), 2016 IEEE International Conference On, pp. 209–218 . IEEE 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. https://doi.org/10.1016/j.neuroimage.2006.09.024 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, pp. 60–63. ACM, New York, NY, USA . https://doi.org/10.1145/2023582.2023593 Keraudren K, Kainz B, Oktay O, Kyriakopoulou V, Rutherford M, Hajnal JV, Rueckert D (2015) Automated localization of fetal organs in mri using random forests with steerable features. In: International conference on medical image computing and computer-assisted Intervention, pp. 620–627. Springer, New York Trans-Cranial-Technologies: 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, pp. 317–322. ACTA Press, Anaheim, CA, USA. http://dl.acm.org/citation.cfm?id=1166506.1166562 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. https://doi.org/10.1109/CEC.2006.1688388 Mansouri A, Singh SP, Sayood K (2019) Online eeg seizure detection and localization. Algorithms 12(9):176 Rai K, Bajaj V, Kumar A (2015) Features extraction for classification of focal and non-focal eeg signals. Inform Sci Appl Lect Notes Electr Eng 339:599–605 Hadoush H, Alafeef M, Abdulhay E (2019) Brain complexity in children with mild and severe autism spectrum disorders: Analysis of multiscale entropy in eeg. Brain topography 1–8 Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181 Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140 Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern C 42(4):463–484 Yang F, Wang H-Z, Mi H, Cai W-W et al (2009) Using random forest for reliable classification and cost-sensitive learning for medical diagnosis. BMC Bioinform 10(1):22 El Saadi H, Al-Sadek AF, Fakhr MW (2012) Informed under-sampling for enhancing patient specific epileptic seizure detection. Int J Comput Appl 57:16 Amin S, Kamboh AM (2016) A robust approach towards epileptic seizure detection. In: Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop On, pp. 1–6 . IEEE Yang Z, Choupan J, Reutens D, Hocking J (2015) Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning. Front Neurol 6:184 Tito M, Cabrerizo M, Ayala M, Jayakar P, Adjouadi M (2009) Seizure detection: an assessment of time-and frequency-based features in a unified two-dimensional decisional space using nonlinear decision functions. J Clin Neurophysiol 26(6):381–391