Most of the recent research has explored the possibility of predicting & analyzing epileptic seizures by using different techniques & methods. Epilepsy is the second most common neurological disorder which affects people of all ages i.e. about 1-2% of the world’s population affected by this major chronic disorder. The Electroencephalogram (EEG) signal is used as a useful tool for the early detection of epileptic seizures in several applications of epilepsy diagnosis. Many techniques have been developed for differentiate the features of seizures present in EEGs. This article reviews the seizure detection techniques & methods reported in last decade/years. However, there are various techniques like Empirical mode decomposition (EMD), wavelet transform, tensors, entropy, chaos theory, and dynamic analysis which are used in the area of epilepsy diagnosis. For better treatment of the patients it is important that the seizures are detected correctly in time. Although efforts have been made for better prediction of the seizures, the translation of current analysis & results to clinical applications is still not possible. We have reviewed a framework of reliable algorithmic seizure prediction studies, discussing each component of the whole block diagram. We have also explored all the processes, from signal acquisition to adequate performance evaluation that should be opted in the designing of an efficient seizure advisory/intervention system. The present review has established that there is a potential for improvement and optimization in the seizure prediction framework.

Epilepsy; Seizure detection algorithm; Signal processing; Feature Extraction; Classification; Performance

[1] World Health Organization, 2011.Available: http://www.who.int/mediacentre/?factsheets/fs999/en
[2] J. Gotman, “Automatic detection of seizures and spikes,” J. Clin. Neurophysiol., 16: 130-140, 1999.
[3] J. C. Sackellares, “Seizure prediction,” Epilepsy Curr., 8: 55-59, 2008.
[4] A. G. Stein, H. G. Eder, D. E. Blum, A. Drachev and R. S. Fisher,“An automated drug delivery system for focal epilepsy,” Epilepsy Res., 39: 103-114, 2000.
[5] Elie Bou Assi, Dang K. Nguyen, Sandy Rihana and Mohamad Sawan, Towards accurate prediction of epileptic seizures: A review, Biomedical Signal Processing and Control 34, 2017.
[6] S.S. Spencer, D.K. Nguyen, R.B. Duckrow, Invasive EEG in presurgicalevaluation of epilepsy, in: The Treatment of Epilepsy, Wiley-Blackwell, 2009,pp. 767–798.
[7] C. Alexandre Teixeira, B. Direito, M. Bandarabadi, M. Le Van Quyen, M.Valderrama, B. Schelter, A. Schulze-Bonhage, V. Navarro, F. Sales, A. Dourado,Epileptic seizure predictors based on computational intelligence techniques:a comparative study with 278 patients, Comput. Methods Progr. Biomed. 114(2014) 324–336.
[8] L. Sörnmo and P. Laguna, Bioelectrical signal processing in cardiac and neurological applications, USA: Elsevier, 2005.
[9] S. Tong and N. V. Thakor (Ed.), Quantitative EEG Analysis Methods and Clinical Applications, Norwood: Artech House, 2009.
[10] M. Bandarabadi, C.A. Teixeira, J. Rasekhi, A. Dourado, Epileptic seizure prediction using relative spectral power features, Clin. Neurophysiol. 126(2015) 237–248.
[11] J. Rasekhi, M.R.K. Mollaei, M. Bandarabadi, C.A. Teixeira, A. Dourado, Preprocessing effects of 22 linear univariate features on the performance ofseizure prediction methods, J. Neurosci. Methods 217 (2013) 9–16.
[12] E. Acar, C. A. Bingol, H. Bingol, R. Bro and B. Yener, “Multiway analysis of epilepsy tensors,” Bioinformatics, 23:i10-i18, 2007.
[13] Y. Park, L. Luo, K.K. Parhi, T. Netoff, Seizure prediction with spectral power of EEG using cost-sensitive support vector machines, Epilepsia 52 (2011)1761–1770.
[14] L.Orosco, A.G.Correa and E.Lacier, Review:A survey of performance and techniques for automatic epilepsy detection, Journal of Medical and Biological Engineering, 2013.
[15] A. Eftekhar, W. Juffali, J. El-Imad, T.G. Constandinou, C. Toumazou,Ngram-derived pattern recognition for the detection and prediction of epileptic seizures, PLoS One 9 (2014) e96235.
[16] J. Klatt, H. Feldwisch-Drentrup, M. Ihle, V. Navarro, M. Neufang, C. Teixeira, C.Adam, M. Valderrama, C. Alvarado-Rojas, A. Witon, M. Le Van Quyen, F. Sales,A. Dourado, J. Timmer, A. Schulze-Bonhage, B. Schelter, The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients, Epilepsia 53 (2012) 1669–1676.
[17] M.J. Cook, T.J. O’Brien, S.F. Berkovic, M. Murphy, A. Morokoff, G. Fabinyi, W.D’Souza, R. Yerra, J. Archer, L. Litewka, S. Hosking, P. Lightfoot, V. Ruedebusch,W.D. Sheffield, D. Snyder, K. Leyde, D. Himes, Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients withdrug-resistant epilepsy: a first-in-man study, Lancet Neurol. 12 (2013)563–571.
[18] B.H. Brinkmann, E.E. Patterson, C. Vite, V.M. Vasoli, D. Crepeau, M. Stead, J.J.Howbert, V. Cherkassky, J.B. Wagenaar, B. Litt, G.A. Worrell, Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy, PLoS One 10 (2015).
[19] A. Varsavsky, I., Mareels and M. Cook, Epileptic seizures and the EEG, Boca Raton: CRC Press, 2011.
[20] A. Garcés Correa, E. Laciar, H. D. PatiñoandM. E. Valentinuzzi, “Artifact removal from EEG signals using adaptive filters in cascade,” J. Phys. Conf. Ser., 90: 012081.
[21] I. Güler and I. G. Übeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,” J.Neurosci. Methods, 148: 113-121, 2005.
[22] A. Subasi, “EEG signal classification using wavelet feature extraction and a mixture of expert model,” Expert Syst. Appl., 32: 1084-1093, 2007.
[23] K. Polat and S. Günes, “Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform,” Appl. Math. Comput., 187: 1017-1026, 2007.
[24] A. M. Chan, F. T. Sun, E. H. Boto and B. M. Wingeier, “Automated seizure onset detection for accurate onset Time Determination in Intracranial EEG,” Clin. Neurophysiol.119: 2687-2696, 2008.
[25] A. Aarabi, R. Fazel-Rezai and Y. Aghakhani, “A fuzzy rule-based system for epileptic seizure detection in intracranial EEG,” Clin. Neurophysiol., 120: 1648-1657, 2009.
[26] R. Meier, H. Dittrich, A. Schulze-Bonhage and A. Aertsen, “Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and real-time detection and classification of polymorphic seizure patterns,” J. Clin. Neurophysiol., 25: 119-131, 2008.
[27] G. R. Minasyan, J. B. Chatten and R. N. Harner, “Patient-specific early seizure detection from scalp EEG,” J. Clin. Neurophysiol., 27: 163-178, 2010.
[28] Q. Yuan, W. Zhou, S. Li and D. Cai, “Epileptic EEG classification based on extreme learning machine and nonlinear features,” Epilepsy Res., 96: 29-38, 2011.
[29] N. Kannathal, M. L. Choob, U. R. Acharya and P.K Sadasivana, “Entropies for detection of epilepsy in EEG,” Comput. Meth. Programs Biomed., 80: 187-194, 2005.
[30] A. J. Gabor, R. R. Leach and F. U. Dowla, “Automated seizure detection using a self-organizing neural network,” Electroencephalogr. Clin. Neurophysiol., 99: 257-266, 1996.
[31] A. T. Tzallas, M. G. Tsipouras and D. I. Fotiadis, “Automaticseizure detection based on time-frequency analysis and artificial neural networks,” Comput. Intell. Neurosci., 80510, 2007.
[32] B. Abibullaev, M. S. Kim and H. D. Seo, “Seizure detection in temporal lobe Epileptic EEGs using the best basis Wavelet functions,” J. Med. Syst., 34: 755-765, 2010.
[33] J. Mitra, J. R. Glover, P. Y. Ktonas, A. T. Kumar, A. Mukherjee, N. B.Karayiannis, J. D. Frost Jr., R. A. Hrachovy and E. M. Mizrahi, “A multi-stage system for the automated detection of epileptic seizures in neonatal EEG,” J. Clin. Neurophysiol., 26: 218-226, 2009.
[34] E. D. Übeyli, “Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks,” Digit. Signal Prog., 19: 134-143, 2009.
[35] H. Adeli and S. Ghosh-Dastidar, Automated EEG-based diagnosis of neurological disorders-inventing the future of neurology, Florida: CRC press, 2010.
[36] G. R. Minasyan, J. B. Chatten and R. N. Harner, “Patient-specific early seizure detection from scalp EEG,” J. Clin. Neurophysiol., 27: 163-178, 2010.
[37] R. Meier, H. Dittrich, A. Schulze-Bonhage and A. Aertsen, “Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and real-time detection and classification of polymorphic seizure patterns,” J. Clin. Neurophysiol., 25: 119-131, 2008.
[38] J. Gotman, “Automatic recognition of epileptic seizures in the EEG,” Electroencephalogr. Clin. Neurophysiol.54: 530-540, 1982.
[39] J. Gotman, “Automatic seizure detection: Improvements and evaluation,” Electroencephalogr. Clin. Neurophysiol.,76: 317-324, 1990.
[40] U. R. Acharya, S. V. Sree and J. S. Suri, “Automatic detection of epileptic EEG signals using higher order cumulant features,” Int. J. Neural. Syst., 21: 403-414, 2011.
[41] K. C. Chua, V. Chandran, U. R. Acharya and C. M. Lim, “Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study,” Conf. Proc. IEEE Eng. Med. Biol. Soc., 3824-3827, 2008.
[42] K. C. Chua, V. Chandran, U. R. Acharya and C. M. Lim, “Application of higher order spectra to identify epileptic EEG,” J. Med. Syst., 35:1563-1571, 2011.
[43] X. Du, S. Dua, U. R. Acharya and K. C. Chua, “Classification of epilepsy using high-order spectra features and principle component analysis,” J. Med. Syst.,36: 1731-1743, 2012.
[44] A. Aarabi, F. Wallois and R. Grebe, “Automated neonatal seizure detection: A multistage classification system through feature selection based on relevance and redundancy analysis,” Clin. Neurophysiol., 117: 328-340, 2006.
[45] L. Orosco, A. Garcés Correa and E. Laciar Leber, “Epileptic seizures detection based on empirical mode decomposition of EEG signals,” in: Prof. Mintaze Kerem Günel (Ed.), Management of Epilepsy-Research, Results and Treatment, 3-20, Austria: IN-TECH Publishing, 2011.
[46] A. J. Gabor, “Seizure detection using a self-organizing neural network: validation and comparison with other detection strategies,” Electroencephalogr. Clin. Neurophysiol.,107: 27-32, 1998.
[47] J. L. Semmlow, Biosignal and biomedical image processing, New York: Marcel Dekker, 2004.
[48] A. Subasi, “Epileptic seizure detection using dynamic wavelet network,” Expert Syst. Appl., 29: 343-355, 2005.
[49] A. Subasi, “Automatic detection of epileptic seizure using dynamic fuzzy neural networks,” Expert Syst. Appl., 31: 320-328, 2006.
[50] Y. U. Khan, O. Farooq and P. Sharma, “Automatic detection of seizure onset in pediatric EEG,” Int. J. Emb. Syst.Appl., 2: 81-89, 2012.
[51] M. E. Saab and J. Gotman, “A system to detect the onset of epileptic seizures in scalp EEG,” Clin. Neurophysiol.,116: 427-442, 2005.
[52] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu,H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A, 454: 903-995, 1998.
[53] P. Grassberger and T. Schrieber, “Nonlinear time sequence analysis,”Int. J. Bifurcation Chaos, 1: 512-547, 1991.
[54] S. M. Pincus, “Approximate entropy (ApEn) as a complexity measure,”Int. J. Bifurcation Chaos, 5: 110-117, 1991.
[55] H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, “A Wavelet-Chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy,” IEEE Trans. Biomed. Eng., 54: 205-211, 2007.
[56] N. Moghim, D.W. Corne, Predicting epileptic seizures in advance, PLoS One 9(2014).
[57] M. Bandarabadi, C. Teixeira, B. Direito, A. Dourado, Epileptic seizureprediction based on a bivariate spectral power methodology, in: AnnualInternational Conference of the IEEE Engineering in Medicine and BiologySociety, IEEE, San Diego, CA, 2012, pp. 5943–5946.
[58] H. Peng, L. Fulmi, C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans.Pattern Anal. Mach. Intell. 27 (2005) 1226–1238.
[59] B. Direito, F. Ventura, C. Teixeira, A. Dourado, Optimized feature subsets forepileptic seizure prediction studies, Engineering in Medicine and BiologySociety, EMBC, 2011 Annual International Conference of the IEEE (2011)1636–1639.
[60] P. Ataee, A. Yazdani, S.K. Setarehdan, H.A. Noubari, Genetic algorithm for selection of best feature and window length for a discriminate pre-seizure and normal state classification, in: 5th International Symposium on Imageand Signal Processing and Analysis, Istabul Turkey, 2007, pp. 107–112.
[61] C.A. Teixeira, B. Direito, H. Feldwisch-Drentrup, M. Valderrama, R.P. Costa, C.Alvarado-Rojas, S. Nikolopoulos, M. Le Van Quyen, J. Timmer, B. Schelter, A.Dourado, EPILAB: a software package for studies on the prediction of epileptic seizures, J. Neurosci. Methods 200 (2011) 257–271.
[62] N.V. Chawla, N. Japkowicz, A. Kotcz, Editorial: special issue on learning fromimbalanced data sets, SIGKDD Explor. Newslett. 6 (2004) 1–6.
[63] R. Costa, P. Oliveira, G. Rodrigues, B. Leitão, A. Dourado, Epileptic seizureclassification using neural networks with 14 features, in: I. Lovrek, R. Howlett,L. Jain (Eds.), Knowledge-Based Intelligent Information and EngineeringSystems, Springer, Berlin Heidelberg, 2008, pp. 281–288.
[64] P. Mirowski, D. Madhavan, Y. Lecun, R. Kuzniecky, Classification of patterns ofEEG synchronization for seizure prediction, Clin. Neurophysiol. 120 (2009).
[65] L. Chisci, A. Mavino, G. Perferi, M. Sciandrone, C. Anile, G. Colicchio, F.Fuggetta, Real-time epileptic seizure prediction using AR models and supportvector machines, IEEE Trans. Biomed. Eng. 57 (2010) 1124–1132.
[66] C. Teixeira, B. Direito, M. Bandarabadi, A. Dourado, Output regularization ofSVM seizure predictors: Kalman Filter versus the Firing Power method, in:Annual International Conference of the IEEE Engineering in Medicine andBiology Society, IEEE, San Diego, CA, 2012.
[67] F. Mormann, R.G. Andrzejak, C.E. Elger, K. Lehnertz, Seizure prediction: the long and winding road, Brain 130 (2007) 314–333.
[68] F. Mormann, T. Kreuz, C. Rieke, R.G. Andrzejak, A. Kraskov, P. David, C.E. Elger,K. Lehnertz, On the predictability of epileptic seizures, Clin. Neurophysiol. 116(2005) 569–587.
[69] F. Mormann, R. Andrzejak, T. Kreuz, P. David, C.E. Elger, K. Lehnertz,Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients, Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 67 (2003).
[70] F. Mormann, T. Kreuz, R.G. Andrzejak, P. David, K. Lehnertz, C.E. Elger, Epileptic seizures are preceded by a decrease in synchronization, EpilepsyRes. 53 (2003) 173–185.
[71] J.R. Williamson, D.W. Bliss, D.W. Browne, J.T. Narayanan, Seizure prediction using EEG spatiotemporal correlation structure, Epilepsy Behav. 25 (2012)230–238.
[72] S. Sanei and J. Chambers, EEG signal processing, Chichester: Wiley, 2007.
[73] E.B. Assi, M. Sawan, D.K. Nguyen, S. Rihana, A hybrid mRMR-genetic basedselection method for the prediction of epileptic seizures, Biomedical Circuitsand Systems Conference (BioCAS), IEEE (2015) 1–4.
[74] J.J. Niederhauser, R. Esteller, J. Echauz, G. Vachtsevanos, B. Litt, Detection ofseizure precursors from depth-EEG using a sign periodogram transform, IEEETrans. Biomed. Eng. 50 (2003) 449–458.
[75] M. Le Van Quyen, J. Martinerie, M. Baulac, F. Varela, Anticipating epilepticseizures in real time by a non-linear analysis of similarity between EEG recordings, Neuroreport 10 (1999) 2149–2155.
[76] J.J. Howbert, E.E. Patterson, S.M. Stead, B. Brinkmann, V. Vasoli, D. Crepeau,C.H. Vite, B. Sturges, V. Ruedebusch, J. Mavoori, K. Leyde, W.D. Sheffield, B.Litt, G.A. Worrell, Forecasting seizures in dogs with naturally occurringepilepsy, PLoS One 9 (2014) e81920.
[77] B. Schelter, M. Winterhalder, T. Maiwald, A. Brandt, A. Schad, A.Schulze-Bonhage, J. Timmer, Testing statistical significance of multivariatetime series analysis techniques for epileptic seizure prediction, Chaos 16(2006) 1054–1500, Print.
[78] B. Schelter, M. Winterhalder, T. Maiwald, A. Brandt, A. Schad, J. Timmer, A.Schulze-Bonhage, Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposedremedies, Epilepsia 47 (2006) 2058–2070.
[79] M.T. Salam, M. Mirzaei, M. Ly, D.K. Nguyen, M. Sawan, An implantable closed loop asynchronous drug delivery system for the treatment of refractory epilepsy, IEEE Trans. Neural Syst. Rehabil. Eng. 20 (2012).
[80] W. Theodore and R. S. Fisher, “Brain stimulation for epilepsy,” Lancet Neurol., 3: 111-118, 2004.
[81] K. Schindler, C. E. Elger and K. Lehnertz, “Changes of EEG synchronization during low-frequency electric stimulation of the seizure onset zone,” Epilepsy Res., 77: 108-119, 2007.
[82] I. Osorio and M. Frei, “Real-time detection, quantification, warning, and control of epileptic seizures: The foundations for a scientific epileptology,” Epilepsy Behav., 16: 391-396, 2009.
[83] L. D. Iasemidis, “Epileptic seizure prediction and control,” IEEE Trans. Biomed. Eng., 50: 549-558, 2003.