A robust methodology for classification of epileptic seizures in EEG signals

World Health Organization. Epilepsy. Fact sheet N999, [Online], 2012, Available: http://www.who.int/mediacentre/factsheets/fs999/en/ . Retrieved 15 Feb. 2018.

Sharma AK, Rani E, Waheed A, Rajput SK. Pharmacoresistant epilepsy: a current update on non-conventional pharmacological and non-pharmacological interventions. J Epilepsy Res. 2015;5(1):1–8.

Scheffer IE, Berkovic S, Capovilla G, Connolly MB, French J, Guilhoto L, et al. ILAE classification of the epilepsies: position paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017;58(4):512–21.

Tzallas AT, Tsipouras MG, Tsalikakis DG, Karvounis EC, Astrakas L, Konitsiotis S, Tzaphlidou M. Automated epileptic seizure detection methods: a review study. In Epilepsy-histological, electroencephalographic and psychological aspects. 2012. www.intechopen.com

Sharma RR, Pachori RB. Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals. IET Sci Meas Technol. 2018;12(1):72–82.

Tzallas AT, Tsipouras MG, Fotiadis DI. Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput Intel Neurosci. 2007;2007(18):1–13.

Martis RJ, Acharya UR, Tan JH, Petznick A, Yanti R, Chua CK, et al. Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals. Int J Neural Syst. 2012;22:6.

Bhardwaj A, Tiwari A, Krishna R, Varma V. A novel genetic programming approach for epileptic seizure detection. Comput Methods Prog Biomed. 2016;124:2–18.

Zhang T, Chen W. LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE Trans Neur Syst Rehab Eng. 2017;25(8):1100–8.

Kumar Y, Dewal ML, Anand RS. Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. SIViP. 2014;8(7):1323–34.

Gajic D, Djurovic Z, Di Gennaro S, Gustafsson F. Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomedical engineering: applications. Basis Commun. 2014;26:2.

Li D, Xie Q, Jin Q, Hirasawa K. A sequential method using multiplicative extreme learning machine for epileptic seizure detection. Neurocomputing. 2014;214:692–707.

Acharya UR, Sree SV, Alvin APC, Suri JS. Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Expert Syst Appl. 2012;39(10):9072–8.

Dhiman R, Saini JS. Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures. Appl Soft Comput. 2014;19:8–17.

Chen G. Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Syst Appl. 2014;41(5):2391–4.

Das AB, Bhuiyan MIH, Alam SS. Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. SIViP. 2014;10(2):259–66.

Patidar S, Panigrahi T. Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals. Biomed Signal Proc Control. 2017;34:74–80.

Sharma M, Pachori RB. A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension. J Mech Med Biol. 2017;17:7.

Wang Y, Zhou W, Yuan Q, Li X, Meng Q, Zhao X, et al. Comparison of ictal and interictal EEG signals using fractal features. Int J Neural Syst. 2013;23:6.

Nicolaou N, Georgiou J. Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl. 2012;39(1):202–9.

Raghunathan S, Jaitli A, Irazoqui PP. Multistage seizure detection techniques optimized for low-power hardware platforms. Epilepsy Behav. 2011;22:S61–8.

Liu Y, Zhou W, Yuan Q, Chen S. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Trans Neur Syst Rehab Eng. 2012;20(6):749–55.

Yuan S, Zhou W, Yuan Q, Zhang Y, Meng Q. Automatic seizure detection using diffusion distance and BLDA in intracranial EEG. Epilepsy Behav. 2014;31:339–45.

Yuan S, Zhou W, Wu Q, Zhang Y. Epileptic seizure detection with log-euclidean Gaussian kernel-based sparse representation. Int J Neural Syst. 2016;26:3.

Zheng YX, Zhu JM, Qi Y, Zheng XX, Zhang JM. An automatic patient-specific seizure onset detection method using intracranial electroencephalography. Neuromodulation: Technol Neural Interface. 2015;18(2):79–84.

Zhang Y, Zhou W, Yuan S. Multifractal analysis and relevance vector machine-based automatic seizure detection in intracranial EEG. Int J Neural Syst. 2015;25:6.

Geng D, Zhou W, Zhang Y, Geng S. Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG. Biocybernet Biomed Eng. 2016;36(2):375–84.

Kevric J, Subasi A. The effect of multiscale PCA de-noising in epileptic seizure detection. J Med Syst. 2014;38(10):131.

Xie S, Krishnan S. Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis. Med Biol Eng Comput. 2013;51:1–2 49-60.

Xie S, Krishnan S. Dynamic principal component analysis with nonoverlapping moving window and its applications to epileptic EEG classification. Sci World J. 2014.

Parvez MZ, Paul M. Epileptic seizure detection by analyzing EEG signals using different transformation techniques. Neurocomputing. 2014;145:190–200.

Bajaj V, Pachori RB. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed. 2011;16(6):1135–42.

Parvez MZ, Paul M. Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals. IET Signal Proc. 2015;9(6):467–75.

Mallat S. A wavelet tour of signal processing. 2nd ed. San Diego: Academic press; 1999.

Tzimourta KD, Astrakas LG, Tsipouras MG, Giannakeas N, Tzallas AT, Konitsiotis S. Wavelet based classification of epileptic seizures in EEG signals. In : 2017 IEEE 30th international symposium on computer-based medical systems (CBMS). IEEE. 2017:35–9.

Tzimourta KD, Tsilimbaris A, Tzioukalia K, Tzallas AT, Tsipouras MG, Astrakas LG, et al. EEG-based automatic sleep stage classification. Biomed J Sci Techn Res (BJSTR). 2018;7(4):1–6.

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

Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. 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. 2001;64:6.

Tzallas AT, Tsipouras MG, Fotiadis DI. Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans Inf Technol Biomed. 2009;13(5):703–10.

Freiburg seizure prediction project. Freiburg, Germany. 2008. [Online]. http://epilepsy.uni-freburg.de/freiburg-seizure-prediction- project/eeg-database

Zhou W, Liu Y, Yuan Q, Li X. Epileptic seizure detection using lacunarity and Bayesian linear discriminant analysis in intracranial EEG. IEEE Trans Biomed Eng. 2013;60(12):3375–81.

Alickovic E, Kevric J, Subasi A. Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Sign Proc Control. 2018;39:94–102.

Nagaraj V, Lee S, Krook-Magnuson E, Soltesz I, Benquet P, Irazoqui P, et al. The future of seizure prediction and intervention: closing the loop. J Clin Neurophysiol : Off Publ Am Electroencephal Soc. 2015;32(3):194–206.