[1] Ko DY, Benbadis SR, Passaro EA. Epileptiform discharges[EB/OL]. [2018-04-02]. https://emedicine.medscape.com/article/1138880-overview.
[2] Shafer PO, Sirven JI. Epilepsy statistics[EB/OL]. [2019-01-18]. http://www.epilepsy.com/learn/epilepsy-statistics.
[3] Zhang ZT, Telesford QK, Giusti C, et al. Choosing wavelet methods, filters, and lengths for functional brain network construction[J]. PLoS One, 2016, 11(6): e0157243. doi:  10.1371/journal.pone.0157243
[4] Chen D, Wan SR, Bao FS. Epileptic focus localization using discrete wavelet transform based on interictal intracranial EEG[J]. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(5): 413–425. doi:  10.1109/TNSRE.2016.2604393
[5] Hopfengärtner R, Kasper BS, Graf W, et al. Automatic seizure detection in long-term scalp EEG using an adaptive thresholding technique: a validation study for clinical routine[J]. Clin Neurophysiol, 2014, 125(7): 1346–1352. doi:  10.1016/j.clinph.2013.12.104
[6] Lima CAM, Coelho ALV, Chagas S. Automatic EEG signal classification for epilepsy diagnosis with relevance vector machines[J]. Expert Syst Appl, 2009, 36(6): 10054–10059. doi:  10.1016/j.eswa.2009.01.022
[7] Übeyli ED. Combined neural network model employing wavelet coefficients for EEG signals classification[J]. Digit Signal Process, 2009, 19(2): 297–308. doi:  10.1016/j.dsp.2008.07.004
[8] Magosso E, Ursino M, Zaniboni A, et al. A wavelet-based energetic approach for the analysis of biomedical signals: application to the electroencephalogram and electro-oculogram[J]. Appl Math Comput, 2009, 207(1): 42–62.
[9] Acharya UR, Sree SV, Alvin APC, et al. Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework[J]. Expert Syst Appl, 2012, 39(10): 9072–9078. doi:  10.1016/j.eswa.2012.02.040
[10] Lima CAM, Coelho ALV. Kernel machines for epilepsy diagnosis via EEG signal classification: a comparative study[J]. Artif Intell Med, 2011, 53(2): 83–95. doi:  10.1016/j.artmed.2011.07.003
[11] Raghu S, Sriraam N, Kumar GP. Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier[J]. Cogn Neurodyn, 2017, 11(1): 51–66. doi:  10.1007/s11571-016-9408-y
[12] Kumar Y, Dewal ML, Anand RS. Epileptic seizures detection in EEG using DWT-based apen and artificial neural network[J]. Signal Image Video Process, 2014, 8(7): 1323–1334. doi:  10.1007/s11760-012-0362-9
[13] Liu YX, Zhou WD, Yuan Q, et al. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG[J]. IEEE Trans Neural Syst Rehabil Eng, 2012, 20(6): 749–755. doi:  10.1109/TNSRE.2012.2206054
[14] Raghu S, Sriraam N, Kumar GP, et al. A novel approach for real-time recognition of epileptic seizures using minimum variance modified fuzzy entropy[J]. IEEE Trans Biomed Eng, 2018, 65(11): 2612–2621. doi:  10.1109/TBME.2018.2810942
[15] Acharya UR, Molinari F, Sree SV, et al. Automated diagnosis of epileptic EEG using entropies[J]. Biomed Signal Process Control, 2012, 7(4): 401–408. doi:  10.1016/j.bspc.2011.07.007
[16] Tawfik NS, Youssef SM, Kholief M. A hybrid automated detection of epileptic seizures in EEG records[J]. Comput Electr Eng, 2016, 53: 177–190. doi:  10.1016/j.compeleceng.2015.09.001
[17] Raghu S, Sriraam N. Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures[J]. Expert Syst Appl, 2017, 89: 205–221. doi:  10.1016/j.eswa.2017.07.029
[18] Kumar SP, Sriraam N, Benakop PG, et al. Entropies based detection of epileptic seizures with artificial neural network classifiers[J]. Expert Syst Appl, 2010, 37(4): 3284–3291. doi:  10.1016/j.eswa.2009.09.051
[19] Srinivasan V, Eswaran C, Sriraam N. Artificial neural network based epileptic detection using time-domain and frequency-domain features[J]. J Med Syst, 2005, 29(6): 647–660. doi:  10.1007/s10916-005-6133-1
[20] Wang D, Miao DQ, Xie C. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection[J]. Expert Syst Appl, 2011, 38(11): 14314–14320.
[21] Acharya UR, Fujita H, Sudarshan VK, et al. Application of entropies for automated diagnosis of epilepsy using EEG signals: a review[J]. Knowl-Based Syst, 2015, 88: 85–96. doi:  10.1016/j.knosys.2015.08.004
[22] Sorokin JM, Paz JT, Huguenard JR. Absence seizure susceptibility correlates with pre-ictal β oscillations[J]. J Physiol Paris, 2016, 110(4): 372–381. doi:  10.1016/j.jphysparis.2017.05.004
[23] Li MY, Chen WZ, Zhang T. Application of MODWT and log-normal distribution model for automatic epilepsy identification[J]. Biocybern Biomed Eng, 2017, 37(4): 679–689. doi:  10.1016/j.bbe.2017.08.003
[24] Ouyang GX, Li XL, Li Y, et al. Application of wavelet-based similarity analysis to epileptic seizures prediction[J]. Comput Biol Med, 2007, 37(4): 430–437. doi:  10.1016/j.compbiomed.2006.08.010
[25] Amini L, Jutten C, Achard S, et al. Directed epileptic network from scalp and intracranial EEG of epileptic patients[C]//Proceedings of 2009 IEEE International Workshop on Machine Learning for Signal Processing. Grenoble, France: IEEE, 2009: 1–6.
[26] Juárez-Guerra E, Alarcon-Aquino V, Gómez-Gil P. Epilepsy seizure detection in EEG signals using wavelet transforms and neural networks[M]//Elleithy K, Sobh T. New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering. Cham: Springer, 2015: 261–269.
[27] Srinivasan V, Eswaran C, Sriraam N. Approximate entropy-based epileptic EEG detection using artificial neural networks[J]. IEEE Trans Inf Technol Biomed, 2007, 11(3): 288–295. doi:  10.1109/TITB.2006.884369
[28] Andrzejak RG, Lehnertz K, Mormann F, et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state[J]. Phys Rev E, 2001, 64(6): 061907.
[29] Percival DB, Mofjeld HO. Analysis of subtidal coastal sea level fluctuations using wavelets[J]. J Am Stat Assoc, 1997, 92(439): 868–880. doi:  10.1080/01621459.1997.10474042
[30] Percival DB, Walden AT. Wavelet methods for time series analysis. Cambridge series in statistical and probabilistic mathematics[M]. Cambridge: Cambridge University Press, 2000.
[31] Kuhlmann L, Burkitt AN, Cook MJ, et al. Seizure detection using seizure probability estimation: comparison of features used to detect seizures[J]. Ann Biomed Eng, 2009, 37(10): 2129–2145. doi:  10.1007/s10439-009-9755-5
[32] Shoeb A, Edwards H, Connolly J, et al. Patient-specific seizure onset detection[J]. Epilepsy Behav, 2004, 5(4): 483–498. doi:  10.1016/j.yebeh.2004.05.005
[33] Shoeb A, Guttag J. Application of machine learning to epileptic seizure detection[C]//Proceedings of the 27th International Conference on Machine Learning. Haifa, 2010: 975–982.
[34] Urigüen JA, Garcia-Zapirain B. EEG artifact removal-state-of-the-art and guidelines[J]. J Neural Eng, 2015, 12(3): 031001. doi:  10.1088/1741-2560/12/3/031001
[35] Faust O, Acharya UR, Adeli H, et al. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis[J]. Seizure, 2015, 26: 56–64. doi:  10.1016/j.seizure.2015.01.012
[36] Raghu S, Sriraam N, Temel Y, et al. Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier[J]. Comput Biol Med, 2019, 110: 127–143. doi:  10.1016/j.compbiomed.2019.05.016
[37] Mateo J, Rieta JJ. Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings[J]. J Med Eng Technol, 2012, 36(2): 90–101. doi:  10.3109/03091902.2011.636859
[38] Shannon CE. A mathematical theory of communication[J]. ACM SIGMOBILE Mobile Comput Commun Rev, 2001, 5(1): 3–55. doi:  10.1145/584091.584093
[39] Coifman RR, Wickerhauser MV. Entropy-based algorithms for best basis selection[J]. IEEE Trans Inf Theory, 1992, 38(2): 713–718. doi:  10.1109/18.119732
[40] Renyi A. On measures of entropy and information[C]//Proceedings of the 4th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1961: 547–561.
[41] Tsallis C. Possible generalization of boltzmann-gibbs statistics[J]. J Stat Phys, 1988, 52(1–2): 479–487.
[42] Gupta V, Priya T, Yadav AK, et al. Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform[J]. Pattern Recognit Lett, 2017, 94: 180–188. doi:  10.1016/j.patrec.2017.03.017
[43] Raghu S, Sriraam N, Hegde AS, et al. A novel approach for classification of epileptic seizures using matrix determinant[J]. Expert Syst Appl, 2019, 127: 323–341. doi:  10.1016/j.eswa.2019.03.021
[44] Sriraam N, Raghu S. Classification of focal and non focal epileptic seizures using multi-features and SVM classifier[J]. J Med Syst, 2017, 41(10): 160.
[45] Raghu S, Sriraam N. Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms[J]. Expert Syst Appl, 2018, 113: 18–32. doi:  10.1016/j.eswa.2018.06.031
[46] Vidyaratne LS, Iftekharuddin KM. Real-time epileptic seizure detection using EEG[J]. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(11): 2146–2156. doi:  10.1109/TNSRE.2017.2697920
[47] Zandi AS, Javidan M, Dumont GA, et al. Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform[J]. IEEE Trans Biomed Eng, 2010, 57(7): 1639–1651. doi:  10.1109/TBME.2010.2046417
[48] Saab ME, Gotman J. A system to detect the onset of epileptic seizures in scalp EEG[J]. Clin Neurophysiol, 2005, 116(2): 427–442. doi:  10.1016/j.clinph.2004.08.004