D. L. Schomer and F. H. Da-silva, Niedermeyer's Electroencephalography Basic Principles, Clinical Applications, and Related Fields, 2010.

R. A. Bergstrom, J. H. Choi, A. Manduca, H. Shin, G. A. Worrell et al., Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice, Scientific Reports, vol.3, issue.1, 2013.

R. Bhuyan, W. Jahan, and N. Upadhyaya, Interictal wave pattern study in EEG of epilepsy patients, International Journal of Research in Medical Sciences, vol.5, issue.8, p.3378, 2017.

A. Quintero-rincón, M. Pereyra, C. D?giano, H. Batatia, and M. Risk, A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals, Journal of Physics: Conference Series, vol.705, p.012032, 2016.

D. Gajic, Z. Djurovic, S. Di-gennaro, and F. Gustafsson, CLASSIFICATION OF EEG SIGNALS FOR DETECTION OF EPILEPTIC SEIZURES BASED ON WAVELETS AND STATISTICAL PATTERN RECOGNITION, Biomedical Engineering: Applications, Basis and Communications, vol.26, issue.02, p.1450021, 2014.

M. A. Navakatikyan, P. B. Colditz, C. J. Burke, T. E. Inder, J. Richmond et al., Seizure detection algorithm for neonates based on wave-sequence analysis, Clinical Neurophysiology, vol.117, issue.6, pp.1190-1203, 2006.

S. Siuly, E. Kabir, H. Wang, and Y. Zhang, Exploring Sampling in the Detection of Multicategory EEG Signals, Computational and Mathematical Methods in Medicine, vol.2015, pp.1-12, 2015.

A. Subasi and E. Erçelebi, Classification of EEG signals using neural network and logistic regression, Computer Methods and Programs in Biomedicine, vol.78, issue.2, pp.87-99, 2005.

A. Quintero-rincon, J. Prendes, V. Muro, and C. D'giano, Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier, 2017 IEEE URUCON, vol.21, pp.1-4, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02134635

C. Donos, M. Dümpelmann, and A. Schulze-bonhage, Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification, International Journal of Neural Systems, vol.25, issue.05, p.1550023, 2015.

R. Fu, Y. Tian, P. Shi, and T. Bao, Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm, Journal of Medical Systems, vol.44, issue.2, pp.1-13, 2020.

A. Ossadtchi, R. E. Greenblatt, V. L. Towle, M. H. Kohrman, and K. Kamada, Inferring spatiotemporal network patterns from intracranial EEG data, Clinical Neurophysiology, vol.121, issue.6, pp.823-835, 2010.

S. B. Wilson and R. Emerson, Spike detection: a review and comparison of algorithms, Clinical Neurophysiology, vol.113, issue.12, pp.1873-1881, 2002.

P. V. Hese, J. Martens, L. Waterschoot, P. Boon, and I. Lemahieu, Automatic detection of spike and wave discharges in the EEG of genetic absence epilepsy rats from Strasbourg, IEEE Trans. Biomed. Eng, vol.56, pp.706-717, 2009.

P. S. Pearce, D. Friedman, J. J. Lafrancois, S. S. Iyengar, A. A. Fenton et al., Spike?wave discharges in adult Sprague?Dawley rats and their implications for animal models of temporal lobe epilepsy, Epilepsy & Behavior, vol.32, pp.121-131, 2014.

M. Le-van-quyen, J. Foucher, J. F. Lachaux, E. Rodriguez, A. Lutz et al., Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony, Journal of Neuroscience Methods, vol.111, issue.2, pp.83-98, 2001.

J. W. Puspita, A. I. Jaya, and S. I. Gunadharma, Classification of epileptiform and wicket spike of EEG pattern using backpropagation neural network, AIP Conf. Proc. 2017, 1825, p.20018, 2017.

A. Gupta, P. Singh, and M. Karlekar, A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.26, issue.5, pp.925-935, 2018.

A. Quintero-rincón, C. D'giano, and H. Batatia, A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures, The Journal of Biomedical Research, vol.34, issue.3, p.205, 2020.

A. Quintero-rincon, J. Prendes, V. Muro, and C. D'giano, Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier, 2017 IEEE URUCON, pp.1-4, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02134635

P. Li, C. Karmakar, J. Yearwood, S. Venkatesh, M. Palaniswami et al., Detection of epileptic seizure based on entropy analysis of short-term EEG, PLOS ONE, vol.13, issue.3, p.e0193691, 2018.

J. Jirka, M. Prauzek, O. Krejcar, and K. Kuca, Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification, Neuropsychiatric Disease and Treatment, vol.Volume 14, pp.2439-2449, 2018.

Y. Paul, Various epileptic seizure detection techniques using biomedical signals: a review, Brain Informatics, vol.5, issue.2, pp.1-19, 2018.

A. Subasi, A. Alkan, E. Koklukaya, and M. K. Kiymik, Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing, Neural Networks, vol.18, issue.7, pp.985-997, 2005.

P. Xanthopoulos, . Chang-chia-liu, . Jicong-zhang, E. R. Miller, S. P. Nair et al., A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2184-2187, 2009.

E. Sitnikova, A. E. Hramov, A. A. Koronovsky, and G. Van-luijtelaar, Sleep spindles and spike?wave discharges in EEG: Their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysis, Journal of Neuroscience Methods, vol.180, issue.2, pp.304-316, 2009.

C. D. Richard, A. Tanenbaum, B. Audit, A. Arneodo, A. Khalil et al., SWDreader: A wavelet-based algorithm using spectral phase to characterize spike-wave morphological variation in genetic models of absence epilepsy, Journal of Neuroscience Methods, vol.242, pp.127-140, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01557071

D. A. Pollen, Intracellular studies of cortical neurons during thalamic induced wave and spike, Electroencephalography and Clinical Neurophysiology, vol.17, issue.4, pp.398-404, 1964.

A. Ovchinnikov, A. Lüttjohann, A. Hramov, and G. Van-luijtelaar, An algorithm for real-time detection of spike-wave discharges in rodents, Journal of Neuroscience Methods, vol.194, issue.1, pp.172-178, 2010.

K. M. Rodgers, F. E. Dudek, and D. S. Barth, Progressive, Seizure-Like, Spike-Wave Discharges Are Common in Both Injured and Uninjured Sprague-Dawley Rats: Implications for the Fluid Percussion Injury Model of Post-Traumatic Epilepsy, Journal of Neuroscience, vol.35, issue.24, pp.9194-9204, 2015.

H. Blumenfeld, Cellular and Network Mechanisms of Spike-Wave Seizures, Epilepsia, vol.46, issue.s9, pp.21-33, 2005.

M. Avoli, A brief history on the oscillating roles of thalamus and cortex in absence seizures, Epilepsia, vol.53, issue.5, pp.779-789, 2012.

J. W. Puspita, S. Gunadharma, S. W. Indratno, and E. Soewono, Bayesian approach to identify spike and sharp waves in EEG data of epilepsy patients, Biomedical Signal Processing and Control, vol.35, pp.63-69, 2017.

J. D. Zhu, C. F. Lin, S. H. Chang, J. H. Wang, T. I. Peng et al., Analysis of spike waves in epilepsy using Hilbert-Huang transform, Journal of Medical Systems, vol.39, issue.1, pp.1-13, 2014.

T. M. Medvedeva, M. V. Sysoeva, G. Van luijtelaar, and I. V. Sysoev, Modeling spike-wave discharges by a complex network of neuronal oscillators, Neural Networks, vol.98, pp.271-282, 2018.

E. Olejarczyk, R. Rudner, R. Marciniak, M. Wartak, M. Stasiowski et al., Detection of the EEG spike-wave patterns evoked by volatile anaesthetics, IFMBE Proceedings, vol.25, pp.407-409, 2009.

I. C. Zibrandtsen, J. M. Nielsen, and T. W. Kjaer, Quantitative characteristics of spike-wave paroxysms in genetic generalized epilepsy, Clinical Neurophysiology, vol.131, issue.6, pp.1230-1240, 2020.

H. S. Haghighi and A. H. Markazi, Dynamic origin of spike and wave discharges in the brain, NeuroImage, vol.197, pp.69-79, 2019.

A. Quintero-rincon, J. Prendes, V. Muro, and C. D'giano, Study on spike-and-wave detection in epileptic signals using T-location-scale distribution and the K-nearest neighbors classifier, 2017 IEEE URUCON, vol.1, pp.1-8, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02134635

S. V. Tenneti and P. P. Vaidyanathan, Absence Seizure Detection Using Ramanujan Filter Banks, 2018 52nd Asilomar Conference on Signals, Systems, and Computers, pp.1913-1917, 2018.

F. Polivannyi, T. Igasaki, N. Murayama, and R. Neshige, Wavelet transform-based algorithm for single spike-and-wave discharges detection in epileptic patients' electroencephalogram, 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), pp.255-259, 2015.

A. Quintero-rincon, J. Prendes, M. Pereyra, H. Batatia, and M. Risk, Multivariate Bayesian classification of epilepsy EEG signals, 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp.1-5, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01782569

A. Quintero-rincón, M. Pereyra, C. D?giano, H. Batatia, and M. Risk, A visual EEG epilepsy detection method based on a wavelet statistical representation and the Kullback-Leibler divergence, VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016, pp.13-16, 2017.

. Springer, AUTHOR INDEX Volume 60, The Singapore Economic Review, vol.60, issue.05, p.1599001, 2015.

A. Quintero-rincón, M. Pereyra, C. D?giano, M. Risk, and H. Batatia, Fast statistical model-based classification of epileptic EEG signals, Biocybernetics and Biomedical Engineering, vol.38, issue.4, pp.877-889, 2018.

N. Ahuja, S. Lertrattanapanich, and N. K. Bose, Properties determining choice of mother wavelet, IEE Proceedings - Vision, Image, and Signal Processing, vol.152, issue.5, p.659, 2005.

P. Abry, Ondelettes et Turbulence. Multirésolutions, Algorithmes de Décomposition, Invariance D'échelles, 1997.

M. N. Do and M. Vetterli, Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance, IEEE Transactions on Image Processing, vol.11, issue.2, pp.146-158, 2002.

A. Quintero-rincón, C. D'giano, and M. Risk, Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling, Neurol. Argent, vol.10, pp.201-217, 2018.

C. M. Bishop, Pattern Recognition and Machine Learning

D. Barber, L. Van-der-maaten, and H. Hinton, Bayesian Reasoning and Machine Learning, J. Mach. Learn. Res, vol.51, pp.2579-2605, 2008.

R. Zemouri, M. Levesque, N. Amyot, C. Hudon, and O. Kokoko, Deep Variational Autoencoder: An Efficient Tool for PHM Frameworks, 2020 Prognostics and Health Management Conference (PHM-Besançon), pp.235-240, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02868384