RS Fisher, WE Boas, W Blume, C Elger, P Genton, P Lee, J Engel, Epileptic seizures and epilepsy: definitions proposed by the international league against epilepsy (ilae) and the international bureau for epilepsy (ibe). Epilepsia 46(4), 470–472 (2005)
Article
Google Scholar
AT Berg, SF Berkovic, MJ Brodie, J Buchhalter, JH Cross, WE Boas, J Engel, J French, TA Glauser, GW Mathern, et al., Revised terminology and concepts for organization of seizures and epilepsies: report of the ILAE commission on classification and terminology, 2005-2009. Epilepsia 51(4), 676–685 (2010)
Article
Google Scholar
S Ramgopal, S Thome-Souza, M Jackson, NE Kadish, IS Fernandez, J Klehm, W Bosl, C Reinsberger, S Schachter, T Loddenkemper, Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 37, 291–307 (2014)
Article
Google Scholar
SF Liang, HC Wang, WL Chang, Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J. Adv. Signal Process. 2010(1), 853434 (2010)
Article
Google Scholar
V Srinivasan, C Eswaran, N Sriraam, Artificial neural network based epileptic detection using time-domain and frequency-domain features. J. Med. Syst. 29(6), 647–660 (2005)
Article
Google Scholar
K Polat, S Günes, Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 187(2), 1017–1026 (2007)
MathSciNet
MATH
Google Scholar
AT Tzallas, MG Tsipouras, DI Fotiadis, Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703–710 (2009)
Article
Google Scholar
H Adeli, Z Zhou, N Dadmehr, Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123(1), 69–87 (2003)
Article
Google Scholar
H Ocak, Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm. Signal Process. 88(7), 1858–1867 (2008)
Article
MathSciNet
MATH
Google Scholar
L Guo, D Rivero, J Dorado, AP CR Munteanu, Automatic feature extraction using genetic programming: an application to epileptic EEG classification. Expert Syst. Appl. 38(8), 10425–10436 (2011)
Article
Google Scholar
G Chen, Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Syst. Appl. 41(5), 2391–2394 (2014)
Article
Google Scholar
BL WC Stacey, Technology insight: neuroengineering and epilepsy-designing devices for seizure control. Nat. Clin. Pract. Neurol. 4(4), 190–201 (2008)
Article
Google Scholar
Y Li, PP Wen, et al., Clustering technique-based least square support vector machine for EEG signal classification. Comput. Methods Prog. Biomed. 104(3), 358–372 (2011)
Article
Google Scholar
Z Iscan, Z Dokur, T Demiralp, Classification of electroencephalogram signals with combined time and frequency features. Expert Syst. Appl. 38(8), 10499–10505 (2011)
Article
Google Scholar
Y Tang, D Durand, A tunable support vector machine assembly classifier for epileptic seizure detection. Expert Syst. Appl. 39(4), 3925–3938 (2012)
Article
Google Scholar
N Nicolaou, J Georgiou, Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst. Appl. 39(1), 202–209 (2012)
Article
Google Scholar
G Zhu, Y Li, PP Wen, Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm. Comput. Methods Prog. Biomed. 115(2), 64–75 (2014)
Article
Google Scholar
A Temko, E Thomas, W Marnane, G Lightbody, G Boylan, EEG-based neonatal seizure detection with support vector machines. Clin. Neurophysiol. 122(3), 464–473 (2011)
Article
Google Scholar
NE Huang, Z Shen, SR Long, MC Wu, HH Shih, Q Zheng, NC Yen, CC Tung, HH Liu, in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, the Royal Society. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, vol 454 (1998), pp. 903–995
Google Scholar
RJ Oweis, EW Abdulhay, Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed. Eng. Online 10(1), 38 (2011)
Article
Google Scholar
F Riaz, A Hassan, S Rehman, IK Niazi, K Dremstrup, EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans. Neural Syst. Rehabil. Eng. 24(1), 28–35 (2016)
Article
Google Scholar
F K, Q J, Y Chai, Y Dong, Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomed. Signal Process. Control 13, 15–22 (2014)
Article
Google Scholar
V Bajaj, RB Pachori, Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed. 16(6), 1135–1142 (2012)
Article
Google Scholar
AG Mahapatra, K Horio, in Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on, IEEE. Overcoming drawback of feature instantaneous bandwidth using EMD for epileptic seizure classification by RMS frequency (2016), pp. 001322–001327
Chapter
Google Scholar
R Sharma, RB Pachori, Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst. Appl. 42(3), 1106–1117 (2015)
Article
Google Scholar
K Samiee, P Kovacs, M Gabbouj, Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction. Knowl.-Based Syst. 118, 228–240 (2017)
Article
Google Scholar
J Spilka, J Frecon, R Leonarduzzi, N Pustelnik, P Abry, M Doret, Sparse support vector machine for intrapartum fetal heart rate classification. IEEE J. Biomed. Health Inform. 21(3), 664–671 (2017)
Article
Google Scholar
IJ Rampil, A primer for EEG signal processing in anesthesia. Anesthesiology 89(4), 980–1002 (1998)
Article
Google Scholar
B Crepon, V Navarro, D Hasboun, S Clemenceau, J Martinerie, M Baulac, C Adam, M Le Van Quyen, Mapping interictal oscillations greater than 200 Hz recorded with intracranial macroelectrodes in human epilepsy. Brain 133(1), 33–45 (2009)
Article
Google Scholar
HS Liu, T Zhang, FS Yang, A multistage, multimethod approach for automatic detection and classification of epileptiform EEG. IEEE Trans. Biomed. Eng. 49(12), 1557–1566 (2002)
Article
Google Scholar
KJ Blinowska, PJ Durka, Unbiased high resolution method of EEG analysis in time-frequency space. Acta Neurobiol. Exp. 61(3), 157{174 (2001)
Google Scholar
E Imani, HR Pourreza, T Banaee, Fully automated diabetic retinopathy screening using morphological component analysis. Comput. Med. Imaging Graph. 43, 78–88 (2015)
Article
Google Scholar
E Imani, M Javidi, HR Pourreza, Improvement of retinal blood vessel detection using morphological component analysis. Comput. Methods Prog. Biomed. 118(3), 263–279 (2015)
Article
Google Scholar
A Hyvärinen, E Oja, Independent component analysis: algorithms and applications. Neural Netw. 13(4–5), 411–430 (2000)
Article
Google Scholar
P Berg, M Scherg, A multiple source approach to the correction of eye artifacts. Electroencephalogr. Clin. Neurophysiol. 90(3), 229–241 (1994)
Article
Google Scholar
GL Wallstrom, RE Kass, A Miller, JF Cohn, NA Fox, Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. Int. J. Psychophysiol. 53(2), 105–119 (2004)
Article
Google Scholar
A Hyvärinen, J Särelä, R Vigario, in Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99). Spikes and bumps: Artefacts generated by independent component analysis with insu cient sample size (1999), pp. 425–429
Google Scholar
J Särelä, R Vigario, Overlearning in marginal distribution based ica: analysis and solutions. J. Mach. Learn. Res. 4(Dec), 1447–1469 (2003)
MathSciNet
MATH
Google Scholar
B Singh, H Wagatsuma, A removal of eye movement and blink artifacts from EEG data using morphological component analysis. Comput. Math. Methods Med. 2017, 1861645 (2017)
Article
MathSciNet
Google Scholar
Y Jiang, M Wang, Image fusion with morphological component analysis. Inf. Fusion 18, 107–118 (2014)
Article
Google Scholar
M Dalla Mura, A Villa, JA Benediktsson, J Chanussot, L Bruzzone, Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geosci. Remote Sens. Lett. 8(3), 542–546 (2011)
Article
Google Scholar
RK X Yong, GE Ward, in Neural Engineering, 2009. NER'09. 4th International IEEE/EMBS Conference on, IEEE. Birch, generalized morphological component analysis for EEG source separation and artifact removal (2009), pp. 343–346
Chapter
Google Scholar
S JW Matiko, J Beeby, in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, IEEE. Tudor, real time eye blink noise removal from EEG signals using morphological component analysis (2013), pp. 13–16
Chapter
Google Scholar
SS Chen, DL Donoho, MA Saunders, Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)
Article
MathSciNet
MATH
Google Scholar
M Püschel, JM Moura, The algebraic approach to the discrete cosine and sine transforms and their fast algorithms. SIAM J. Comput. 32(5), 1280{1316 (2003)
Article
MathSciNet
MATH
Google Scholar
X Shao, SG Johnson, Type-IV DCT, DST, and MDCT algorithms with reduced numbers of arithmetic operations. Signal Process. 88(6), 1313–1326 (2008)
Article
MATH
Google Scholar
Y JL Starck, J Moudden, M Bobin, DD Elad, Morphological component analysis. Proc. SPIE 5914, 1–15 (2005)
Google Scholar
S Sardy, A Bruce, P Tseng, Block coordinate relaxation methods for nonparametric signal denoising with wavelet dictionaries, (1998).
Google Scholar
PJ Loughlin, B Tacer, Comments on the interpretation of instantaneous frequency. IEEE Signal Process Lett. 4(5), 123–125 (1997)
Article
MATH
Google Scholar
L Cohen, Time-frequency analysis (Prentice Hall PTR, Englewood Cliffs, 1995)
Google Scholar
L Cohen, C Lee, in Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on, IEEE. Instantaneous bandwidth for signals and spectrogram (1990), pp. 2451–2454
Google Scholar
RG Andrzejak, K Lehnertz, F Mormann, C Rieke, P David, CE Elger, 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 (2001)
Article
Google Scholar
S Tolwinski, The Hilbert Transform and Empirical Mode Decomposition as Tools for Data Analysis (University of Arizona, Tucson, 2007)
Google Scholar
V Vapnik, The nature of statistical learning theory (Springer science & business media, 2013)
K Samiee, P Kovacs, M Gabbouj, Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Trans. Biomed. Eng. 62(2), 541–552 (2015)
Article
Google Scholar
AR Hassan, A Subasi, Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput. Methods Prog. Biomed. 136, 65–77 (2016)
Article
Google Scholar
S Altunay, Z Telatar, O Erogul, Epileptic EEG detection using the linear prediction error energy. Expert Syst. Appl. 37(8), 5661–5665 (2010)
Article
Google Scholar
V Joshi, RB Pachori, A Vijesh, Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed. Signal Process. Control 9, 1–5 (2014)
Article
Google Scholar
RB Pachori, S Patidar, Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput. Methods Prog. Biomed. 113(2), 494–502 (2014)
Article
Google Scholar
J Martinez-del Rincon, MJ Santofimia, X del Toro, J Barba, F Romero, P Navas, JC Lopez, Non-linear classifiers applied to EEG analysis for epilepsy seizure detection. Expert Syst. Appl. 86, 99 (2017)
Article
Google Scholar