1 |
K. P. Indiradevi et al., A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram, Comput. Biol. Med. 38 (2008), no. 7, 805-816.
DOI
|
2 |
R. W. Wall, Simple methods for detecting zero crossing, in Proc. Ann. Conf. Industrial Elect. Soc., Roanoke, VA, USA, 2004, pp. 2477-2481.
|
3 |
M. Z. Uddin, D. H. Kim, and T. S. Kim, A human activity recognition system using HMMs with GDA on enhanced independent component features, Int. Arab J. Inf. Technol. 12 (2015), no. 3, 304-310.
|
4 |
M. H. Abdullah, J. M. Abullah, and M. Z. Abdullah, Seizure detection by means of hidden Markov model and stationary wavelet transform of electroencephalograph signals, in Proc. Biomed. Health Informatics, Hong Kong, China, 2012, pp. 62-65.
|
5 |
R. Sharma and R. B. Pachori, Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions, Expert Syst. Applicat. 42 (2015), no. 3, 1106-1117.
DOI
|
6 |
R. R. Sharma and R. B. Pachori, Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals, IET Sci. Meas. Technol. 12 (2017), no. 1, 72-82.
|
7 |
M. Peker, B. Sen, and D. Delen, A novel method for automated diagnosis of epilepsy using complex‐valued classifiers, IEEE J. Biomed. Health Inform. 20 (2015), no. 1, 108-118.
DOI
|
8 |
K. Samiee, P. Kovacs, and M. Gabboouj, Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform, IEEE Trans. Biomed. Eng. 62 (2014), no. 2, 541-552.
DOI
|
9 |
T. S. Kumar, Classification of seizure and seizure‐free EEG signals using local binary patterns, Biomed. Signal Process. Contr. 15 (2015), 33-40.
DOI
|
10 |
Y. Kaya et al., 1D-local binary pattern based feature extraction for classification of epileptic EEG signals, Appl. Math. Comput. 243 (2014), 209-219.
DOI
|
11 |
T. S. Kumar et al., Classification of seizure and seizure-free EEG signals using multi-level local patterns, in Proc. Int. Conf. Digit. Signal Process., Hong Kong, China, Aug. 2014, pp. 646-650.
|
12 |
A. K. Jaiswal and H. Banka, Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals, Biomed. Signal Process. Contr. 34 (2017), 81-92.
DOI
|
13 |
A. K. Jaiswal and H. Banka, Epileptic seizure detection in EEG signal with GModPCA and support vector machine, Biomed. Mater. Eng. 28 (2017), no. 2, 141-157.
|
14 |
A. K. Jaiswal and H. Banka, Epileptic seizure detection in EEG signal using machine learning techniques, Australas. Phys. Eng. Sci. Med. 41 (2018), no. 1, 81-94.
DOI
|
15 |
T. Gandhi et al., Expert model for detection of epileptic activity in EEG signature, Expert Syst. Appl. 37 (2010), no. 4, 3513-3520.
DOI
|
16 |
N. J. Sairamya et al., Detection of epileptic EEG signal using improved local pattern transformation methods, Circ. Syst. Signal Process. 37 (2018), no. 12, 1-22.
DOI
|
17 |
J. L. Song and R. Zhang, Automatic seizure detection using a novel EEG feature based on nonlinear complexity, in Proc. Int. Joint Conf. Neural Netw., Vancouver, Canada, 2016, pp. 1686-1695.
|
18 |
Y. Kumar, M. L. Dewal, and R. S. Anand, Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine, Neurocomput. 133 (2014), 271-279.
DOI
|
19 |
F. Mormann et al., Seizure prediction: the long and winding road, Brain 130 (2006), no. 2, 314-333.
DOI
|
20 |
B. Litt and K. Lehnertz, Seizure prediction and the preseizure period, Curr. Opin. Neurol. 14 (2002), no. 2. 173-177.
|
21 |
V. Joshi, R. B. Pachori, and A. Vijesh, Classification of ictal and seizure‐free EEG signals using fractional linear prediction, Biomed. Signal Process. Contr. 9 (2013), 1-5.
DOI
|
22 |
L. Guo et al., Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks, J. Neurosci. Methods 191 (2010), no. 1, 101-109.
DOI
|
23 |
A. Subasi and M. I. Gursoy, EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Syst. Applicat. 37 (2010), no. 12, 8659-8666.
DOI
|
24 |
S. Xie et al., Feature extraction via dynamic PCA for epilepsy diagnosis and epileptic seizure detection, in Proc. IEEE Int. Work. Machine Lear. Signal Process., Kittila, Finland, 2010, pp. 337-342.
|
25 |
S. Ghosh‐Dastidar, H. Adeli, and N. Daddmehr, Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection, IEEE Trans. Biomed. Eng. 55 (2008), no. 2, 512-518.
DOI
|
26 |
J. Kevric and A. Subasi, The effect of multiscale PCA de-noising in epileptic seizure detection, J. Med. Syst. 38 (2014), no. 10, 131-143.
DOI
|
27 |
Y. Kumar, M. L. Dewal, and R. S. Anand, Epileptic seizures detection in EEG using DWT‐based ApEn and artificial neural network, Signal Image Video Process 8 (2012), no. 7, 1323-1334.
DOI
|
28 |
M. Sharmar, R. B. Pachori, and U. R. Acharya, A new approach to characterize epileptic seizures using analytic time‐frequency flexible wavelet transform and fractal dimension, Pattern Recogn. Lett. 94 (2017), 172-179.
DOI
|
29 |
D. Bhati, R. B. Pachori, and V. M. Gadre, Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification, Digit. Signal Process. 62 (2017), 259-273.
DOI
|
30 |
H. Ocak, Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy, Expert Syst. Applicat. 36 (2009), no. 2, 2027-2036.
DOI
|
31 |
S. Nasehi and H. Pourghassem, Automatic prediction of epileptic seizure using kernel fisher discriminant classifiers, in Proc. Int. Compt. Bio-Med. Instrum., Wuhan, China, 2011, pp. 200-203.
|
32 |
A. K. Tiwari et al., Automated diagnosis of epilepsy using keypoints based local binary pattern of EEG signals, IEEE J. Biomed. Health. 21 (2017), no. 4, 888-896.
DOI
|
33 |
A. Bhattacharyya et al., Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals, Appl. Sci. 7 (2017), no. 4, 385-402.
DOI
|
34 |
R. B. Pachori and S. Patidar, Epileptic seizure classification in EEG signals using second‐order difference plot of intrinsic mode functions, Comput. Methods Programs Biomed. 113 (2014), no. 2, 494-502.
DOI
|
35 |
V. Bajaj and R. B. Pachori, Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals, Biomed. Eng. Lett. 3 (2013), no. 1, 17-21.
DOI
|
36 |
V. Bajaj and R. B. Pachori, Classification of seizure and nonseizure EEG signals using empirical mode decomposition, IEEE Trans. Inf. Technol. Biomed. 16 (2012), no. 6, 1135-1142.
DOI
|
37 |
R. B. Pachori and V. Bajaj, Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition, Comput. Methods Programs Biomed. 104 (2011), no. 3, 373-381.
DOI
|
38 |
R. B. Pachori, Discrimination between ictal and seizure‐free EEG signals using empirical mode decomposition, Res. Lett. Signal Process. 2008 (2008), no. 14, 1-5.
DOI
|
39 |
I. Conradsen et al., Automated algorithm for generalised tonicclonic epileptic seizure onset detection based on sEMG zero‐crossing rate, IEEE Trans. Biomed. Eng. 59 (2012), no. 2, 579-585.
DOI
|
40 |
S. Elgohary, S. Eldawlathly, and M. I. Khalil, Epileptic seizure prediction using zero‐crossings analysis of EEG wavelet detail coefficients, in Proc. IEEE Conf. Comput. Intell. Bioinformatics. Comput. Biol., Chiang, Thailand, Oct. 2016, pp. 1-6.
|
41 |
A. S. Zandi et al., Predicting epileptic seizures in scalp EEG based on a variational bayesian faussian mixture model of zero‐crossing intervals, IEEE Trans. Biomed. Eng. 60 (2013), no. 5, 1401-1413.
DOI
|
42 |
A. S. Zandi et al., Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG, in Proc. Ann. Int. Conf. Eng. Medi. Biol. Soc., Buenos Aires, Argentina, 2010, pp. 5537-5540.
|
43 |
A. Subasi, Application of adaptive neuro‐fuzzy inference system for epileptic seizure detection using wavelet feature extraction, Comput. Biol. Med. 37 (2007), no. 2, 227-244.
DOI
|
44 |
M. R. Lee et al., Classification of both seizure and non‐seizure based on EEG signals using hidden markov model, in Proc. Int. Conf. BIGCOMP., Shanghai, China, 2018, pp. 469-474.
|
45 |
R. Oostenveld and P. Praamstra, The five percent electrode system for high‐resolution EEG and ERP measurements, Clin. Neurophysiol. 112 (2001), no. 4, 713-719.
DOI
|
46 |
M. R. Lee et al., A novel R peak detection method for mobile environments, IEEE Access 6 (2018), 51227-51237.
DOI
|
47 |
R. G. Andrzejak et al., 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 (2001), no. 6, 061907:1-8.
DOI
|