Detection of Epileptic EEG Signal Using Improved Local Pattern Transformation Methods-Reference-Cited by-同舟云学术

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Detection of Epileptic EEG Signal Using Improved Local Pattern Transformation Methods

Published:2018-05-07 Issue:12 Volume:37 Page:5554-5575
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Sairamya N. J.,George S. Thomas,Ponraj D. Narain,Subathra M. S. P.

Funder

DST-TSDP Scheme, Ministry of Sceince and Technology, Government of India

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

http://link.springer.com/article/10.1007/s00034-018-0829-1/fulltext.html

Reference31 articles.

1. U.R. Acharya, S.V. Sree, A.P. Alvin, R. Yanti, J.S. Suri, Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 22(2), 1250002 (2012)

2. H. Adeli, Z. Zhou, N. Dadmehr, Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123, 69–87 (2003)

3. R.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C.E. 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)

4. V. Bajaj, R.B. Pachori, Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed. 16, 1135–1142 (2012)

5. M. Crouse, R. Nowak, R. Baraniuk, Wavelet-based statistical signal processing using hidden Markov models. IEEE Trans. Signal Process. 46, 886–902 (1998)

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