A novel approach for automated detection of focal EEG signals using empirical wavelet transform-Reference-Cited by-同舟云学术

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A novel approach for automated detection of focal EEG signals using empirical wavelet transform

Published:2016-11-16 Issue:8 Volume:29 Page:47-57
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Bhattacharyya Abhijit,Sharma Manish,Pachori Ram Bilas,Sircar Pradip,Acharya U. Rajendra

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2646-4/fulltext.html

Reference39 articles.

1. Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl Based Syst 45:147–165

2. Andrzejak RG, Schindler K, Rummel C (2012) Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys Rev E 86(4):046206

3. Bajaj V, Pachori RB (2012) Separation of rhythms of EEG signals based on Hilbert–Huang transformation with application to seizure detection. In: Convergence and hybrid information technology, pp 493–500

4. Cohen ME, Hudson DL, Deedwania PC (1996) Applying continuous chaotic modeling to cardiac signal analysis. IEEE Eng Med Biol Mag 15(5):97–102

5. Das AB, Bhuiyan MIH (2016) Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed Signal Process Control 29:11–21

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