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All issues Volume 32 (2020) ITM Web Conf., 32 (2020) 02008 References
Open Access
Issue
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
Article Number 02008
Number of page(s) 5
Section Communication
DOI https://doi.org/10.1051/itmconf/20203202008
Published online 29 July 2020
  1. Islam, Md Kafiul, Amir Rastegarnia, and Zhi Yang. “A wavelet-based artifact reduction from scalp EEG for epileptic seizure detection.” IEEE journal of biomedical and health informatics 20.5 (2016): 1321-1321. [Google Scholar]
  2. Fisher, Robert S., et al. “The impact of epilepsy from the patients perspective I. Descriptions and subjective perceptions.” Epilepsy research 41.1 (2000): 39-39. [Google Scholar]
  3. O Regan, Simon, Stephen Faul, and William Marnane. “Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals.” Medical engineering physics 35.7 (2013): 867-867. [Google Scholar]
  4. Gwin, Joseph Toliver, et al. “Removal of movement artifact from high-density EEG recorded during walking and running.” American Journal of Physiology-Heart and Circulatory Physiology (2010). [Google Scholar]
  5. Saab, M.E., and Jean Gotman. “A system to detect the onset of epileptic seizures in scalp EEG.” Clinical Neurophysiology 116.2 (2005): 427-427. [Google Scholar]
  6. Teplan, Michal. “Fundamentals of EEG measurement.” Measurement science review 2.2 (2002): 1-11. [Google Scholar]
  7. Bowman, James, F. Edward Dudek, and Mark Spitz. “Epilepsy is a chronic neurological disorder characterized by recurrent epileptic seizures. Brain injury and genetic abnormalities underlie this disorder.” [Google Scholar]
  8. Islam, Md Kafiul, et al. “Artifact characterization and removal for in vivo neural recording.” Journal of neuroscience methods 226 (2014): 110-123. [Google Scholar]
  9. Coifman, Ronald R., and David L. Donoho. “Translation-invariant de-noising.” Wavelets and statistics. Springer, New York, NY, 1995. 125-150. [Google Scholar]
  10. Gao, Hong-Ye. “Wavelet shrinkage denoising using the non-negative garrote.” Journal of Computational and Graphical Statistics 7.4 (1998): 469-488. [Google Scholar]
  11. Flexer, Arthur, et al. “Using ICA for removal of ocular artifacts in EEG recorded from blind subjects.” Neural Networks 18.7 (2005): 998-1005. [Google Scholar]
  12. Joyce, Carrie A., Irina F. Gorodnitsky, and Marta Kutas. “Automatic removal of eye movement and blink artifacts from EEG data using blind component separation.” Psychophysiology 41.2 (2004): 313-325. [Google Scholar]
  13. Hallez, Hans, et al. “Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging.” Clinical Neurophysiology 120.7 (2009): 1262-1272. [Google Scholar]
  14. Esteller, Rosana, et al. “Line length: an efficient feature for seizure onset detection.” 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol. 2. IEEE, 2001. [Google Scholar]
  15. Rafiuddin, Nidal, Yusuf Uzzaman Khan, and Omar Farooq. “Feature extraction and classification of EEG for automatic seizure detection.” 2011 International Conference on Multimedia, Signal Processing and Communication Technologies. IEEE, 2011. [Google Scholar]
  16. Song, Yuedong, Jon Crowcroft, and Jiaxiang Zhang. “Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine.” Journal of neuroscience methods 210.2 (2012): 132-146. [Google Scholar]
  17. Misiti, M., et al. “Wavelet toolbox for use with Matlab, Users Guide, Ver. 3., The MathWorks.” Inc. Users Guide (2004). [Google Scholar]
  18. (2014, Nov. 21). [Online]. Available: http://physionet.nlm.nih.gov/pn6/chbmit/ [Google Scholar]
  19. Kutlu, Yakup, and Damla Kuntalp. “Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients.” Computer methods and programs in biomedicine 105.3 (2012): 257-267. [Google Scholar]

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