Comparison of Various Empirical-Mode Decomposition Techniques of EEG for the Diagnostics of Epilepsy
Author:
Publisher
Springer Science and Business Media LLC
Subject
Physiology,General Neuroscience
Link
https://link.springer.com/content/pdf/10.1007/s11062-023-09935-1.pdf
Reference31 articles.
1. M. S. J. Solaija, S. Saleem, K. Khurshid, et al., “Dynamic mode decomposition based epileptic seizure detection from scalp EEG,” IEEE Access, 6, 38683–38692 (2018); doi: https://doi.org/10.1109/ACCESS.2018.2853125.
2. U. Acharya, S. V. Sree, G. Swapna, et al., “Automated EEG analysis of epilepsy: A review,” Knowledge Based Syst., 45, 147–165 (2013); doi: https://doi.org/10.1016/j.knosys.2013.02.014.
3. W. A. Mir, M. Anjum, Izharuddin, and S. Shahab, “Deep-EEG: an optimized and robust framework and method for EEG-based diagnosis of epileptic seizure,” Diagnostics (Basel), 13, No. 4, 773 (2023); doi: https://doi.org/10.3390/diagnostics13040773.
4. J. E. Jacob, V. V. Sreelatha, T. Iype, et al., “Diagnosis of epilepsy from interictal EEGs based on chaotic and wavelet transformation,” Analog Integr. Circ. Sig. Process, 89 131–138 (2016); doi: https://doi.org/10.1007/s10470-016-0810-5.
5. V. S. Vijith, J. E. Jacob, T. Iype, et al., “Epileptic seizure detection using non linear analysis of EEG,” Intern. Conf. Invent. Comp. Techn. (ICICT), 3, 1–6 (2016); doi: https://doi.org/10.1109/INVENTIVE.2016.7830193.
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