The SVMD is a robust method that extracts the modes successively and does not need to know the number of modes (unlike VMD). The method considers the mode as a signal with a maximally compact spectrum, as VMD does. It has been demonstrated that the SVMD method without knowing the number of modes converges to the same modes as VMD does with knowing the precise number of modes. Moreover, the computational complexity of SVMD is much lower than that of VMD. Also, another advantage of SVMD over VMD is more robustness against the initial values of the center frequencies of modes.
Nazari, Mojtaba, and Sayed Mahmoud Sakhaei. “Successive Variational Mode Decomposition.” Signal Processing, vol. 174, Elsevier BV, Sept. 2020, p. 107610, doi:10.1016/j.sigpro.2020.107610.
APA
Nazari, M., & Sakhaei, S. M. (2020). Successive variational mode decomposition. Signal Processing, 174, 107610. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.sigpro.2020.107610
Nazari, Mojtaba, and Sayed Mahmoud Sakhaei. “Variational Mode Extraction: A New Efficient Method to Derive Respiratory Signals from ECG.” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 4, Institute of Electrical and Electronics Engineers (IEEE), July 2018, pp. 1059–67, doi:10.1109/jbhi.2017.2734074.
Nazari, Mojtaba, and Sayed Mahmoud Sakhaei. “Variational Mode Extraction: A New Efficient Method to Derive Respiratory Signals from ECG.” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 4, Institute of Electrical and Electronics Engineers (IEEE), July 2018, pp. 1059–67, doi:10.1109/jbhi.2017.2734074.
APA
Nazari, M., & Sakhaei, S. M. (2018). Variational Mode Extraction: A New Efficient Method to Derive Respiratory Signals from ECG. IEEE Journal of Biomedical and Health Informatics, 22(4), 1059–1067. Institute of Electrical and Electronics Engineers (IEEE). Retrieved from https://doi.org/10.1109%2Fjbhi.2017.2734074
BibTeX
@article{Nazari_2018,
doi = {10.1109/jbhi.2017.2734074},
url = {https://doi.org/10.1109%2Fjbhi.2017.2734074},
year = 2018,
month = {jul},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
volume = {22},
number = {4},
pages = {1059--1067},
author = {Mojtaba Nazari and Sayed Mahmoud Sakhaei},
title = {Variational Mode Extraction: A New Efficient Method to Derive Respiratory Signals from {ECG}},
journal = {{IEEE} Journal of Biomedical and Health Informatics}
}
Dragomiretskiy, Konstantin, and Dominique Zosso. “Variational Mode Decomposition.” IEEE Transactions on Signal Processing, vol. 62, no. 3, Institute of Electrical and Electronics Engineers (IEEE), Feb. 2014, pp. 531–44, doi:10.1109/tsp.2013.2288675.
Dragomiretskiy, Konstantin, and Dominique Zosso. “Variational Mode Decomposition.” IEEE Transactions on Signal Processing, vol. 62, no. 3, Institute of Electrical and Electronics Engineers (IEEE), Feb. 2014, pp. 531–44, doi:10.1109/tsp.2013.2288675.
APA
Dragomiretskiy, K., & Zosso, D. (2014). Variational Mode Decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544. Institute of Electrical and Electronics Engineers (IEEE). Retrieved from https://doi.org/10.1109%2Ftsp.2013.2288675
BibTeX
@article{Dragomiretskiy_2014,
doi = {10.1109/tsp.2013.2288675},
url = {https://doi.org/10.1109%2Ftsp.2013.2288675},
year = 2014,
month = {feb},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
volume = {62},
number = {3},
pages = {531--544},
author = {Konstantin Dragomiretskiy and Dominique Zosso},
title = {Variational Mode Decomposition},
journal = {{IEEE} Transactions on Signal Processing}
}
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