Mirjebreili, S. M., Shalbaf, R., & Shalbaf, A. (2024). Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal. Physical and Engineering Sciences in Medicine: The Official Journal of the Australasian College of Physical Scientists and Engineers in Medicine, 47(2), 633-642. https://doi.org/10.1007/s13246-024-01392-2
Chicago-referens (17:e uppl.)Mirjebreili, Seyed Morteza, Reza Shalbaf, och Ahmad Shalbaf. "Prediction of Treatment Response in Major Depressive Disorder Using a Hybrid of Convolutional Recurrent Deep Neural Networks and Effective Connectivity Based on EEG Signal." Physical and Engineering Sciences in Medicine: The Official Journal of the Australasian College of Physical Scientists and Engineers in Medicine 47, no. 2 (2024): 633-642. https://doi.org/10.1007/s13246-024-01392-2.
MLA-referens (9:e uppl.)Mirjebreili, Seyed Morteza, et al. "Prediction of Treatment Response in Major Depressive Disorder Using a Hybrid of Convolutional Recurrent Deep Neural Networks and Effective Connectivity Based on EEG Signal." Physical and Engineering Sciences in Medicine: The Official Journal of the Australasian College of Physical Scientists and Engineers in Medicine, vol. 47, no. 2, 2024, pp. 633-642, https://doi.org/10.1007/s13246-024-01392-2.
