References#

[SLEEP_EDF-2018]

B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000). And Also Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., … & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

[MASS-2014]

Christian O’Reilly et al. “Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research”. en. In: Journal of Sleep Research 23.6 (2014). _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jsr.12169, pp. 628–635. issn: 1365-2869. doi: 10.1111/jsr.12169. url: http://onlinelibrary.wiley.com/doi/abs/10.1111/jsr.12169

[Chambon-2018]

S. Chambon, M. N. Galtier, P. J. Arnal, G. Wainrib, et A. Gramfort. A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series. IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26, nᵒ 4, p. 758–769, avr. 2018, doi: 10.1109/TNSRE.2018.2813138.

[Satapathy-2023]

S. K. Satapathy et D. Loganathan. «Automated classification of multi-class sleep stages classification using polysomnography signals: a nine- layer 1D-convolution neural network approach». Multimed Tools Appl, vol. 82, nᵒ 6, p. 8049-8091, mars 2023, doi: 10.1007/s11042-022-13195-2.