J. L. Hossain and C. M. Shapiro, The prevalence, cost implications, and management of sleep disorders: An overview, Sleep Breathing, vol.6, pp.85-102, 2002.

T. A. Hargens, A. S. Kaleth, E. S. Edwards, and K. L. Butner, Association between sleep disorders, obesity, and exercise: A review, Nature Sci. Sleep, vol.5, pp.27-35, 2013.

C. Iber, S. Ancoli-israel, A. L. Chesson, and S. F. Quan, The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications, Sleep Med, 2007.

H. Danker-hopfe, Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard, J. Sleep Res, vol.18, issue.1, pp.74-84, 2009.

S. A. Imtiaz and E. Rodriguez-villegas, Automatic sleep staging using state machine-controlled decision trees, Proc. 37th Annu. Int. Conf, pp.378-381, 2015.

A. R. Hassan and M. I. Bhuiyan, A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features, J. Neurosci. Methods, vol.271, pp.107-118, 2016.

K. D. Tzimourta, Eeg-based automatic sleep stage classification, Biomed. J. Sci. Tech. Res, vol.7, issue.4, pp.1-6, 2018.

C. Panagiotou, I. Samaras, J. Gialelis, P. Chondros, and D. Karadimas, A comparative study between SVM and fuzzy inference system for the automatic prediction of sleep stages and the assessment of sleep quality, Proc. 9th Int. Conf. Pervasive Comput, pp.293-296, 2015.

S. Seifpour, H. Niknazar, M. Mikaeili, and A. M. Nasrabadi, A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal, Expert Syst. Appl, vol.104, pp.277-293, 2018.

B. A. Savareh, A. Bashiri, A. Behmanesh, G. H. Meftahi, and B. Hatef, Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis, PeerJ, vol.6, p.5247, 2018.

M. Prucnal and A. G. Polak, Effect of feature extraction on automatic sleep stage classification by artificial neural network, Metrol. Meas. Syst, vol.24, issue.2, pp.229-240, 2017.

S. Raiesdana, Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations, Australas. Phys. Eng. Sci. Med, vol.41, issue.1, pp.161-176, 2018.

T. Lajnef, Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines, J. Neurosci. Methods, vol.250, pp.94-105, 2015.

X. Li, L. Cui, S. Tao, J. Chen, X. Zhang et al., Hyclasss: A hybrid classifier for automatic sleep stage scoring, IEEE J. Biomed. Health Inform, vol.22, issue.2, pp.375-385, 2018.

O. Tsinalis, P. M. Matthews, Y. Guo, and S. Zafeiriou, Automatic sleep stage scoring with single-channel EEG using convolutional neural networks, 2016.

J. Zhang and Y. Wu, Complex-valued unsupervised convolutional neural networks for sleep stage classification, Comput. Methods Programs Biomed, vol.164, pp.181-191, 2018.
DOI : 10.1016/j.cmpb.2018.07.015

A. Sors, S. Bonnet, S. Mirek, L. Vercueil, and J. Payen, A convolutional neural network for sleep stage scoring from raw single-channel EEG, Biomed. Signal Process. Control, vol.42, pp.107-114, 2018.
DOI : 10.1016/j.bspc.2017.12.001

H. Dong, A. Supratak, W. Pan, C. Wu, P. M. Matthews et al., Mixed neural network approach for temporal sleep stage classification, IEEE Trans. Neural Syst. Rehabil. Eng, vol.26, issue.2, pp.324-333, 2018.
DOI : 10.1109/tnsre.2017.2733220
URL : http://arxiv.org/pdf/1610.06421

A. Ugon, Fusion symbolique et données polysomnographiques, 2013.

C. Chen, Symbolic fusion: A novel decision support algorithm for sleep staging application, Proc. 5th EAI Int. Conf. Wireless Mobile Commun. Healthcare, pp.19-22, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01315584

A. Ugon, Knowledge-based decision system for automatic sleep staging using symbolic fusion in a turing machine-like decision process formalizing the sleep medicine guidelines, Expert Syst. Appl, vol.114, pp.414-427, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01857040

I. Bloch and H. Maitre, Data fusion in 2D and 3D image processing: An overview, Proc. 10th Brazilian Symp, pp.127-134, 1997.
DOI : 10.1109/sigra.1997.625158

P. Lambert and T. Carron, Symbolic fusion of luminance-hue-chroma features for region segmentation, Pattern Recognit, vol.32, issue.11, pp.1857-1872, 1999.
DOI : 10.1016/s0031-3203(99)00010-2

A. Ugon, J. Ganascia, C. Philippe, H. Amiel, and P. Lévy, How to use symbolic fusion to support the sleep apnea syndrome diagnosis, Proc. Conf, pp.45-54, 2011.
DOI : 10.1007/978-3-642-22218-4_6

R. S. Rosenberg and S. Van-hout, The American academy of sleep medicine inter-scorer reliability program: Sleep stage scoring, J. Clin. Sleep Med, vol.9, issue.1, pp.81-87, 2013.
DOI : 10.5664/jcsm.2350
URL : http://jcsm.aasm.org/Articles/jcsm.9.1.81.pdf

S. Liang, Y. Chen, C. Kuo, J. Chen, and S. Hsu, A fuzzy inference system for sleep staging, Proc. IEEE Int. Conf. Fuzzy Syst, pp.2104-2107, 2011.
DOI : 10.1109/fuzzy.2011.6007380

S. Liang, C. Kuo, Y. Hu, and Y. Cheng, A rule-based automatic sleep staging method, J. Neurosci. Methods, vol.205, issue.1, pp.169-176, 2012.

C. Chen, Personalized sleep staging system using evolutionary algorithm and symbolic fusion, Proc. 38th Annu. Int. Conf, pp.2266-2269, 2016.
DOI : 10.1109/embc.2016.7591181
URL : https://hal.archives-ouvertes.fr/hal-01396595

C. Chen, Cross entropy-based automatic thresholds setting-up method for sleep staging system, Proc. IEEE Biomed. Circuits Syst. Conf. (BioCAS), pp.312-315, 2016.
DOI : 10.1109/biocas.2016.7833794
URL : https://hal.archives-ouvertes.fr/hal-01419354

P. K. Wong, F. Yu, A. Shahangian, G. Cheng, R. Sun et al., Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm, Proc. Nat. Acad. Sci. USA, vol.105, issue.13, pp.5105-5110, 2008.

C. Ho, Keynote: Personalized medicine enabled by FSC.X technology, Proc. IEEE Faible Tension Faible Consommation (FTFC), pp.1-2, 2014.

N. Zeng, H. Qiu, Z. Wang, W. Liu, H. Zhang et al., A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease, Neurocomputing, vol.320, pp.195-202, 2018.

N. Zeng, H. Zhang, W. Liu, J. Liang, and F. E. Alsaadi, A switching delayed PSO optimized extreme learning machine for shortterm load forecasting, Neurocomputing, vol.240, pp.175-182, 2017.
DOI : 10.1016/j.neucom.2017.01.090

N. Zeng, Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach, IEEE Trans. Med. Imag, vol.33, issue.5, pp.1129-1136, 2014.
DOI : 10.1109/tmi.2014.2305394
URL : http://bura.brunel.ac.uk/bitstream/2438/10300/1/Fulltext.pdf

R. Storn and K. Price, Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim, vol.11, issue.4, pp.341-359, 1997.

C. M. Selvi and K. Gnanambal, Power system voltage stability analysis using modified differential evolution, Proc. Int. Conf. Comput, pp.382-387, 2011.

R. A. Sarker, S. M. Elsayed, and T. Ray, Differential evolution with dynamic parameters selection for optimization problems, IEEE Trans. Evol. Comput, vol.18, issue.5, pp.689-707, 2014.
DOI : 10.1109/tevc.2013.2281528
URL : https://doi.org/10.1109/tevc.2013.2281528

Q. K. Le, Q. D. Truong, and V. T. Vo, A tool for analysis and classification of sleep stages, Proc. Int. Conf, pp.307-310, 2011.

J. R. Landis and G. G. Koch, The measurement of observer agreement for categorical data, Biometrics, vol.33, issue.1, pp.159-174, 1977.

D. Shrivastava, S. Jung, M. Saadat, R. Sirohi, and K. Crewson, How to interpret the results of a sleep study, J. Community Hospital Internal Med. Perspect, vol.4, issue.5, p.24983, 2014.

K. Shahveisi, A. Jalali, M. R. Moloudi, S. Moradi, A. Maroufi et al., Sleep architecture in patients with primary snoring and obstructive sleep apnea, Basic Clin. Neurosci, vol.9, issue.2, pp.147-156, 2018.

L. Breiman, Classification and Regression Trees, 2017.

L. Breiman, Random forests, Mach. Learn, vol.45, issue.1, pp.5-32, 2001.

G. Mclachlan, Discriminant Analysis and Statistical Pattern Recognition, vol.544, 2004.

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, Support vector machines, IEEE Intell. Syst. Appl, vol.13, issue.4, pp.18-28, 2008.

J. M. Keller, M. R. Gray, and J. A. Givens, A fuzzy K-nearest neighbor algorithm, IEEE Trans. Syst, vol.15, issue.4, pp.580-585, 1985.

K. P. Murphy, Machine Learning: A Probabilistic Perspective, 2012.

K. Pillay, A. Dereymaeker, K. Jansen, G. Naulaers, S. Van-huffel et al., Automated EEG sleep staging in the term-age baby using a generative modelling approach, J. Neural Eng, vol.15, issue.3, p.2019, 2018.