C. L. Fillmore, B. E. Bray, and K. Kawamoto, Systematic review of clinical decision support interventions with potential for inpatient cost reduction. BMC medical informatics and decision making, pp.13-135, 2013.

S. Aishwarya and S. Anto, A Medical Decision Support System based on Genetic Algorithm and Least Square Support Vector Machine for Diabetes Disease Diagnosis, International Journal of Engineering Sciences & Research Technology, pp.4042-4046, 2014.

M. H. Trivedi, J. K. Kern, B. D. Grannemann, K. Z. Altshuler, and P. Sunderajan, A Computerized Clinical Decision Support System as a Means of Implementing Depression Guidelines, Psychiatric Services, vol.55, issue.8, pp.8-879, 2004.
DOI : 10.1176/appi.ps.55.8.879

A. C. Stasis, E. N. Loukis, S. A. Pavlopoulos, and D. Koutsouris, Using decision tree algorithms as a basis for a heart sound diagnosis decision support system. 4th, International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, pp.354-357, 2003.

J. Eberhardt, A. Bilchik, and A. Stojadinovic, Clinical decision support systems: Potential with pitfalls, Journal of Surgical Oncology, vol.9, issue.5, pp.502-510, 2012.
DOI : 10.1002/jso.23053

S. Ram, H. Seirawan, S. K. Kumar, and G. T. Clark, Prevalence and impact of sleep disorders and sleep habits in the United States, Sleep and Breathing, vol.43, issue.11, pp.63-70, 2010.
DOI : 10.1007/s11325-009-0281-3

S. Liang, C. Kuo, Y. Hu, and Y. Cheng, A rule-based automatic sleep staging method, Journal of Neuroscience Methods, vol.205, issue.1, pp.169-176, 2012.
DOI : 10.1016/j.jneumeth.2011.12.022

A. Hobson and J. , A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, Electroencephalography and Clinical Neurophysiology, vol.26, issue.6, p.644, 1969.
DOI : 10.1016/0013-4694(69)90021-2

C. Iber, S. Ancoli-israel, A. L. Chesson, J. , and S. F. Quan, AASM Manual for the Scoring of Sleep and Associated Events, pp.2015-2022

M. Hanaoka, M. Kobayashi, and H. Yamazaki, Automated sleep stage scoring by decision tree learning, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1751-1754, 2001.
DOI : 10.1109/IEMBS.2001.1020556

F. Ebrahimi, M. Mikaeili, E. Estrada, and H. Nazeran, Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1151-1154, 2008.
DOI : 10.1109/IEMBS.2008.4649365

N. Kerkeni, F. Alexandre, M. H. Bedoui, L. Bougrain, and M. Dogui, Automatic Classification of Sleep Stages on a EEG Signal by Artificial Neural Networks, Proceedings of the 5th WSEAS International Conference on Signal, Speech and Image Processing, pp.128-131, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00000512

M. E. Tagluk, N. Sezgin, and M. Akin, Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG, Journal of Medical Systems, vol.36, issue.2, pp.717-725, 2010.
DOI : 10.1007/s10916-009-9286-5

D. Álvarez-esté-vez, J. M. Ferná-ndez-pastoriza, E. Herná-ndez-pereira, and V. Moret-bonillo, A method for the automatic analysis of the sleep macrostructure in continuum, Expert Systems with Applications, vol.40, pp.5-1796, 2013.

C. A. Kushida, Practice parameters for the indications for polysomnography and related procedures: an update for 2005, Sleep, vol.28, issue.4, pp.499-521, 2005.

J. Haton, M. Haton, and F. Charpillet, Numeric/Symbolic Approaches for Data and Information Fusion, 1998.
URL : https://hal.archives-ouvertes.fr/inria-00098419

M. Merino, O. Rivera, I. Gomez, A. Molina, and E. Dorronzoro, A Method of EOG Signal Processing to Detect the Direction of Eye Movements, 2010 First International Conference on Sensor Device Technologies and Applications, pp.100-105, 2010.
DOI : 10.1109/SENSORDEVICES.2010.25

V. C. Figueroa-helland, A. Gapelyuk, A. Suhrbier, M. Riedl, T. Penzel et al., Investigation of an Automatic Sleep Stage Classification by Means of Multiscorer Hypnogram, Methods of Information in Medicine, vol.49, issue.5, pp.5-467, 2010.
DOI : 10.3414/ME09-02-0052

S. Güne?, K. Polat, and ?. Yosunkaya, Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Systems with Applications, pp.12-7922, 2010.

M. Stevens, N. Sleep-forman, and G. , An extensive empirical study of feature selection metrics for text classification, Sleep Physiology and Sleep Deprivation J. Mach. Learn. Res, vol.3, pp.1289-130, 2003.