The prevalence, cost implications, and management of sleep disorders: An overview, Sleep Breathing, vol.6, pp.85-102, 2002. ,
Association between sleep disorders, obesity, and exercise: A review, Nature Sci. Sleep, vol.5, pp.27-35, 2013. ,
The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications, Sleep Med, 2007. ,
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. ,
Automatic sleep staging using state machine-controlled decision trees, Proc. 37th Annu. Int. Conf, pp.378-381, 2015. ,
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. ,
Eeg-based automatic sleep stage classification, Biomed. J. Sci. Tech. Res, vol.7, issue.4, pp.1-6, 2018. ,
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. ,
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. ,
Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis, PeerJ, vol.6, p.5247, 2018. ,
Effect of feature extraction on automatic sleep stage classification by artificial neural network, Metrol. Meas. Syst, vol.24, issue.2, pp.229-240, 2017. ,
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. ,
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. ,
Hyclasss: A hybrid classifier for automatic sleep stage scoring, IEEE J. Biomed. Health Inform, vol.22, issue.2, pp.375-385, 2018. ,
Automatic sleep stage scoring with single-channel EEG using convolutional neural networks, 2016. ,
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 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
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
Fusion symbolique et données polysomnographiques, 2013. ,
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
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
Data fusion in 2D and 3D image processing: An overview, Proc. 10th Brazilian Symp, pp.127-134, 1997. ,
DOI : 10.1109/sigra.1997.625158
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
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
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
A fuzzy inference system for sleep staging, Proc. IEEE Int. Conf. Fuzzy Syst, pp.2104-2107, 2011. ,
DOI : 10.1109/fuzzy.2011.6007380
A rule-based automatic sleep staging method, J. Neurosci. Methods, vol.205, issue.1, pp.169-176, 2012. ,
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
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
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. ,
Keynote: Personalized medicine enabled by FSC.X technology, Proc. IEEE Faible Tension Faible Consommation (FTFC), pp.1-2, 2014. ,
A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease, Neurocomputing, vol.320, pp.195-202, 2018. ,
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
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
Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim, vol.11, issue.4, pp.341-359, 1997. ,
Power system voltage stability analysis using modified differential evolution, Proc. Int. Conf. Comput, pp.382-387, 2011. ,
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
A tool for analysis and classification of sleep stages, Proc. Int. Conf, pp.307-310, 2011. ,
The measurement of observer agreement for categorical data, Biometrics, vol.33, issue.1, pp.159-174, 1977. ,
How to interpret the results of a sleep study, J. Community Hospital Internal Med. Perspect, vol.4, issue.5, p.24983, 2014. ,
Sleep architecture in patients with primary snoring and obstructive sleep apnea, Basic Clin. Neurosci, vol.9, issue.2, pp.147-156, 2018. ,
Classification and Regression Trees, 2017. ,
Random forests, Mach. Learn, vol.45, issue.1, pp.5-32, 2001. ,
, Discriminant Analysis and Statistical Pattern Recognition, vol.544, 2004.
Support vector machines, IEEE Intell. Syst. Appl, vol.13, issue.4, pp.18-28, 2008. ,
A fuzzy K-nearest neighbor algorithm, IEEE Trans. Syst, vol.15, issue.4, pp.580-585, 1985. ,
, Machine Learning: A Probabilistic Perspective, 2012.
Automated EEG sleep staging in the term-age baby using a generative modelling approach, J. Neural Eng, vol.15, issue.3, p.2019, 2018. ,