Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating

Arzi, 2000, Humans can learn new information during sleep, Nat. Neurosci., 15, 1460, 10.1038/nn.3193

Mahowald, 2005, Insights from studying human sleep disorders, Nature, 437, 1279, 10.1038/nature04287

Rechtschaffen, 1968

Iber, 2005

Moser, 2009, Sleep classification according to aasm and rechtschaffen & kales: effects on sleep scoring parameters, Sleep, 32, 139, 10.1093/sleep/32.2.139

Penzel, 2000, Computer based sleep recording and analysis, Sleep Med. Rev., 4, 131, 10.1053/smrv.1999.0087

Lin, 2008, Noninvasive neural prostheses using mobile and wireless EEG, Proc. IEEE, 96, 1167, 10.1109/JPROC.2008.922561

Liang, 2012, Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models, IEEE Trans. Instrum. Meas., 61, 1649, 10.1109/TIM.2012.2187242

Motamedi-Fakhr, 2014, Signal processing techniques applied to human sleep EEG signals a review, Biomed. Signal Process. Control, 10, 21, 10.1016/j.bspc.2013.12.003

Agarwal, 2001, Computer-assisted sleep staging, IEEE Trans. Biomed. Eng., 48, 1412, 10.1109/10.966600

Held, 2006, Extracting fuzzy rules from polysomnographic recordings for infant sleep classification, IEEE Trans. Biomed. Eng., 53, 1954, 10.1109/TBME.2006.881798

Krakovská, 2011, Automatic sleep scoring: a search for an optimal combination of measures, Artif. Intell. Med., 53, 25, 10.1016/j.artmed.2011.06.004

Ronzhina, 2012, Sleep scoring using artificial neural networks, Sleep Med. Rev., 16, 251, 10.1016/j.smrv.2011.06.003

Hsu, 2013, Automatic sleep stage recurrent neural classifier using energy features of EEG signals, Neurocomputing, 104, 105, 10.1016/j.neucom.2012.11.003

Zhu, 2014, Analysis and classification of sleep stages based on difference visibility graphs from a single channel EEG signal, IEEE J. Biomed. Health Inf., PP

Imtiaz, 2014, A low computational cost algorithm for REM sleep detection using single channel EEG, Ann. Biomed. Eng., 42, 2344, 10.1007/s10439-014-1085-6

Koch, 2014, Automatic sleep classification using a data-driven topic model reveals latent sleep states, J. Neurosci. Methods, 235, 130, 10.1016/j.jneumeth.2014.07.002

Humeau-Heurtier, 2015, Analysis of laser speckle contrast images variability using a novel empirical mode decomposition: Comparison of results with laser doppler flowmetry signals variability, IEEE Trans. Med. Imaging, 34, 618, 10.1109/TMI.2014.2364079

Goldberger, 2000, Components of a new research resource for complex physiologic signals, Circulation, 101, e215, 10.1161/01.CIR.101.23.e215

Kemp, 2000, Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG, IEEE Trans. Biomed. Eng., 47, 1185, 10.1109/10.867928

B. Kemp, The sleep-edf database online (2014). URL http://www.physionet.org/physiobank/database/sleep-edf/.

Berthomier, 2007, Automatic analysis of single-channel sleep EEG: validation in healthy individuals, Sleep, 30, 1587, 10.1093/sleep/30.11.1587

Huang, 1998, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci., 454, 903, 10.1098/rspa.1998.0193

Park, 2011, Time-frequency analysis of EEG asymmetry using bivariate empirical mode decomposition, IEEE Trans. Neural Syst. Rehabil. Eng., 19, 366, 10.1109/TNSRE.2011.2116805

Wang, 2015, The removal of eog artifacts from EEG signals using independent component analysis and multivariate empirical mode decomposition, IEEE J. Biomed. Health Inform., PP

Wu, 2009, Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adapt. Data Anal., 01, 1, 10.1142/S1793536909000047

Torres, 2011, A complete ensemble empirical mode decomposition with adaptive noise, 4144

Kruskal, 1952, Use of ranks in one-criterion variance analysis, Journal of the American Statistical Association, 47, 583, 10.1080/01621459.1952.10483441

Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655

Polikar, 2006, Ensemble based systems in decision making, IEEE Circuits Syst. Mag., 6, 21, 10.1109/MCAS.2006.1688199

Marshall, 2006, Boosting slow oscillations during sleep potentiates memory, Nature, 444, 610, 10.1038/nature05278

tieng Wu, 2015, Assess sleep stage by modern signal processing techniques, IEEE Trans. Biomed. Eng., 62, 1159, 10.1109/TBME.2014.2375292

Iranzo, 2006, Rapid-eye-movement sleep behaviour disorder as an early marker for a neurodegenerative disorder: a descriptive study, Lancet Neurol., 5, 572, 10.1016/S1474-4422(06)70476-8

Siuly, 2014, A novel statistical algorithm for multiclass EEG signal classification, Eng. Appl. Artif. Intell., 34, 154, 10.1016/j.engappai.2014.05.011

beyli, 2009, Decision support systems for time-varying biomedical signals: EEG signals classification, Expert Syst. Appl., 36, 2275, 10.1016/j.eswa.2007.12.025

Fatma Guler, 2007, Multiclass support vector machines for EEG-signals classification, IEEE Trans. Inf. Technol. Biomed., 11, 117, 10.1109/TITB.2006.879600

Murphy, 2012

Fraiwan, 2012, Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier, Comput. Methods Prog. Biomed., 108, 10, 10.1016/j.cmpb.2011.11.005

Charbonnier, 2011, Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging, Comput. Biol. Med., 41, 380, 10.1016/j.compbiomed.2011.04.001

Kayikcioglu, 2015, Fast and accurate PLS-based classification of EEG sleep using single channel data, Expert Syst. Appl., 42, 7825, 10.1016/j.eswa.2015.06.010

Long, 2014, Analyzing respiratory effort amplitude for automated sleep stage classification, Biomed. Signal Process. Control, 14, 197, 10.1016/j.bspc.2014.08.001

Seiffert, 2010, RUSBoost: a hybrid approach to alleviating class imbalance, IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans, 40, 185, 10.1109/TSMCA.2009.2029559