Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting

Rechtschaffen, 1968

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

Zhu, 2014, Analysis and classification of sleep stages based on difference visibility graphs from a single-channel eeg signal, IEEE J. Biomed. Health Inf., 18, 1813, 10.1109/JBHI.2014.2303991

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

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

Tsinalis, 2016, Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders, Ann. Biomed. Eng., 44, 1587, 10.1007/s10439-015-1444-y

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

Hassan, 2016, A decision support system for automatic sleep staging from EEG signals using tunable q-factor wavelet transform and spectral features, J. Neurosci. Methods, 271, 107, 10.1016/j.jneumeth.2016.07.012

Dong, 2010, Automated sleep staging technique based on the empirical mode decomposition algorithm: a preliminary study, Adv. Adapt. Data Anal., 2, 267, 10.1142/S1793536910000483

Lajnef, 2015, Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines, J. Neurosci. Methods, 250, 94, 10.1016/j.jneumeth.2015.01.022

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

Hassan, 2016, Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating, Biomed. Signal Process. Control, 24, 1, 10.1016/j.bspc.2015.09.002

Hassan, 2016, Automatic sleep scoring using statistical features in the EMD domain and ensemble methods, Biocybern. Biomed. Eng., 36, 248, 10.1016/j.bbe.2015.11.001

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

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

Huang, 2014, Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels, Front. Neurosci., 8, 1

Charbonnier, 2011, Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging, Computers in Biology and Medicine, 41, 380, 10.1016/j.compbiomed.2011.04.001

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

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

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

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

The dreams subjects database, URL http://www.tcts.fpms.ac.be/devuyst/Databases/DatabaseSubjects/, 2004 (accessed 01.04.2015).

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

Hassan, 2016, Epileptic seizure detection in EEG signals using tunable-q factor wavelet transform and bootstrap aggregating, Comput. Methods Programs Biomed., 137, 247, 10.1016/j.cmpb.2016.09.008

Hassan, 2015, Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos, Comput. Methods Programs Biomed., 122, 341, 10.1016/j.cmpb.2015.09.005

Hassan, 2016, Automatic identification of epileptic seizures from EEG signals using linear programming boosting, Comput. Methods Programs Biomed., 136, 65, 10.1016/j.cmpb.2016.08.013

Hassan, 2016, Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting, Biomed. Signal Process. Control, 29, 22, 10.1016/j.bspc.2016.05.009

Hassan, 2016, Computer-aided obstructive sleep apnea identification using statistical features in the EMD domain and extreme learning machine, Biomed. Phys. Eng. Express, 2, 035003, 10.1088/2057-1976/2/3/035003

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

Murphy, 2012

Hassan, 2015, Epilepsy and seizure detection using statistical features in the complete ensemble empirical mode decomposition domain, 1

Doroshenkov, 2007, Classification of human sleep stages based on eeg processing using hidden Markov models, Biomed. Eng., 41, 24, 10.1007/s10527-007-0006-5

Vural, 2010, Determination of sleep stage separation ability of features extracted from EEG signals using principal component analysis, J. Med. Syst., 34, 83, 10.1007/s10916-008-9218-9

Liang, 2012, A rule-based automatic sleep staging method, J. Neurosci. Methods, 205, 169, 10.1016/j.jneumeth.2011.12.022

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

Hassan, 2017, An automated method for sleep staging from EEG signals using normal inverse gaussian parameters and adaptive boosting, Neurocomputing, 219, 76, 10.1016/j.neucom.2016.09.011