A comparative review on sleep stage classification methods in patients and healthy individuals

Park, 2000, Automated sleep stage scoring using hybrid rule-and case-based reasoning, Comput. Biomed. Res., 33, 330, 10.1006/cbmr.2000.1549 Yeh, 2012, Development of the insomnia screening scale based on icsd-ii, Int. J. Psychiatry Clin. Pract., 16, 259, 10.3109/13651501.2011.640938 Torabi-Nami, 2015, Withstanding the obstructive sleep apnea syndrome at the expense of arousal instability, altered cerebral autoregulation and neurocognitive decline, J. Integr. Neurosci., 14, 169, 10.1142/S0219635215500144 Younes, 2015, Utility of technologist editing of polysomnography scoring performed by a validated automatic system, Ann. Am. Thorac. Soc., 12, 1206 Malhotra, 2013, Performance of an automated polysomnography scoring system versus computer-assisted manual scoring, Sleep, 36, 573, 10.5665/sleep.2548 Collop, 2002, Scoring variability between polysomnography technologists in different sleep laboratories, Sleep Med., 3, 43, 10.1016/S1389-9457(01)00115-0 Chapotot, 2010, Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules, Int. J. Adapt. Control Signal Process., 24, 409 Ferri, 2012, A new quantitative automatic method for the measurement of non-rapid eye movement sleep electroencephalographic amplitude variability, J. Sleep Res., 21, 212, 10.1111/j.1365-2869.2011.00981.x Chiu, 2014, Recognition of sleep stages based on a combined neural network and fuzzy system using wavelet transform features, Biomed. Eng.: Appl. Basis Commun., 26, 1450029 Motamedi-Fakhr, 2014, Signal processing techniques applied to human sleep eeg signalsa review, Biomed. Signal Process. Control, 10, 21, 10.1016/j.bspc.2013.12.003 Shokrollahi, 2015, A review of sleep disorder diagnosis by electromyogram signal analysis, Crit. Rev. Biomed. Eng., 43, 10.1615/CritRevBiomedEng.2015012037 Helland, 2010, Investigation of an automatic sleep stage classification by means of multiscorer hypnogram, Methods Inf. Med., 49, 467, 10.3414/ME09-02-0052 Özşen, 2013, Classification of sleep stages using class-dependent sequential feature selection and artificial neural network, Neural Comput. Appl., 23, 1239, 10.1007/s00521-012-1065-4 Pan, 2012, A transition-constrained discrete hidden Markov model for automatic sleep staging, Biomed. Eng. Online, 11, 1, 10.1186/1475-925X-11-52 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 Tagluk, 2010, Estimation of sleep stages by an artificial neural network employing eeg, emg and eog, J .Med. Syst., 34, 717, 10.1007/s10916-009-9286-5 Shi, 2015, Multi-channel eeg-based sleep stage classification with joint collaborative representation and multiple kernel learning, J. Neurosci. Methods, 254, 94, 10.1016/j.jneumeth.2015.07.006 Piryatinska, 2012, Optimal channel selection for analysis of eeg-sleep patterns of neonates, Comput. Methods Programs Biomed., 106, 14, 10.1016/j.cmpb.2011.08.002 Khalighi, 2013, Automatic sleep staging: a computer assisted approach for optimal combination of features and polysomnographic channels, Expert Syst. Appl., 40, 7046, 10.1016/j.eswa.2013.06.023 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 Al-Jumeily, 2015, A novel method of early diagnosis ofAlzheimers disease based on eeg signals, Sci. World J., 2015, 10.1155/2015/931387 Berthomier, 2007, Automatic analysis of single-channel sleep eeg: validation in healthy individuals, Sleep-New York Then Westchester, 30, 1587 Flexer, 2005, A reliable probabilistic sleep stager based on a single eeg signal, Artif. Intell. Med., 33, 199, 10.1016/j.artmed.2004.04.004 Hsu, 2013, Automatic sleep stage recurrent neural classifier using energy features of eeg signals, Neurocomputing, 104, 105, 10.1016/j.neucom.2012.11.003 Šušmáková, 2008, Discrimination ability of individual measures used in sleep stages classification, Artif. Intell. Med., 44, 261, 10.1016/j.artmed.2008.07.005 Penzel, 2000, Computer based sleep recording and analysis, Sleep Med. Rev., 4, 131, 10.1053/smrv.1999.0087 Kim, 2015, Rethinking aasm guideline for split-night polysomnography in asian patients with obstructive sleep apnea, Sleep and Breathing, 19, 1273, 10.1007/s11325-015-1158-2 Novelli, 2010, Sleep classification according to aasm and Rechtschaffen and Kales: effects on sleep scoring parameters of children and adolescents, J Sleep Res., 19, 238, 10.1111/j.1365-2869.2009.00785.x A. Rechtschaffen, A. Kales, A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects (1968). Berry, 2012, Rules for scoring respiratory events in sleep: update of the 2007 aasm manual for the scoring of sleep and associated events, J. Clin. Sleep Med., 8, 597, 10.5664/jcsm.2172 Hori, 2001, Proposed supplements and amendments to a manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, the Rechtschaffen & Kales (1968) standard, Psychiatry Clin. Neurosci., 55, 305, 10.1046/j.1440-1819.2001.00810.x Himanen, 2000, Limitations of Rechtschaffen and Kales, Sleep Med. Rev., 4, 149, 10.1053/smrv.1999.0086 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 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 Şen, 2014, A comparative study on classification of sleep stage based on eeg signals using feature selection and classification algorithms, J. Med. Syst., 38, 1, 10.1007/s10916-014-0018-0 Ronzhina, 2012, Sleep scoring using artificial neural networks, Sleep Med. Rev., 16, 251, 10.1016/j.smrv.2011.06.003 Kim, 1990, [problems and a resolution of clinical sleep research–a recording system of polysomnography], Seishin shinkeigaku zasshi= Psychiatria et neurologia Japonica, 93, 59 Weiss, 2009, Spatio-temporal analysis of monofractal and multifractal properties of the human sleep eeg, J. Neurosci. Methods, 185, 116, 10.1016/j.jneumeth.2009.07.027 Hamaneh, 2014, Automated removal of ekg artifact from eeg data using independent component analysis and continuous wavelet transformation, IEEE Trans. Biomed. Eng., 61, 1634, 10.1109/TBME.2013.2295173 Crespo-Garcia, 2008, Muscle artifact removal from human sleep eeg by using independent component analysis, Ann. Biomed Eng., 36, 467, 10.1007/s10439-008-9442-y Koley, 2012, An ensemble system for automatic sleep stage classification using single channel eeg signal, Comput. Biol. Med., 42, 1186, 10.1016/j.compbiomed.2012.09.012 Muthuswamy, 1998, Spectral analysis methods for neurological signals, J. Neurosci. Methods, 83, 1, 10.1016/S0165-0270(98)00065-X Correa, 2007, Artifact removal from eeg signals using adaptive filters in cascade, 90, 012081 Croft, 2005, Eog correction: a comparison of four methods, Psychophysiology, 42, 16, 10.1111/j.1468-8986.2005.00264.x Jiang, 2007, An automatic analysis method for detecting and eliminating ecg artifacts in eeg, Comput. Biol. Med., 37, 1660, 10.1016/j.compbiomed.2007.03.007 Fraiwan, 2010, Classification of sleep stages using multi-wavelet time frequency entropy and lda, Methods Inf. Med., 49, 230, 10.3414/ME09-01-0054 Bishop, 2006, Pattern recognition, Mach. Learn., 128, 1 Hjorth, 1970, Eeg analysis based on time domain properties, Electroencephalogr. Clin. Neurophysiol., 29, 306, 10.1016/0013-4694(70)90143-4 Herrera, 2013, Combination of heterogeneous eeg feature extraction methods and stacked sequential learning for sleep stage classification, Int. J. Neural Syst., 23, 1350012, 10.1142/S0129065713500123 B.-L. Su, Y. Luo, C.-Y. Hong, M.L. Nagurka, C.-W. Yen, Detecting slow wave sleep using a single eeg signal channel, J. Neurosci. Methods 24347–52. Stepnowsky, 2013, Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters, Sleep Med., 14, 1199, 10.1016/j.sleep.2013.04.022 Brignol, 2013, Phase space and power spectral approaches for eeg-based automatic sleep–wake classification in humans: a comparative study using short and standard epoch lengths, Comput. Methods Programs Biomed., 109, 227, 10.1016/j.cmpb.2012.10.002 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 Akaike, 1974, A new look at the statistical model identification, IEEE Trans. Automat. Contr., 19, 716, 10.1109/TAC.1974.1100705 Sörnmo, 2005, 8 Liang, 2012, A rule-based automatic sleep staging method, J. Neurosci. Methods, 205, 169, 10.1016/j.jneumeth.2011.12.022 Güneş, 2010, Efficient sleep stage recognition system based on eeg signal using k-means clustering based feature weighting, Expert Syst. Appl., 37, 7922, 10.1016/j.eswa.2010.04.043 Chua, 2010, Application of higher order statistics/spectra in biomedical signalsa review, Med. Eng. Phys., 32, 679, 10.1016/j.medengphy.2010.04.009 Acharya, 2010, Analysis and automatic identification of sleep stages using higher order spectra, Int. J. Neural Syst., 20, 509, 10.1142/S0129065710002589 A. Petropulu, Higher-order spectral analysis, V.K. Madisetti D.B. Williams Digital Signal Processing HandbookChapman & Hall/CRCnetBASE (1999). Bajaj, 2013, Automatic classification of sleep stages based on the time-frequency image of eeg signals, Comput. Methods Programs Biomed., 112, 320, 10.1016/j.cmpb.2013.07.006 Schlüter, 2012, An approach for automatic sleep stage scoring and apnea-hypopnea detection, Front. Comput. Sci., 6, 230, 10.1007/s11704-012-2872-6 Fraiwan, 2011, Time frequency analysis for automated sleep stage identification in fullterm and preterm neonates, J. Med. Syst., 35, 693, 10.1007/s10916-009-9406-2 Latchoumane, 2011, Quantification of brain macrostates using dynamical nonstationarity of physiological time series, IEEE Trans. Biomed. Eng., 58, 1084, 10.1109/TBME.2009.2034840 Karimzadeh, 2015, Presenting efficient features for automatic cap detection in sleep eeg signals, 448 Sabeti, 2009, Entropy and complexity measures for eeg signal classification of schizophrenic and control participants, Artif. Intell. Med., 47, 263, 10.1016/j.artmed.2009.03.003 Acharya, 2005, Non-linear analysis of eeg signals at various sleep stages, Comput. Methods Programs Biomed., 80, 37, 10.1016/j.cmpb.2005.06.011 Piryatinska, 2009, Automated detection of neonate eeg sleep stages, Comput. Methods Programs Biomed., 95, 31, 10.1016/j.cmpb.2009.01.006 Mandelbrot, 1967, How long is the coast of Britain, Science, 156, 636, 10.1126/science.156.3775.636 Esteller, 2001, A comparison of waveform fractal dimension algorithms, IEEE Trans. Circuits Syst. I, 48, 177, 10.1109/81.904882 Zoubek, 2007, Feature selection for sleep/wake stages classification using data driven methods, Biomed. Signal Process. Control, 2, 171, 10.1016/j.bspc.2007.05.005 Vural, 2010, Determination of sleep stage separation ability of features extracted from eeg signals using principle component analysis, J Med Syst, 34, 83, 10.1007/s10916-008-9218-9 Xue, 2014, Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms, Appl. Soft Comput., 18, 261, 10.1016/j.asoc.2013.09.018 Fukunaga, 2013 Vapnik, 2013 Wu, 2015, Assess sleep stage by modern signal processing techniques, IEEE Trans. Biomed. Eng., 62, 1159, 10.1109/TBME.2014.2375292 Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Duda, 2012 Schmidhuber, 2015, Deep learning in neural networks: an overview, Neural Netw., 61, 85, 10.1016/j.neunet.2014.09.003 Sinha, 2008, Artificial neural network and wavelet based automated detection of sleep spindles, rem sleep and wake states, J. Med. Syst., 32, 291, 10.1007/s10916-008-9134-z Fraley, 2002, Model-based clustering, discriminant analysis, and density estimation, J. Am. Stat. Assoc., 97, 611, 10.1198/016214502760047131 Reynolds, 1995, Robust text-independent speaker identification using Gaussian mixture speaker models, IEEE Trans. Speech Audio Process., 3, 72, 10.1109/89.365379 Bilmes, 1998, A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models, Int. Comput. Sci. Inst., 4, 126 Yaghouby, 2015, Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables, Comput. Biol. Med., 59, 54, 10.1016/j.compbiomed.2015.01.012 MacQueen, 1967, Some methods for classification and analysis of multivariate observations, 1, 281 Agarwal, 2001, Computer-assisted sleep staging, IEEE Trans. Biomed. Eng., 48, 1412, 10.1109/10.966600 Berkhin, 2006, A Survey of Clustering Data Mining Techniques, 25 Murtagh, 1983, A survey of recent advances in hierarchical clustering algorithms, Comput. J., 26, 354, 10.1093/comjnl/26.4.354 Jarvis, 1973, Clustering using a similarity measure based on shared near neighbors, IEEE Trans. Comput., 100, 1025, 10.1109/T-C.1973.223640 P. PhysioBank, Physionet: components of a new research resource for complex physiologic signals, Circulation. v101 i23. e215-e220. 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 Terzano, 2001, Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (cap) in human sleep, Sleep Med., 2, 537, 10.1016/S1389-9457(01)00149-6 Elahi, 2013, Estimation of hypnosis susceptibility based on electroencephalogram signal features, Scientia Iranica, 20, 730 Jensen, 1996, Autoregressive modeling with exogenous input of middle-latency auditory-evoked potentials to measure rapid changes in depth of anesthesia., Methods Inf. Med., 35, 256, 10.1055/s-0038-1634658 Livia Fantini, 2003, Slowing of electroencephalogram in rapid eye movement sleep behavior disorder, Ann. Neurol., 53, 774, 10.1002/ana.10547