Borbély AA, et al. Sleep homeostasis and models of sleep regulation. J Biol Rhythms. 1999;14(6):557-568.
Tononi G, Cirelli C. Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 2014;81:12-34.
Borbély AA, et al. The two-process model of sleep regulation: a reappraisal. J Sleep Res 2016;25:131-143.
Gaggioni G, et al. Neuroimaging, cognition, light and circadian rhythms. Front Syst Neurosci 2014;8:126.
Fattinger S, et al. Theta waves in children's waking electroencephalogram resemble local aspects of sleep during wakefulness. Sci Rep. 2017;7(1):11187.
Holm A, et al. Estimating brain load from the EEG. Sci World J. 2009;9:639-651.
Groppe DM, et al. Dominant frequencies of resting human brain activity as measured by the electrocorticogram. Neuroimage. 2013;79:223-233.
Borghini G, et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev 2014;44:58-75.
Kuo C-C, et al. Classification of intended motor movement using surface EEG ensemble empirical mode decomposition. In: 2011 Annual international conference of the IEEE Engineering in Medicine and Biology Society; August 30-September 3, 2011; Boston, MA. doi:10.1109/IEMBS.2011.6091550.
Wascher E, et al. Driver state examination-treading new paths. Accid Anal Prev 2016;91:157-165.
Jap BT, et al. Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst Appl 2009;36:2352-2359.
Jagannath M, et al. Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator. Appl Ergon. 2014;45(4):1140-1147.
Foong R, et al. An Analysis on Driver Drowsiness Based on Reaction Time and EEG Band Power. In: 37th annual international conference of the IEEE Engineering in Medicine and Biology Society; August 25-29, 2015; Milan, Italy. doi:10.1109/ EMBC.2015.7320244.
Mahachandra M, Garnaby ED. The effectiveness of in-vehicle peppermint fragrance to maintain car driver's alertness. Procedia Manuf 2015;4:471-477.
Natarajan K, et al. Nonlinear analysis of EEG signals at different mental states. Biomed Eng Online 2004;3:7.
Yin Y, Shang P. Multivariate weighted multiscale permutation entropy for complex time series. Nonlinear Dyn 2017;88:1707-1722.
Ma Y, et al. Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. Sleep Med. Rev 2018;37:85-93.
Zanin M, et al. Permutation entropy and its main biomedical and econophysics applications: a review. Entropy 2012;14:1553-1577.
Bandt C, Pompe B. Permutation entropy: a natu4ral complexity measure for time series. Phys Rev Lett 2002;88:174102.
Groth A. Visualization of coupling in time series by order recurrence plots. Phys Rev E Stat Nonlin Soft Matter Phys. 2005;72(4 Pt 2):046220.
Olofsen E, et al. Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. Br J Anaesth. 2008;101(6):810-821.
Cao Y, et al. Detecting dynamical changes in time series using the permutation entropy. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;70(4 Pt 2):046217.
Keller K, Wittfeld K. Distances of time series components by means of symbolic dynamics. Int J Bifurcat Chaos 2004;14:693-703.
Li X, et al. Predictability analysis of absence seizures with permutation entropy. Epilepsy Res. 2007;77(1):70-74.
Ouyang G, et al. Ordinal pattern based similarity analysis for EEG recordings. Clin Neurophysiol. 2010;121(5):694-703.
Olofsen E, et al. Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. Br J Anaesth. 2008;101(6):810-821.
Silva A, et al. Performance of anesthetic depth indexes in rabbits under propofol anesthesia: prediction probabilities and concentration-effect relations. Anesthesiology. 2011;115(2):303-314.
Silva A, et al. Comparison of anesthetic depth indexes based on thalamocortical local field potentials in rats. Anesthesiology 2010;112:355.
Schinkel S, et al. Order patterns recurrence plots in the analysis of ERP data. Cogn Neurodyn. 2007;1(4):317-325.
Schinkel S, et al. Brain signal analysis based on recurrences. J Physiol Paris. 2009;103(6):315-323.
Thul A, et al. EEG entropy measures indicate decrease of cortical information processing in Disorders of Consciousness. Clin Neurophysiol. 2016;127(2):1419-1427.
Wielek T, et al. Sleep in patients with disorders of consciousness characterized by means of machine learning. PLoS ONE 2018;13:1-14.
Nicolaou N, Georgiou J. The use of permutation entropy to characterize sleep electroencephalograms. Clin EEG Neurosci 2011;42:24-28.
Tosun PD, et al. Effects of ageing and sex on complexity in the human sleep EEG: A comparison of three symbolic dynamic analysis methods. Complexity 2019;2019:9254309.
Linkenkaer-Hansen K, et al. Long-range temporal correlations and scaling behavior in human brain oscillations. J Neurosci. 2001;21(4):1370-1377.
Smith RJ, et al. Long-Range temporal correlations reflect treatment response in the electroencephalogram of patients with infantile spasms. Brain Topogr. 2017;30(6):810-821.
Costa M, et al. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett. 2002;89(6):068102.
Costa M, et al. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys. 2005;71(2 Pt 1):021906.
Liu Q, et al. EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks. Comput Math Methods Med 2015;2015:232381.
Chen C, et al. Multiscale entropy-based analysis and processing of EEG signal during watching 3DTV. Measurement 2018;125:432-437.
Lu W-Y, et al. Multiscale entropy of electroencephalogram as a potential predictor for the prognosis of neonatal seizures. PLoS One 2015;10:0144732.
Norris PR, et al. Heart rate multiscale entropy at three hours predicts hospital mortality in 3,154 trauma patients. Shock. 2008;30(1):17-22.
Silva LEV, et al. Multiscale entropy analysis of heart rate variability in heart failure, hypertensive, and sinoaorticdenervated rats: classical and refined approaches. Am J Physiol Regul Integr Comp Phy 2016;311:150-156.
Udhayakumar RK, et al. Multiscale entropy profiling to estimate complexity of heart rate dynamics. Phys Rev E. 2019;100(1-1):012405.
Yao Z, et al. Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Comput Biol. 2010;11:1001006.
Mourtazaev MS, et al. Age and gender affect different characteristics of slow waves in the sleep EEG. Sleep. 1995;18(7):557-564.
Zhang GQ, et al. The national sleep research resource: towards a sleep data commons. J Am Med Inform Assoc. 2018;25(10):1351-1358.
Quan SF, et al. The sleep heart health study: design, rationale, and methods. Sleep. 1997;20(12):1077-1085.
Thomas RJ, et al. Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method. Sleep. 2007;30(12):1756-1769.
Sleep Heart Health Study. Overall_shhs1. https://sleepdata. org/datasets/shhs/variables/overall_shhs1.
Duhamel P, Vetterli M. Fast Fourier transforms: a tutorial review and a state of the art. Signal Process (Elsevier) 1990;19:259-299.
Viola AU, et al. PER3 polymorphism predicts sleep structure and waking performance(Article). Curr Biol. 2007;17(7):613-618.
Ly JQM, et al. Circadian regulation of human cortical excitability. Nat. Commun 2016;7:11828.
Cajochen C, et al. Separation of circadian and wake duration-dependent modulation of EEG activation during wakefulness. Neuroscience. 2002;114(4):1047-1060.
Rétey JV, et al. Adenosinergic mechanisms contribute to individual differences in sleep deprivation-induced changes in neurobehavioral function and brain rhythmic activity. J Neurosci 2006;26:10472-10479.
Finelli LA, et al. Dual electroencephalogram markers of human sleep homeostasis: correlation between theta activity in waking and slow-wave activity in sleep. Neuroscience. 2000;101(3):523-529.
Hung CS, et al. Local experience-dependent changes in the wake EEG after prolonged wakefulness. Sleep. 2013;36(1):59-72.
Feinberg I, et al. Systematic trends across the night in human sleep cycles. Psychophysiology. 1979;16(3):283-291.
Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315-1316.
Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32-35.
Zar JH. Significance testing of the spearman rank correlation coefficient. Publications Am Stat Assoc. 1972;67:578-580.
Mann HB. Non-parametric test against trend. Econometrica 1945;13:245-259.
Kenward MG, et al. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics. 1997;53(3):983-997.
González J, et al. Decreased electrocortical temporal complexity distinguishes sleep from wakefulness. Sci Rep. 2019;9(1):18457.
Carrier J, et al. Sex differences in age-related changes in the sleep-wake cycle. Front Neuroendocrinol. 2017;47:66-85.
Fell J, et al. Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. Electroencephalogr Clin Neurophysiol. 1996;98(5):401-410.
Achermann P, et al. Correlation dimension of the human sleep electroencephalogram: cyclic changes in the course of the night. Eur J Neurosci. 1994;6(3):497-500.
Steriade M. The corticothalamic system in sleep. Front Biosci. 2003;8:d878-d899.
Massimini M, et al. Breakdown of cortical effective connectivity during sleep. Science 2005;309:2228-2232.
Boly M, et al. Hierarchical clustering of brain activity during human nonrapid eye movement sleep. Proc Natl Acad Sci U S A. 2012;109(15):5856-5861.
Colton H, Altevogt B. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. In: Washington, DC: National Academies Press, 2006.
Sarasso S, et al. Hippocampal sleep spindles preceding neocortical sleep onset in humans. Neuroimage. 2014;86:425-432.
Meisel C, et al. The interplay between long-and short-range temporal correlations shapes cortex dynamics across vigilance states. J Neurosci. 2017;37:10114-10124.
Gaggioni G, et al. Human fronto-parietal response scattering subserves vigilance at night. Neuroimage. 2018;175:354-364.
Meisel C, et al. Decline of long-range temporal correlations in the human brain during sustained wakefulness. Sci Rep. 2017;7(1):11825.
Schartner M, et al. Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia. PLoS ONE 2015;10(8):e0133532.
Wielek T, et al. Sleep in patients with disorders of consciousness characterized by means of machine learning. PLoS One. 2018;13(1):e0190458.
Mateos DM, et al. Measures of entropy and complexity in altered states of consciousness. Cogn Neurodyn. 2018;12(1):73-84.
Cajochen DC, Dijk D-J. Electroencephalographic activity during wakefulness, rapid eye movement and non-rapid eye movement sleep in humans: comparison of their circadian and homeostatic modulation. Sleep Biol Rhythms. 2003;1:85-95.
Basner M, et al. Sleep deprivation and neurobehavioral dynamics. Curr Opin Neurobiol. 2013;23(5):854-863.
Killgore WD. Effects of sleep deprivation on cognition. Prog Brain Res. 2010;185:105-129.
Durmer JS, et al. Neurocognitive consequences of sleep deprivation. Semin Neurol. 2005;25(1):117-129.
Maric A, et al. Intraindividual increase of homeostatic sleep pressure across acute and chronic sleep loss: a high-density EEG study. Sleep. 2017;40. doi: 10.1093/sleep/zsx122
Banks S, et al. Behavioral and physiological consequences of sleep restriction. J Clin Sleep Med. 2007;3(5):519-528.