A review of automated sleep stage scoring based on physiological signals for the new millennia
Tài liệu tham khảo
Ohayon, 2002, Epidemiology of insomnia: what we know and what we still need to learn, Sleep Med. Rev., 6, 97, 10.1053/smrv.2002.0186
Kajeepeta, 2015, Adverse childhood experiences are associated with adult sleep disorders: a systematic review, Sleep Med., 16, 320, 10.1016/j.sleep.2014.12.013
Willemen, 2014, An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification, IEEE J. Biomed. Health Inf., 18, 661, 10.1109/JBHI.2013.2276083
Ohayon, 2002, Prevalence and consequences of insomnia disorders in the general population of italy, Sleep Med., 3, 115, 10.1016/S1389-9457(01)00158-7
Baldwin, 2001, The association of sleep-disordered breathing and sleep symptoms with quality of life in the sleep heart health study, Sleep, 24, 96, 10.1093/sleep/24.1.96
Helland, 2010, Investigation of an automatic sleep stage classification by means of multiscorer hypnogram, Method. Inf. Med., 49, 467, 10.3414/ME09-02-0052
Bolge, 2009, Association of insomnia with quality of life, work productivity, and activity impairment, Qual. Life Res., 18, 415, 10.1007/s11136-009-9462-6
Daley, 2009, Insomnia and its relationship to health-care utilization, work absenteeism, productivity and accidents, Sleep Med., 10, 427, 10.1016/j.sleep.2008.04.005
Wickwire, 2016, Health economics of insomnia treatments: the return on investment for a good night’s sleep, Sleep Med. Rev., 30, 72, 10.1016/j.smrv.2015.11.004
Ozminkowski, 2007, The direct and indirect costs of untreated insomnia in adults in the united states, Sleep, 30, 263, 10.1093/sleep/30.3.263
Williamson, 2011, The link between fatigue and safety, Accid. Anal. Prev., 43, 498, 10.1016/j.aap.2009.11.011
Léger, 2002, Medical and socio-professional impact of insomnia, Sleep, 25, 621, 10.1093/sleep/25.6.621
Panossian, 2009, Review of sleep disorders, Med. Clin. North Am., 93, 407, 10.1016/j.mcna.2008.09.001
Faust, 2016, A review of ECG-based diagnosis support systems for obstructive sleep apnea, J. Mech. Med. Biol., 16, 1640004, 10.1142/S0219519416400042
Wong, 2015, Review of sleep studies of patients with chronic insomnia at a sleep disorder unit, Singapore Med. J., 56, 317, 10.11622/smedj.2015089
Lugaresi, 1983, Good and poor sleepers: an epidemiological survey of the san marino population, Sleep/Wake Disord., 1
Leger, 2008, An international survey of sleeping problems in the general population, Curr. Med. Res. Opin., 24, 307, 10.1185/030079907X253771
Hobson, 1969, A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects: a. rechtschaffen and a. kales (editors).(public health service, us government printing office, washington, dc, 1968, 58 p., $4.00), Clin. Neurophysiol., 26, 644, 10.1016/0013-4694(69)90021-2
Berry, 2012, The AASM manual for the scoring of sleep and associated events, Rules, Terminol. Tech. Specificat. Darien, Illinois, Am. Acad. Sleep Med.
Spriggs, 2014
Steriade, 2013
Rechtschaffen, 1968, A manual for standardized terminology, techniques and scoring system for sleep stages in human subjects, Brain Inf. Serv.
Kales, 1968
Silber, 2007, The visual scoring of sleep in adults, J. Clin. Sleep Med., 3, 10.5664/jcsm.26814
Penzel, 2003, Reliablität der visuellen schlafauswertung nach rechtschaffen und kales von acht aufzeichnungen durch neun schlaflabore: reliability of visual evaluation of sleep stages according to rechtschaffen and kales from eight polysomnographs by nine sleep centres, Somnologie, 7, 49, 10.1046/j.1439-054X.2003.03199.x
Danker-Hopfe, 2004, Interrater reliability between scorers from eight european sleep laboratories in subjects with different sleep disorders, J. Sleep Res., 13, 63, 10.1046/j.1365-2869.2003.00375.x
Ohayon, 2004, Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan, Sleep, 27, 1255, 10.1093/sleep/27.7.1255
Faust, 2013, Automated detection of pulmonary edema and respiratory failure using physiological signals, J. Med. Imag. Health Inf., 3, 424
Sharma, 2018, An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank, Comput. Biol. Med., 98, 58, 10.1016/j.compbiomed.2018.04.025
Doroshenkov, 2007, Classification of human sleep stages based on eeg processing using hidden markov models, Biomed. Eng., 41, 25, 10.1007/s10527-007-0006-5
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
Shi, 2015, Multi-channel eeg-based sleep stage classification with joint collaborative representation and multiple kernel learning, J. Neurosci. Method., 254, 94, 10.1016/j.jneumeth.2015.07.006
Diykh, 2016, Eeg sleep stages classification based on time domain features and structural graph similarity, IEEE Trans. Neural Syst. Rehabilit. Eng., 24, 1159, 10.1109/TNSRE.2016.2552539
Seifpour, 2018, A new automatic sleep staging system based on statistical behavior of local extrema using single channel eeg signal, Expert Syst. Appl., 104, 277, 10.1016/j.eswa.2018.03.020
Pillay, 2018, Automated eeg sleep staging in the term-age baby using a generative modelling approach, J. Neural Eng., 15, 036004, 10.1088/1741-2552/aaab73
Ebrahimi, 2015, Automatic sleep staging by simultaneous analysis of ecg and respiratory signals in long epochs, Biomed. Signal Process. Control, 18, 69, 10.1016/j.bspc.2014.12.003
Acharya, 2010, Analysis and automatic identification of sleep stages using higher order spectra, Int. J. Neural Syst., 20, 509, 10.1142/S0129065710002589
Dimitriadis, 2018, A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates, Clin. Neurophysiol., 129, 815, 10.1016/j.clinph.2017.12.039
da Silveira, 2017, Single-channel eeg sleep stage classification based on a streamlined set of statistical features in wavelet domain, Med. Biol. Eng. Comput., 55, 343, 10.1007/s11517-016-1519-4
Hassan, 2016, A decision support system for automatic sleep staging from EEG signals using tunable q-factor wavelet transform and spectral features, J. Neurosci. Method., 271, 107, 10.1016/j.jneumeth.2016.07.012
Čić, 2013, Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal, Comput. Biol. Med., 43, 2110, 10.1016/j.compbiomed.2013.10.002
Acharya, 2015, Nonlinear dynamics measures for automated EEG-based sleep stage detection, Eur. Neurol., 74, 268, 10.1159/000441975
Peker, 2016, An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms, Neurocomputing, 207, 165, 10.1016/j.neucom.2016.04.049
Memar, 2018, A novel multi-class eeg-based sleep stage classification system, IEEE Trans. Neural Syst. Rehabilit. Eng., 26, 84, 10.1109/TNSRE.2017.2776149
Hassan, 2017, A decision support system for automated identification of sleep stages from single-channel eeg signals, Know.-Based Syst., 128, 115, 10.1016/j.knosys.2017.05.005
Ronzhina, 2012, Sleep scoring using artificial neural networks, Sleep Med. Rev., 16, 251, 10.1016/j.smrv.2011.06.003
Hassan, 2017, Automated identification of sleep states from eeg signals by means of ensemble empirical mode decomposition and random under sampling boosting, Comput. Method. Progr. Biomed., 140, 201, 10.1016/j.cmpb.2016.12.015
Bajaj, 2013, Automatic classification of sleep stages based on the time-frequency image of EEG signals, Comput. Method. Program. Biomed., 112, 320, 10.1016/j.cmpb.2013.07.006
Hsu, 2013, Automatic sleep stage recurrent neural classifier using energy features of EEG signals, Neurocomputing, 104, 105, 10.1016/j.neucom.2012.11.003
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
Supratak, 2017, Deepsleepnet: a model for automatic sleep stage scoring based on raw single-channel EEG, IEEE Trans. Neural Syst. Rehabilit. Eng., 25, 1998, 10.1109/TNSRE.2017.2721116
Michielli, 2019, Cascaded lstm recurrent neural network for automated sleep stage classification using single-channel eeg signals, Comput. Biol. Med., 10.1016/j.compbiomed.2019.01.013
Mousavi, 2019, Sleepeegnet: automated sleep stage scoring with sequence to sequence deep learning approach,
Chriskos, 2018, Achieving accurate automatic sleep staging on manually pre-processed EEG data through synchronization feature extraction and graph metrics, Front. Human Neurosci., 12, 110, 10.3389/fnhum.2018.00110
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
Şen, 2014, A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms, J. Med. Syst., 38, 18, 10.1007/s10916-014-0018-0
Acharya, 2005, Non-linear analysis of EEG signals at various sleep stages, Comput. Method. Progr. Biomed., 80, 37, 10.1016/j.cmpb.2005.06.011
Acharya, 2011, Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters, Physiol. Measur., 32, 287, 10.1088/0967-3334/32/3/002
Yücelbaş, 2018, Automatic sleep staging based on svd, vmd, hht and morphological features of single-lead ecg signal, Expert Syst. Appl., 102, 193, 10.1016/j.eswa.2018.02.034
Kesper, 2012, Ecg signal analysis for the assessment of sleep-disordered breathing and sleep pattern, Med. Biol. Eng. Comput., 50, 135, 10.1007/s11517-011-0853-9
Xiao, 2013, Sleep stages classification based on heart rate variability and random forest, Biomed. Signal Process. Control, 8, 624, 10.1016/j.bspc.2013.06.001
Redmond, 2006, Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea, IEEE Trans. Biomed. Eng., 53, 485, 10.1109/TBME.2005.869773
Redmond, 2007, Sleep staging using cardiorespiratory signals, Somnologie-Schlafforschung und Schlafmedizin, 11, 245, 10.1007/s11818-007-0314-8
Fell, 2000, Nonlinear Analysis of continuous ECG during sleep i. reconstruction, Biol. Cybernet., 82, 477, 10.1007/s004220050600
Fell, 2000, Nonlinear analysis of continuous ECG during sleep ii. dynamical measures, Biol. Cybernet., 82, 485, 10.1007/s004220050601
Bartsch, 2012, Phase transitions in physiologic coupling, Proc. Natl. Acad. Sci., 109, 10181, 10.1073/pnas.1204568109
Kryger, 2016, Principles and Practice of Sleep Medicine
Chattipakorn, 2007, Heart rate variability in myocardial infarction and heart failure, Int. J. Cardiol., 120, 289, 10.1016/j.ijcard.2006.11.221
Faust, 2012, Comprehensive analysis of normal and diabetic heart rate signals: a review, J. Mech. Med. Biol., 12, 1240033, 10.1142/S0219519412400337
Acharya, 2013, An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes, Comput. Method. Biomech. Biomed. Eng., 16, 222, 10.1080/10255842.2011.616945
Hock, 2013, Automated detection of premature ventricular contraction using recurrence quantification analysis on heart rate signals, J. Med. Imag. Health Inf., 3, 462
Acharya, 2014, Linear and nonlinear analysis of normal and cad-affected heart rate signals, Comput. Method. Progr. Biomed., 113, 55, 10.1016/j.cmpb.2013.08.017
Acharya, 2013, Automated identification of normal and diabetes heart rate signals using nonlinear measures, Comput. Biol. Med., 43, 1523, 10.1016/j.compbiomed.2013.05.024
Malik, 1996, Heart rate variability: standards of measurement, physiological interpretation, and clinical use, Eur. Heart J., 17, 354, 10.1093/oxfordjournals.eurheartj.a014868
Camm, 1996, Heart rate variability: standards of measurement, physiological interpretation and clinical use. task force of the european society of cardiology and the north american society of pacing and electrophysiology, Circulation, 93, 1043, 10.1161/01.CIR.93.5.1043
Faust, 2004, Analysis of cardiac signals using spatial filling index and time-frequency domain, BioMed. Eng. OnLine, 3, 30, 10.1186/1475-925X-3-30
Kryger, 2010
Yoon, 2017, Rem sleep estimation based on autonomic dynamics using r–r intervals, Physiol. Measur., 38, 631, 10.1088/1361-6579/aa63c9
Penzel, 2003, Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea, IEEE Trans. Biomed. Eng., 50, 1143, 10.1109/TBME.2003.817636
Stein, 2012, Heart rate variability, sleep and sleep disorders, Sleep Med. Rev., 16, 47, 10.1016/j.smrv.2011.02.005
Trinder, 2001, Autonomic activity during human sleep as a function of time and sleep stage, J. Sleep Res., 10, 253, 10.1046/j.1365-2869.2001.00263.x
de Zambotti, 2015, Cardiac autonomic function during sleep: effects of alcohol dependence and evidence of partial recovery with abstinence, Alcohol, 49, 409, 10.1016/j.alcohol.2014.07.023
Liu, 2017, Comparison between heart rate variability and pulse rate variability during different sleep stages for sleep apnea patients, Technol. Health Care, 25, 435, 10.3233/THC-161283
Virtanen, 2007, Sleep stage dependent patterns of nonlinear heart rate dynamics in postmenopausal women, Autonomic Neurosci., 134, 74, 10.1016/j.autneu.2007.01.010
Crasset, 2001, Effects of aging and cardiac denervation on heart rate variability during sleep, Circulation, 103, 84, 10.1161/01.CIR.103.1.84
Faust, 2013, Heart rate variability analysis for different age and gender, J. Med. Imag. Health Inf., 3, 395
Mendez, 2010, Sleep staging from heart rate variability: time-varying spectral features and hidden markov models, Int. J. Biomed. Eng. Technol., 3, 246, 10.1504/IJBET.2010.032695
Liang, 2015, Development of an EOG-based automatic sleep-monitoring eye mask, IEEE Trans. Instrument. Measur., 64, 2977, 10.1109/TIM.2015.2433652
Virkkala, 2007, Automatic sleep stage classification using two-channel electro-oculography, J. Neurosci. Method., 166, 109, 10.1016/j.jneumeth.2007.06.016
Rahman, 2018, Sleep stage classification using single-channel EOG, Comput. Biol. Med., 10.1016/j.compbiomed.2018.08.022
Penzel, 2007, Cardiovascular and respiratory dynamics during normal and pathological sleep, Chaos, 17, 015116, 10.1063/1.2711282
Douglas, 1982, Respiration during sleep in normal man., Thorax, 37, 840, 10.1136/thx.37.11.840
Somers, 1993, Sympathetic-nerve activity during sleep in normal subjects, New Engl. J. Med., 328, 303, 10.1056/NEJM199302043280502
Long, 2014, Analyzing respiratory effort amplitude for automated sleep stage classification, Biomed. Signal Process. Control, 14, 197, 10.1016/j.bspc.2014.08.001
Sheldon, 2014
of Sleep Medicine Task Force, 1999, Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research, Sleep, 22, 667, 10.1093/sleep/22.5.667
Liang, 2012, A rule-based automatic sleep staging method, J. Neurosci. Method., 205, 169, 10.1016/j.jneumeth.2011.12.022
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
Kishi, 2011, Nrem sleep stage transitions control ultradian REM sleep rhythm, Sleep, 34, 1423, 10.5665/SLEEP.1292
Leung, 2009, Sleep-disordered breathing: autonomic mechanisms and arrhythmias, Progress Cardiovasc. Diseases, 51, 324, 10.1016/j.pcad.2008.06.002
Tracik, 2001, Sudden daytime sleep onset in parkinson’s disease: polysomnographic recordings, Movement Disorders, 16, 500, 10.1002/mds.1083
Kushida, 2001, Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients, Sleep Med., 2, 389, 10.1016/S1389-9457(00)00098-8
Montgomery-Downs, 2006, Polysomnographic characteristics in normal preschool and early school-aged children, Pediatrics, 117, 741, 10.1542/peds.2005-1067
Long, 2014, Sleep and wake classification with actigraphy and respiratory effort using dynamic warping, IEEE J. Biomed. Health Inf., 18, 1272, 10.1109/JBHI.2013.2284610
Kirjavainen, 1996, Respiratory and body movements as indicators of sleep stage and wakefulness in infants and young children, J. Sleep Res., 5, 186, 10.1046/j.1365-2869.1996.t01-1-00003.x
Tripathy, 2018, Use of features from rr-time series and eeg signals for automated classification of sleep stages in deep neural network framework, Biocybernet. Biomed. Eng., 38, 890, 10.1016/j.bbe.2018.05.005
Yildirim, 2019, A deep learning model for automated sleep stages classification using PSG signals, Int. J. Environ. Res. Public Health, 16, 599, 10.3390/ijerph16040599
Takatani, 2018, Relationship between frequency spectrum of heart rate variability and autonomic nervous activities during sleep in newborns, Brain Dev., 40, 165, 10.1016/j.braindev.2017.09.003
Fonseca, 2015, Sleep stage classification with ECG and respiratory effort, Physiol. Measur., 36, 2027, 10.1088/0967-3334/36/10/2027
Kesek, 2009, Heart rate variability during sleep and sleep apnoea in a population based study of 387 women, Clin. Physiol. Funct. Imag., 29, 309, 10.1111/j.1475-097X.2009.00873.x
Estévez, 2002, Polysomnographic pattern recognition for automated classification of sleep-waking states in infants, Med. Biol. Eng. Comput., 40, 105, 10.1007/BF02347703
Shannon, 2001, A mathematical theory of communication, ACM SIGMOBILE Mobile Comput. Commun. Rev., 5, 3, 10.1145/584091.584093
Piva, 2014, Laboratory critical values: automated notification supports effective clinical decision making, Clin. Biochem., 47, 1163, 10.1016/j.clinbiochem.2014.05.056
Patterson, 2005, Six sigma applied throughout the lifecycle of an automated decision system, Qual. Reliabil. Eng. Int., 21, 275, 10.1002/qre.629
Faust, 2015, The role of real-time in biomedical science: a meta-analysis on computational complexity, delay and speedup, Comput. Biol. Med., 58, 73, 10.1016/j.compbiomed.2014.12.024
Islam, 2015, The internet of things for health care: a comprehensive survey, IEEE Access, 3, 678, 10.1109/ACCESS.2015.2437951
Faust, 2018, Automated detection of atrial fibrillation using long short-term memory network with rr interval signals, Comput. Biol. Med., 10.1016/j.compbiomed.2018.07.001
Faust, 2012, Formal design methods for reliable computer-aided diagnosis: a review, IEEE Rev. Biomed. Eng., 5, 15, 10.1109/RBME.2012.2184750
Faust, 2018, Deep learning for healthcare applications based on physiological signals: a review, Comput. Method. Progr. Biomed., 161, 1, 10.1016/j.cmpb.2018.04.005
Pan, 1985, A real-time qrs detection algorithm, IEEE Trans. Biomed. Eng, 32, 230, 10.1109/TBME.1985.325532
Macfarlane, 1995, Resting 12-lead ecg electrode placement and associated problems, Soc Cardiol. Tech. Update, 2, 10
García-Niebla, 2009, Technical mistakes during the acquisition of the electrocardiogram, Annal. Noninvas. Electrocardiol., 14, 389, 10.1111/j.1542-474X.2009.00328.x
Kohler, 2002, The principles of software qrs detection, IEEE Eng. Med. Biol. Magaz., 21, 42, 10.1109/51.993193
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
Moody, 2001, Physionet: a web-based resource for the study of physiologic signals, IEEE Eng. Med. Biol. Magaz., 20, 70, 10.1109/51.932728