Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework
Tài liệu tham khảo
Winfree, 1982, Human body clocks and the timing of sleep, Nature, 297, 23, 10.1038/297023a0
Taheri, 2004, Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index, PLoS Med, 1, e62, 10.1371/journal.pmed.0010062
Tripathy, 2017
Van Cauter, 1997, Roles of circadian rhythmicity and sleep in human glucose regulation, Endocr Rev, 18, 716
Berry, 2012, The AASM manual for the scoring of sleep and associated events
Carskadon, 2005, Normal human sleep: an overview, Princ Pract Sleep Med, 4, 13, 10.1016/B0-72-160797-7/50009-4
Agarwal, 2001, Computer-assisted sleep staging, IEEE Trans Biomed Eng, 48, 1412, 10.1109/10.966600
Acharya, 2010, Analysis and automatic identification of sleep stages using higher order spectra, Int J Neural Syst, 20, 509, 10.1142/S0129065710002589
Boostani, 2017, A comparative review on sleep stage classification methods in patients and healthy individuals, Comput Methods Programs Biomed, 140, 77, 10.1016/j.cmpb.2016.12.004
Aboalayon, 2016, Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation, Entropy, 18, 272, 10.3390/e18090272
Hassan, 2017, Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting, Comput Methods Programs Biomed, 140, 201, 10.1016/j.cmpb.2016.12.015
Prucnal, 2017, Effect of feature extraction on automatic sleep stage classification by artificial neural network, Metrol Meas Syst, 24, 229, 10.1515/mms-2017-0036
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
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, 10.1016/j.compbiomed.2018.04.025
Ebrahimi, 2013, Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals, Comput Methods Programs Biomed, 112, 47, 10.1016/j.cmpb.2013.06.007
Adnane, 2012, Sleep-wake stages classification and sleep efficiency estimation using single-lead electrocardiogram, Expert Syst Appl, 39, 1401, 10.1016/j.eswa.2011.08.022
Scherz, 2017, Heart rate spectrum analysis for sleep quality detection, EURASIP J Embed Syst, 2017, 26, 10.1186/s13639-017-0072-z
Kim, 2017, Sleep stage classification based on noise-reduced fractal property of heart rate variability, Proc Comput Sci, 116, 435, 10.1016/j.procs.2017.10.026
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
Hsu, 2013, Automatic sleep stage recurrent neural classifier using energy features of EEG signals, Neurocomputing, 104, 105, 10.1016/j.neucom.2012.11.003
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
Jo, 2010, Genetic fuzzy classifier for sleep stage identification, Comput Biol Med, 40, 629, 10.1016/j.compbiomed.2010.04.007
Otzenberger, 1997, Temporal relationship between dynamic heart rate variability and electroencephalographic activity during sleep in man, Neurosci Lett, 229, 173, 10.1016/S0304-3940(97)00448-5
Ako, 2003, Correlation between electroencephalography and heart rate variability during sleep, Psychiatry Clin Neurosci, 57, 59, 10.1046/j.1440-1819.2003.01080.x
Durmer, 2005, Neurocognitive consequences of sleep deprivation, 117
Nagai, 2010, Sleep duration as a risk factor for cardiovascular disease – a review of the recent literature, Curr Cardiol Rev, 6, 54, 10.2174/157340310790231635
Wolk, 2003, Sleep-disordered breathing and cardiovascular disease, Circulation, 108, 9, 10.1161/01.CIR.0000072346.56728.E4
Miglis, 2017
Rolink, 2015, Recurrence quantification analysis across sleep stages, Biomed Signal Process Control, 20, 107, 10.1016/j.bspc.2015.04.006
Lin, 2009, Iterative filtering as an alternative algorithm for empirical mode decomposition, Adv Adapt Data Anal, 1, 543, 10.1142/S179353690900028X
Sharma, 2017, Automatic sleep stages classification based on iterative filtering of electroencephalogram signals, Neural Comput Appl, 28, 2959, 10.1007/s00521-017-2919-6
Lv, 2017, Remote sensing image classification based on ensemble extreme learning machine with stacked autoencoder, IEEE Access, 5, 9021, 10.1109/ACCESS.2017.2706363
Planinšič, 2018
Zhang, 2017
Zhang, 2017, A new method for automatic sleep stage classification, IEEE Trans Biomed Circuits Syst, 11, 1097, 10.1109/TBCAS.2017.2719631
Faust, 2018, Deep learning for healthcare applications based on physiological signals: a review, Comput Methods Program Biomed, 161, 1, 10.1016/j.cmpb.2018.04.005
Ichimaru, 1999, Development of the polysomnographic database on CD-ROM, Psychiatry Clin Neurosci, 53, 175, 10.1046/j.1440-1819.1999.00527.x
Pan, 1985, A real-time QRS detection algorithm, IEEE Trans Biomed Eng, 3, 230, 10.1109/TBME.1985.325532
Cicone, 2016, Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis, Appl Comput Harm Anal, 41, 384, 10.1016/j.acha.2016.03.001
Penzel, 2003, Dynamics of heart rate and sleep stages in normals and patients with sleep apnea, Neuropsychopharmacology, 28, S48, 10.1038/sj.npp.1300146
Acharya, 2015, Nonlinear dynamics measures for automated EEG-based sleep stage detection, Eur Neurol, 74, 268, 10.1159/000441975
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
Marwan, 2007, Recurrence plots for the analysis of complex systems, Phys Rep, 438, 237, 10.1016/j.physrep.2006.11.001
Marwan, 2008, A historical review of recurrence plots, Eur Phys J Spec Top, 164, 3, 10.1140/epjst/e2008-00829-1
Eckmann, 1987, Recurrence plots of dynamical systems, Europhys Lett, 4, 973, 10.1209/0295-5075/4/9/004
Yang, 2011, Multiscale recurrence quantification analysis of spatial cardiac vectorcardiogram signals, IEEE Trans Biomed Eng, 58, 339, 10.1109/TBME.2010.2063704
Rostaghi, 2016, Dispersion entropy: a measure for time-series analysis, IEEE Signal Process Lett, 23, 610, 10.1109/LSP.2016.2542881
Subha, 2010, EEG signal analysis: a survey, J Med Syst, 34, 195, 10.1007/s10916-008-9231-z
Aboalayon, 2014, Efficient sleep stage classification based on EEG signals, 1
Shin, 2013, Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4d patient data, IEEE Trans Pattern Anal Mach Intell, 35, 1930, 10.1109/TPAMI.2012.277
Sharma, 2015, Multiscale energy and eigenspace approach to detection and localization of myocardial infarction, IEEE Trans Biomed Eng, 62, 1827, 10.1109/TBME.2015.2405134
Vanoli, 1995, Heart rate variability during specific sleep stages: a comparison of healthy subjects with patients after myocardial infarction, Circulation, 91, 1918, 10.1161/01.CIR.91.7.1918
ŽEmaitytė, 1984, Heart rhythm control during sleep, Psychophysiology, 21, 279, 10.1111/j.1469-8986.1984.tb02935.x
Hauri, 2013
Hayet, 2012, Sleep-wake stages classification based on heart rate variability, 996
Werteni, 2014, An automatic sleep-wake classifier using ECG signals, Int J Comput Sci Issues (IJCSI), 11, 84
Rossow, 2011, Automatic sleep staging using a single-channel EEG modeling by Kalman filter and HMM, 1
Redmond, 2003, Electrocardiogram-based automatic sleep staging in sleep disordered breathing, 609
Song, 2004, Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans, Neurosci Lett, 366, 148, 10.1016/j.neulet.2004.05.025
Ng, 2012, Impact of obstructive sleep apnea on sleep-wake stage ratio, 4660
Acharya, 2006, Heart rate variability: a review, Med Biol Eng Comput, 44, 1031, 10.1007/s11517-006-0119-0
Kannathal, 2005, Entropies for detection of epilepsy in EEG, Comput Methods Programs Biomed, 80, 187, 10.1016/j.cmpb.2005.06.012
Chua, 2010, Application of higher order statistics/spectra in biomedical signals – a review, Med Eng Phys, 32, 679, 10.1016/j.medengphy.2010.04.009
Jabloun, 1999, Teager energy based feature parameters for speech recognition in car noise, IEEE Signal Process Lett, 6, 259, 10.1109/97.789604
Tan, 2018, Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals, Comput Biol Med, 94, 19, 10.1016/j.compbiomed.2017.12.023
Acharya, 2017, Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Inf Sci, 415, 190, 10.1016/j.ins.2017.06.027
Acharya, 2017, A deep convolutional neural network model to classify heartbeats, Comput Biol Med, 89, 389, 10.1016/j.compbiomed.2017.08.022
Acharya, 2017, Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal, Knowl Based Syst, 132, 156, 10.1016/j.knosys.2017.06.026
Acharya, 2017, Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network, Knowl Based Syst, 132, 62, 10.1016/j.knosys.2017.06.003
Acharya, 2017, Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Comput Biol Med