Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques
Tóm tắt
Từ khóa
Tài liệu tham khảo
Rauchs, 2005, The relationships between memory systems and sleep stages, J. Sleep Res., 14, 123, 10.1111/j.1365-2869.2005.00450.x
Landmann, 2014, The reorganisation of memory during sleep, Sleep Med. Rev., 18, 531, 10.1016/j.smrv.2014.03.005
Hublin, 2007, Sleep and mortality: A population-based 22-year follow-up study, Sleep, 30, 1245, 10.1093/sleep/30.10.1245
Lovin, 1988, The role of polysomnography in the differential diagnosis of chronic insomnia, Am. J. Psychiatry., 145, 346, 10.1176/ajp.145.3.346
Steriade, 1993, Thalamocortical oscillations in the sleeping and aroused brain, Science, 262, 679, 10.1126/science.8235588
Niedermeyer, E., and da Silva, F. (2005). Electroencephalography, Lippincott Williams and Wilkins.
Iber, C. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, American Academy of Sleep Medicine.
Hopfe, 2009, Interrater reliability for sleep scoring according to the Rechtschaffen Kales and the new AASM standard, J. Sleep Res., 18, 74, 10.1111/j.1365-2869.2008.00700.x
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
Fraiwan, 2009, Automatic sleep stage scoring with Wavelet Packets based on single EEG recording, Proc. World Acad. Sci. Eng. Technol. Paris, 54, 385
Vuckovic, 2002, Automatic recognition of alertness and drowsiness from EEG by an artificial neural network, Med. Eng. Phys., 24, 349, 10.1016/S1350-4533(02)00030-9
Robert, 1998, Review of neural network applications in sleep research, J. Neurosci. Methods., 79, 187, 10.1016/S0165-0270(97)00178-7
Ronzhina, 2012, Sleep scoring using artificial neural networks, Sleep Med. Rev., 16, 251, 10.1016/j.smrv.2011.06.003
Subasi, 2005, Classification of EEG signals using neural network and logistic regression, Comput. Methods Programs Biomed., 78, 87, 10.1016/j.cmpb.2004.10.009
2012, Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering, Comput. Methods Programs Biomed., 108, 250, 10.1016/j.cmpb.2012.04.007
Kemp, 2000, Analysis of a sleep-dependent neural feedback loop: The slow-wave microcontinuity of the EEG, IEEE–BME, 9, 1185, 10.1109/10.867928
Goldberger, 2000, Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals, Circulation, 101, e215, 10.1161/01.CIR.101.23.e215
Kemp, 1990, Alternative electrode placement in (automatic) sleep scoring (Fpz-Cz / Pz-Oz versus C4-A1 / C3-A2), Sleep, 3, 279
Mourtazaev, 1995, Age and gender affect different characteristics of slow waves in the sleep EEG, Sleep, 7, 557, 10.1093/sleep/18.7.557
Rechtschaffen, A., and Kales, A. (1968). A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, US Department of Health, Education, and Welfare.
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
Raghavendra, 2010, Computing fractal dimension of signals using multiresolution box-counting method, Int. J. Inf. Math. Sci., 6, 50
Shoupeng, 2007, A fractal-dimension-based signal processing technique and its use for nondestructive testing, Russ. J. Nondestruct. Test., 43, 270, 10.1134/S1061830907040080
Peng, C.K., Buldyrev, S.V., Havlin, S., Simons, M.S., Eugene, H., and Goldberger, A.L. (1994). Mosaic organization of DNA nucleotides. Phys. Rev. E, 1685–1689.
Peng, 1995, Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series, Chaos, 5, 82, 10.1063/1.166141
Fell, 1996, Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures, Electroencephalogr. Clin. Neurophysiol., 98, 401, 10.1016/0013-4694(96)95636-9
Pincus, 1995, Approximate entropy (ApEn) as a complexity measure, Chaos Interdiscip. J. Nonlinear Sci., 5, 110, 10.1063/1.166092
Richman, 2000, Physiological time–series analysis using Approximate Entropy and Sample Entropy, Am. J. Physiol.–Heart Circulatory Physiol., 278, H2039, 10.1152/ajpheart.2000.278.6.H2039
Costa, 2005, Multiscale entropy analysis of biological signals, Phys. Rev. E, 71, 021906, 10.1103/PhysRevE.71.021906
Toennies, K.D., Celler, A., Blinder, S., Moeller, T., and Harrop, R. R. (15, January Part). Scatter segmentation in dynamic SPECT images using principal component analysis. San Diego, CA, USA.
Wolf, L., and Shashua, A. (2003, January 13–16). Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weighted-based approach. Nice, France.
Hansen, 2001, J-Means: a new local search heuristic for minimum sum of squares clustering, Pattern Recognit., 34, 405, 10.1016/S0031-3203(99)00216-2
Gunes, 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
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
Hese, P.V., Philips, W., Koninck, J.D., de Walle, R.V., and Lemahieu, I. (2001, January 25–28). Automatic detection of sleep stages using the EEG.
Oropesa, E., Cycon, H.L., and Jobert, M. Sleep stage classification using Wavelet Transform and neural network. Available online: http://www.researchgate.net/publication/216570220_Sleep_Stage_Classification_Using_Wavelet_Transform__Neural_Network.
Krakovska, 2011, Automatic sleep scoring: A search for an optimal combination of measures, Artif. Intell. Med., 53, 25, 10.1016/j.artmed.2011.06.004
Shambroom, 2012, Validation of an automated wireless system to monitor sleep in healthy adults, J. Sleep Res., 21, 221, 10.1111/j.1365-2869.2011.00944.x
Swarnkar, 2014, Bispectral analysis of single channel EEG to estimate macro-sleep-architecture, Int. J. Med. Eng. Inform., 6, 43
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
Viera, 2005, Understanding interobserver agreement: The Kappa statistic, Fam. Med., 5, 360
Weiss, 2011, Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages, Brain Res. Bull., 84, 359, 10.1016/j.brainresbull.2010.12.005
Susmakova, 2008, Discrimination ability of individual measures used in sleep stages classification, Artif. Intell. Med., 44, 261, 10.1016/j.artmed.2008.07.005
Eckehard, 2011, The sleeping brain as a complex system, Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci., 369, 3697, 10.1098/rsta.2011.0199
Buckelmuller, 2006, Trait-like individual differences in the human sleep electroencephalogram, Neuroscience, 138, 351, 10.1016/j.neuroscience.2005.11.005
Dongen, 2005, Individual differences in adult human sleep and wakefulness. Leitmotif for a research agenda, Sleep, 28, 479, 10.1093/sleep/28.4.479
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
Kemp, 1993, A digital telemetry system for ambulatory sleep recording, Sleep-Wake Research in The Netherlands, 4, 129
Dijk, 1989, Effects of seganserin, a 5-HT2 antagonist, and temazepam on human sleep stages and EEG power spectra, Eur. J. Pharmacol., 171, 207, 10.1016/0014-2999(89)90109-X
Rodriguez-Sotelo, J., Osorio-Forero, A., Jimenez-Rodriguez, A., and Restrepo, F. (2014, January 15–17). A new tool for assisted sleep staging and transitory sleep patterns analysis in EEG signals.
Zhang, 2014, Construction of rules for seizure prediction based on approximate entropy, Clin. Neurophysiol., 125, 1959, 10.1016/j.clinph.2014.02.017
