Sleep EEG analysis utilizing inter-channel covariance matrices

Biocybernetics and Biomedical Engineering - Tập 40 - Trang 527-545 - 2020
Gopika Gopan K.1, Sathvik S. Prabhu1, Neelam Sinha1
1International Institute of Information Technology (IIIT-B), Bangalore, Karnataka, India

Tài liệu tham khảo

Brown, 2012, Control of sleep and wakefulness, Physiol Rev, 92, 1087, 10.1152/physrev.00032.2011

McCarley, 2007, Neurobiology of REM and NREM sleep, Sleep Med, 8, 302, 10.1016/j.sleep.2007.03.005

Berry, 2012, The AASM manual for the scoring of sleep and associated events, Rules, Terminology and Technical Specifications, Darien, Illinois, Am Acad Sleep Med, 176

Carskadon, 2005, Normal human sleep: an overview, Principles and practice of sleep medicine, 4, 13, 10.1016/B0-72-160797-7/50009-4

Jiang, 2019, Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement, Expert Syst Appl, 121, 188, 10.1016/j.eswa.2018.12.023

Hassan, 2016, A decision support system for automatic sleep staging from EEG signals using tunable q-factor wavelet transform and spectral features, J Neurosci Methods, 271, 107, 10.1016/j.jneumeth.2016.07.012

Hassan, 2017, A decision support system for automated identification of sleep stages from single-channel EEG signals, Knowl-Based Syst, 128, 115, 10.1016/j.knosys.2017.05.005

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

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

Zhu, 2014, Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal, IEEE J Biomed Health Informatics, 18, 1813, 10.1109/JBHI.2014.2303991

Ghimatgar, 2019, An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov model, J Neurosci Methods, 108320, 10.1016/j.jneumeth.2019.108320

Frilot, 2018, An original method for staging sleep based on dynamical analysis of a single EEG signal, J Neurosci Methods, 308, 135, 10.1016/j.jneumeth.2018.07.017

Alickovic, 2018, Ensemble SVM method for automatic sleep stage classification, IEEE Trans Instrum Meas, 67, 1258, 10.1109/TIM.2018.2799059

Chambon, 2018, A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series, IEEE Trans Neural Syst Rehabil Eng, 26, 758, 10.1109/TNSRE.2018.2813138

Zhang, 2017, A new method for automatic sleep stage classification, IEEE Trans Biomed Circuits Syst, 11, 1097, 10.1109/TBCAS.2017.2719631

Mousavi, 2019, Sleepeegnet: automated sleep stage scoring with sequence to sequence deep learning approach, PLOS ONE, 14, e0216456, 10.1371/journal.pone.0216456

Devuyst S. DREAMS Subject database. [Online. accessed 15 November 2018].

Hassan, 2016, Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating, Biomed Signal Process Control, 24, 1, 10.1016/j.bspc.2015.09.002

Hassan, 2016, Automatic sleep scoring using statistical features in the EMD domain and ensemble methods, Biocybern Biomed Eng, 36, 248, 10.1016/j.bbe.2015.11.001

Hassan, 2017, An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting, Neurocomputing, 219, 76, 10.1016/j.neucom.2016.09.011

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

Rodríguez-Sotelo, 2014, Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques, Entropy, 16, 6573, 10.3390/e16126573

Shi, 2017, A comparison study on stages of sleep: quantifying multiscale complexity using higher moments on coarse-graining, Commun Nonlinear Sci Numer Simul, 44, 292, 10.1016/j.cnsns.2016.08.019

Acharya, 2015, Nonlinear dynamics measures for automated EEG-based sleep stage detection, Eur Neurol, 74, 268, 10.1159/000441975

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

Sors, 2018, A convolutional neural network for sleep stage scoring from raw single-channel EEG, Biomed Signal Process Control, 42, 107, 10.1016/j.bspc.2017.12.001

Cen, 2010, Deep learning method for sleep stage classification, 796

Michielli, 2019, Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals, Comput Biol Med, 106, 71, 10.1016/j.compbiomed.2019.01.013

Supratak, 2017, Deepsleepnet: a model for automatic sleep stage scoring based on raw single-channel EEG, IEEE Trans Neural Syst Rehabil Eng, 25, 1998, 10.1109/TNSRE.2017.2721116

Tripathy, 2018, Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework, Biocybern Biomed Eng, 38, 890, 10.1016/j.bbe.2018.05.005

Malafeev, 2018, Automatic human sleep stage scoring using deep neural networks, Front Neurosci, 12, 781, 10.3389/fnins.2018.00781

Rahman, 2018, Sleep stage classification using single-channel EOG, Comput Biol Med, 102, 211, 10.1016/j.compbiomed.2018.08.022

Al-Salman, 2019, Detecting sleep spindles in EEGs using wavelet Fourier analysis and statistical features, Biomed Signal Process Control, 48, 80, 10.1016/j.bspc.2018.10.004

Vimala, 2019, An intelligent sleep apnea classification system based on EEG signals, J Med Syst, 43, 36, 10.1007/s10916-018-1146-8

Faust, 2019, A review of automated sleep stage scoring based on physiological signals for the new millennia, Comput Methods Programs Biomed, 10.1016/j.cmpb.2019.04.032

Stevner, 2019, Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep, Nat Commun, 10, 1035, 10.1038/s41467-019-08934-3

Braun, 1997, Regional cerebral blood flow throughout the sleep-wake cycle. An h2 (15) o pet study, Brain, 120, 1173, 10.1093/brain/120.7.1173

Nofzinger, 1997, Forebrain activation in REM sleep: an FDG pet study, Brain Res, 770, 192, 10.1016/S0006-8993(97)00807-X

Maquet, 1996, Functional neuroanatomy of human rapid-eye-movement sleep and dreaming, Nature, 383, 163, 10.1038/383163a0

Barachant, 2013, Classification of covariance matrices using a Riemannian-based kernel for BCI applications, Neurocomputing, 112, 172, 10.1016/j.neucom.2012.12.039

Congedo, 2017, Riemannian geometry for EEG-based brain–computer interfaces a primer and a review, Brain–Comput Interfaces, 4, 155, 10.1080/2326263X.2017.1297192

Meurant, 2012

Daubechies, 1992

Subasi, 2007, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Syst Appl, 32, 1084, 10.1016/j.eswa.2006.02.005

Ghare, 2016, Human emotion recognition using non linear and non stationary EEG signal, 1013

Mohammadi, 2017, Wavelet-based emotion recognition system using EEG signal, Neural Comput. Appl., 28, 1985, 10.1007/s00521-015-2149-8

Usak, 2017, EEG biomarker of sleep apnoea using discrete wavelet transform and approximate entropy, 330

Zhang, 2017, Classification of EEG signals based on autoregressive model and wavelet packet decomposition, Neural Process Lett, 45, 365, 10.1007/s11063-016-9530-1

Lütkepohl, 1996

Petersen, 2006

Abe, 2005

Cover, 1967, Nearest neighbor pattern classification, IEEE Trans Inform Theory, 13, 21, 10.1109/TIT.1967.1053964

Dietterich, 2000, An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization, Mach Learn, 40, 139, 10.1023/A:1007607513941

Maaten, 2008, Visualizing data using t-SNE, J Mach Learn Res, 9, 2579

Fraga González, 2018, EEG resting state functional connectivity in adult dyslexics using phase lag index and graph analysis, Front Human Neurosci, 12, 341, 10.3389/fnhum.2018.00341

Kiiski, 2020, Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms, Clin Neurophysiol, 131, 330, 10.1016/j.clinph.2019.08.010

Briels, 2020, In pursuit of a sensitive EEG functional connectivity outcome measure for clinical trials in Alzheimer's disease, Clin Neurophysiol, 131, 88, 10.1016/j.clinph.2019.09.014

Tüshaus, 2017, In human non-rem sleep, more slow-wave activity leads to less blood flow in the prefrontal cortex, Sci Rep, 7, 14993, 10.1038/s41598-017-12890-7

Igawa, 2001, Activation of visual cortex in rem sleep measured by 24-channel NIRS imaging, Psychiatry Clin Neurosci, 55, 187, 10.1046/j.1440-1819.2001.00819.x

Kaufmann, 2005, Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/FMRI study, Brain, 129, 655, 10.1093/brain/awh686

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Biocybernetics and Biomedical Engineering

Tập 40

527-545

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