A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

friedman, 2001, Greedy function approximation: A gradient boosting machine, Ann Statist, 29, 1189, 10.1214/aos/1013203451

10.1016/j.neuroimage.2013.10.027

10.1111/j.1469-8986.2003.00141.x

srivastava, 2014, Dropout: A simple way to prevent neural networks from overfitting, J Mach Learn Res, 15, 1929

nair, 2010, Rectified linear units improve restricted boltzmann machines, Proc ICML, 807

lawhern, 2016, EEGNet a compact convolutional network for EEG-based brain-computer interfaces, 1

abadi, 2015, TensorFlow Large-Scale Machine Learning on Heterogeneous Systems

chollet, 2015, Keras

kingma, 2014, Adam: A method for stochastic optimization, CoRR, 1

goodfellow, 2016, Deep Learning

dong, 2016, Mixed neural network approach for temporal sleep stage classification

10.1145/2939672.2939785

tsinalis, 2016, Automatic sleep stage scoring with single-channel EEG using convolutional neural networks, 1

10.1109/TNSRE.2017.2721116

10.1016/j.bspc.2017.12.001

zhao, 2017, Learning sleep stages from radio signals: A conditional adversarial architecture, Proc 34th Int Conf Mach Learn, 70, 4100

10.1109/TKDE.2008.239

10.1016/j.jneumeth.2015.01.022

10.1111/jsr.12169

10.1016/j.neuroimage.2005.05.032

10.1109/MSP.2008.4408441

10.3389/fncom.2015.00146

10.1007/s10439-015-1444-y

stober, 2014, Using convolutional neural networks to recognize rhythm stimuli from electroencephalography recordings, Proc Adv Neural Inf Process Syst, 1449

10.1016/0013-4694(69)90021-2

rosenberg, 2014, The American academy of sleep medicine inter-scorer reliability program: Sleep stage scoring, J Clin Sleep Med, 10, 447, 10.5664/jcsm.3630

stober, 2016, Deep feature learning for eeg recordings, 1

stephansen, 2017, The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy, CoRR, 1

vilamala, 2017, Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring, CoRR, 1

10.3390/e18090272

iber, 2007, The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications

biswal, 2017, SLEEPNET: Automated sleep staging system via deep learning, CoRR, 1

10.1093/sleep/30.11.1587

10.3115/1073012.1073017

10.1109/10.623056

10.1162/neco.1997.9.8.1735

10.1088/1741-2560/8/3/036015

mirowski, 2009, Classification of patterns of EEG synchronization for seizure prediction, Clin Neurophysiol, 120, 1927, 10.1016/j.clinph.2009.09.002

zeiler, 2014, Visualizing and understanding convolutional networks, Proc Eur Conf Comp Vis (ECCV), 818

bashivan, 2016, Learning representations from EEG with deep recurrent-convolutional neural networks, Proc ICLR, 1

bergstra, 2013, Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures, Proc ICML, 1

10.1109/ICME.2014.6890166

10.1016/j.sleep.2004.06.007

10.1109/TPAMI.2010.125

springenberg, 2015, Striving for simplicity: The all convolutional net, Proc ICLR, 1

10.1109/ICPR.2008.4761638