Relational local electroencephalography representations for sleep scoring
Tài liệu tham khảo
Anderer, 2005, An E-health solution for automatic sleep classification according to rechtschaffen and kales: Validation study of the somnolyzer 24 x 7 utilizing the siesta database, Neuropsychobiology, 51, 115, 10.1159/000085205
Anderer, 2010, Computer-assisted sleep classification according to the standard of the American academy of sleep medicine: Validation study of the AASM version of the somnolyzer 24 × 7, Neuropsychobiology, 62, 250, 10.1159/000320864
Ba, 2016
Bahdanau, 2015, Neural machine translation by jointly learning to align and translate, 1
Berry, 2013, The AASM manual for the scoring of sleep and associated events, American Academy of Sleep Medicine, 53, 1689
Bi, 2019, Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning, Neural Networks, 114, 119, 10.1016/j.neunet.2019.02.005
Brandmayr, 2021, Self-attention long-term dependency modelling in electroencephalography sleep stage prediction, 379
Danker-Hopfe, 2009, Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard, Journal of Sleep Research, 18, 74, 10.1111/j.1365-2869.2008.00700.x
Delgado, 2019, Why Cohen’s Kappa should be avoided as performance measure in classification, PLoS One, 14, 10.1371/journal.pone.0222916
Dong, 2018, Mixed neural network approach for temporal sleep stage classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26, 324, 10.1109/TNSRE.2017.2733220
Fürbass, 2020, An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard, Clinical Neurophysiology, 131, 1174, 10.1016/j.clinph.2020.02.032
Goldberger, 2000, PhysioBank, PhysioToolkit, and PhysioNet, Circulation, 101, 10.1161/01.CIR.101.23.e215
Graves, 2005, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks, 18, 602, 10.1016/j.neunet.2005.06.042
Guillot, 2019, Dreem open datasets: Multi-scored sleep datasets to compare human and automated sleep staging, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 1955, 10.1109/TNSRE.2020.3011181
He, 2016, Deep residual learning for image recognition, 770
Hochreiter, 2001
Hochreiter, 1997, Long short-term memory, Neural Computation, 9, 1735, 10.1162/neco.1997.9.8.1735
Ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 448
Kemp, 2000, Analysis of a sleep-dependent neuronal feedback loop: The slow-wave microcontinuity of the EEG, IEEE Transactions on Biomedical Engineering, 47, 1185, 10.1109/10.867928
Korkalainen, 2019, Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea, IEEE Journal of Biomedical and Health Informatics, 24, 1, 10.1109/JBHI.2019.2951346
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25, 1097
Lin, 2017, A structured self-attentive sentence embedding
Loshchilov, 2019, Decoupled weight decay regularization
Luong, 2015, Effective approaches to attention-based neural machine translation, 1412
Mousavi, 2019, Sleepeegnet: Automated sleep stage scoring with sequence to sequence deep learning approach, PLoS One, 14, 1, 10.1371/journal.pone.0216456
Olesen, 2018, Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms
O’Reilly, 2014, Montreal archive of sleep studies: An open-access resource for instrument benchmarking and exploratory research, Journal of Sleep Research, 23, 628, 10.1111/jsr.12169
Perslev, 2021, U-sleep: resilient high-frequency sleep staging, Npj Digital Medicine, 4, 72, 10.1038/s41746-021-00440-5
Perslev, 2019, U-time: A fully convolutional network for time series segmentation applied to sleep staging
Phan, 2019, SeqSleepNet: End-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27, 400, 10.1109/TNSRE.2019.2896659
Phan, 2018, DNN filter bank improves 1-max pooling CNN for single-channel EEG automatic sleep stage classification
Phan, 2020
Powers, 2012, The problem with kappa, 345
Qu, 2020, A residual based attention model for EEG based sleep staging, IEEE Journal of Biomedical and Health Informatics, 24, 2833, 10.1109/JBHI.2020.2978004
Rechtschaffen, 1968
Schirrmeister, 2017, Deep learning with convolutional neural networks for EEG decoding and visualization, Human Brain Mapping, 38, 5391, 10.1002/hbm.23730
Seeck, 2017, The standardized EEG electrode array of the IFCN, Clinical Neurophysiology, 128, 2070, 10.1016/j.clinph.2017.06.254
Seo, 2020, Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG, Biomedical Signal Processing and Control, 61, 10.1016/j.bspc.2020.102037
Shaw, 2018, Self-attention with relative position representations, 464
Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15, 1929
Supratak, 2017, DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25, 1998, 10.1109/TNSRE.2017.2721116
Tsinalis, 2016
Vaswani, 2017, Attention is all you need
Winter, 2013, Using the student’s t-test with extremely small sample sizes, Practical Assessment, Research and Evaluation, 18, 1
Xu, 2020, A one-dimensional CNN-LSTM model for epileptic seizure recognition using EEG signal analysis, Frontiers in Neuroscience, 14, 10.3389/fnins.2020.578126
Yuan, 2018, A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning, 206
Zhao, 2020, Exploring self-attention for image recognition, 10076
Zhu, 2020, Convolution-and attention-based neural network for automated sleep stage classification, International Journal of Environmental Research and Public Health, 17, 1, 10.3390/ijerph17114152