This repo contains a list of papers for sleep stages classification using machine learning/deep learning.
If you have any suggested papers, please contact me ziyujia{at}bjtu.edu.cn
We provide an introduction and some examples of the tools for processing PSG signals for sleep staging. Please visit here.
Title | Author | Date | Publication | URL | Model | Signal type | Code |
---|---|---|---|---|---|---|---|
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification | Huy Phan, Kaare B Mikkelsen, Oliver Chen, Philipp Koch, Alfred Mertins, Maarten De Vos | 2022 | IEEE Transactions on Biomedical Engineering | URL | Transformer | EEG | |
Automatic sleep stage classification based on two-channel EOG and one-channel EMG | Yanjun Li, Zhi Xu, Yu Zhang, Zhongping Cao and Hua Chen | 2022 | Physiological Measurement | URL | RF | PSG | |
Sleep staging classification based on a new parallel fusion method of multiple sources signals | Yafang Hei, Tuming Yuan, Zhigao Fan, Bo Yang and Jiancheng Hu | 2022 | Physiological Measurement | URL | NN, XGBoost | PSG | |
Multimodal Multiclass Machine Learning Model for Automated Sleep Staging Based on Time Series Data | Satapathy S K, Loganathan D | 2022 | SN Computer Science | URL | RF, AdaBoost | PSG | |
Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification | Xiaopeng Ji, Yan Li, Peng Wen | 2022 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | JK-STGCN | PSG | |
A hybrid deep learning scheme for multi-channel sleep stage classification | Pei, Wei and Li, Yan and Siuly, Siuly and Wen, Peng | 2022 | Computers, Materials and Continua | URL | CNN, GRU | PSG | |
Self-supervised EEG Representation Learning for Automatic Sleep Staging | Chaoqi Yang, Danica Xiao, M. Brandon Westover, Jimeng Sun | 2022 | arXiv | URL | ContraWR | EEG | |
Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages | Iqram Hussain, Md Azam Hossain, Rafsan Jany, Md Abdul Bari, Musfik Uddin, Abu Raihan Mostafa Kamal, Yunseo Ku and Jik-Soo Kim | 2022 | MDPI Sensors | URL | NN | EEG | |
Deep adaptation network for subject-specific sleep stage classification based on a single-lead ECG | Tang M, Zhang Z, He Z, et al. | 2022 | Biomedical Signal Processing and Control | URL | NN | ECG | |
Deep Convolutional Recurrent Model for Automatic Scoring Sleep Stages Based on Single-Lead ECG Signal | Erdenebayar Urtnasan Jong-Uk Park, Eun Yeon Joo and Kyoung-Joung Lee | 2022 | Diagnostics | URL | CNN+LSTM | ECG | |
Two-Stream Squeeze-and-Excitation Network for Multi-modal Sleep Staging | Xiyang Cai, Ziyu Jia, Zehui Jiao | 2021 | 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) | URL | TS-SEN | PSG | Github |
Automatic Sleep Stage Identification with Time Distributed Convolutional Neural Network | Micheal Dutt, Morten Goodwin, Christian W. Omlin | 2021 | 2021 International Joint Conference on Neural Networks (IJCNN) | URL | CNN | PSG | |
EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal | Jiahao Fan, Chenglu Sun, Meng Long, Chen Chen, and Wei Chen | 2021 | Frontiers in Neuroscience | URL | CNN+LSTM | EOG | |
An EEG spectrogram-based automatic sleep stage scoring method via data augmentation, ensemble convolution neural network, and expert knowledge | Chih-En Kuo, Guan-Ting Chen, Po-Yu Liao | 2021 | Biomedical Signal Processing and Control | URL | CNN | EEG | |
An effective multi-model fusion method for EEG-based sleep stage classification | Panfeng An, Zhiyong Yuan, Jianhui Zhao, Xue Jiang, Bo Du | 2021 | Knowledge-Based Systems | URL | SVM | EEG | |
A new dissimilarity measure based on ordinal pattern for analyzing physiological signals | Yunxiao Liu, Youfang Lin, Ziyu Jia, Jing Wang, Ma Yan | 2021 | Physica A: Statistical Mechanics and its Applications | URL | -- | EEG | |
Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals | Manish Sharma, Harch S. Dhiman, U. Rajendra Acharya | 2021 | Computers in Biology and Medicine | URL | SVM | ECG | |
Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data | Nico Surantha, Tri Fennia Lesmana and Sani Muhamad Isa | 2021 | Journal of Big Data | URL | ELM, PSO | ECG | |
Self-supervised Contrastive Learning for EEG-based Sleep Staging | Xue Jiang, Jianhui Zhao, Bo Du, Zhiyong Yuan | 2021 | International Joint Conference on Neural Networks (IJCNN) | URL | SSL | EEG | Github |
Self-Supervised Learning for Sleep Stage Classification with Predictive and Discriminative Contrastive Coding | Qinfeng Xiao, Jing Wang, Jianan Ye, Hongjun Zhang, Yuyan Bu, Yiqiong Zhang, Hao Wu | 2021 | IEEE International Conference on Acoustics, Speech and Signal Processing | URL | SSL | EEG | |
Time-Series Representation Learning via Temporal and Contextual Contrasting | Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li and Cuntai Guan | 2021 | IJCAI-21 | URL | TS-TCC | EEG~~, ECG~~ | Github |
RobustSleepNet: Transfer learning for automated sleep staging at scale | Antoine Guillot, Valentin Thorey | 2021 | IEEE Engineering in Medicine and Biology Society (EMBC) | URL | Transfer Learning | PSG | Github |
An Attention-based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG | Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan | 2021 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | CNN | EEG | Github |
CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG | Wenpeng Neng, Jun Lu, and Lei Xu | 2021 | Brain Sciences | URL | CNN, LSTM | EEG | Github |
SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging | Ziyu Jia, Youfang Lin , Jing Wang, Xuehui Wang , Peiyi Xie and Yingbin Zhang | 2021 | IJCAI-21 | URL | CNN | PSG | Github |
Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification | Ziyu Jia, Youfang Lin, Jing Wang, Xiaojun Ning, Yuanlai He, Ronghao Zhou, Yuhan Zhou, Li-wei H. Lehman | 2021 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | GCN | PSG | Github |
Automatic Sleep Stage Classification using Marginal Hilbert Spectrum Features and a Convolutional Neural Network | Wenshuai Wang, Pan Liao, Yi Sun, Guiping Su, Shiwei Ye, Yan Liu | 2020 | International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) | URL | CNN | EEG | |
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning | Nannapas Banluesombatkul, Pichayoot Ouppaphan, Pitshaporn Leelaarporn, Payongkit Lakhan; | 2020 | IEEE Journal of Biomedical and Health Informatics | URL | Transfer Learning | PSG | Github |
Personalized automatic sleep staging with single-night data: a pilot study with Kullback–Leibler divergence regularization | Huy Phan, Kaare Mikkelsen, Oliver Y Chén, Philipp Koch, Alfred Mertins, Preben Kidmose and Maarten De Vos | 2020 | URL | Transfer Learning | |||
A Residual Based Attention Model for EEG Based Sleep Staging | Wei Qu, Zhiyong Wang, Hong Hong, Zheru Chi, David Dagan Feng, Ron Grunstein, Christopher Gordon | 2020 | IEEE Journal of Biomedical and Health Informatics | URL | CNN | EEG | |
Convolution and Attention-Based Neural Network for Automated Sleep Stage Classification | Tianqi Zhu, Wei Luo and Feng Yu | 2020 | International Journal of Environmental Research and Public Health | URL | CNN | PSG | |
An Automatic Sleep Staging Model Combining Feature Learning and Sequence Learning | Yinghao Li, Zhenghui Gu, Zichao Lin, Zhuliang Yu, Yuanqing Li | 2020 | International Conference on Advanced Computational Intelligence (ICACI) | URL | CNN+LSTM | PSG | |
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging | Huy Phan, Oliver Y. Chén, Minh C. Tran, Philipp Koch, Alfred Mertins, and Maarten De Vos | 2020 | IEEE Transactions on Pattern Analysis and Machine Intelligence | URL | CNN+LSTM | PSG | Github |
TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG | Akara Supratak, Yike Guo | 2020 | International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) | URL | CNN | EEG | Github |
An Automatic Sleep Staging Method Using a Multi-head and Sequence Network | Qing Wang, Shan Wang, Na Wu, Li-Qun Xu | 2020 | International Conference on Biological Information and Biomedical Engineering | URL | CNN+LSTM | EEG, EOG | |
SleepPrintNet: A Multivariate Multimodal Neural Network based on Physiological Time-series for Automatic Sleep Staging | Ziyu Jia, Xiyang Cai, Gaoxing Zheng, Jing Wang, Youfang Lin | 2020 | IEEE Transactions on Artificial Intelligence | URL | CNN | PSG | Github |
Computation-Efficient Multi-Model Deep Neural Network for Sleep Stage Classification | Mingkai Xu, Xingjun Wang, Xiaoqing Zhangt, Guangxiang Bin, Ziqian Jia, Kexin Chen | 2020 | Asia Service Sciences and Software Engineering Conference | URL | CNN | PSG | |
TRIER: Template-Guided Neural Networks for Robust and Interpretable Sleep Stage Identification from EEG Recordings | Taeheon Lee, Jeonghwan Hwang and Honggu Lee | 2020 | arXiv | URL | CNN+RNN | ||
A Graph-Temporal fused dual-input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals | Qing Cai, Zhongke Gao, Jianpeng An, Shuang Gao, Celso Grebogi | 2020 | IEEE Transactions on Circuits and Systems II: Express Briefs | URL | EEG | ||
Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification | Ziyu Jia , Youfang Lin, Jing Wang, Ronghao Zhou , Xiaojun Ning , Yuanlai He and Yaoshuai Zhao | 2020 | Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI | URL | GCN | EEG | Github |
Towards more accurate automatic sleep staging via deep transfer learning | Phan, Huy, et al. | 2020 | IEEE Transactions on Biomedical Engineering | URL | BiRNN, CNN | PSG | Github |
Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study | Fiorillo, Luigi, et al. | 2020 | 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) | URL | CNN, LSTM | EEG | |
Intra-and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG | Seo, Hogeon, et al. | 2020 | Biomedical Signal Processing and Control | URL | CNN, RNN | EEG | |
Automatic identification of insomnia based on single-channel EEG labelled with sleep stage annotations | Yang, Bufang and Liu, Hongxing | 2020 | IEEE Access | URL | CNN | EEG | |
Automatic sleep stage classification with single channel EEG signal based on two-layer stacked ensemble model | Zhou, Jinjin, et al. | 2020 | IEEE Access | URL | RF, LGB | EEG | |
Long short-term memory networks for unconstrained sleep stage classification using polyvinylidene fluoride film sensor | Choi, Sang Ho, et al. | 2020 | IEEE journal of biomedical and health informatics | URL | LSTM | PVDF | |
DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification | Huy Phan, Fernando Andreotti, Navin Cooray, Oliver Y. Chèn, Maarten De Vos | 2019 | IEEE Engineering in Medicine and Biology Society (EMBC) | URL | CNN | EEG | |
Deep learning for automated feature discovery and classification of sleep stages | Michael Sokolovsky, Francisco Guerrero, Sarun Paisarnsrisomsuk, Carolina Ruiz, S. A. Alvarez | 2019 | IEEE Transactions On Computational Biology and Bioinformatics | URL | CNN | PSG | |
Automatic Sleep Staging Based on XGBOOST Physiological Signals | Zhao, Xiangfa, et al. | 2019 | Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019) | URL | XGBOOST | EEG | |
Diffuse to fuse EEG spectra-intrinsic geometry of sleep dynamics for classification | Liu, Gi-Ren, et al. | 2019 | Biomedical Signal Processing and Control | URL | Dffusion Map | EEG | |
Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG | Zhang, Junming, et al. | 2019 | Computer methods and programs in biomedicine | URL | CNN | EEG, ECG | |
An image based prediction model for sleep stage identification | Kanwal, Saira, et al. | 2019 | 2019 IEEE International Conference on Image Processing (ICIP) | URL | CNN | EEG | |
A hierarchical neural network for sleep stage classification based on comprehensive feature learning and multi-flow sequence learning | Sun, Chenglu, et al. | 2019 | IEEE journal of biomedical and health informatics | URL | CNN, BiLSTM | PSG | |
A two-stage neural network for sleep stage classification based on feature learning, sequence learning, and data augmentation | Sun, Chenglu, et al. | 2019 | IEEE Access | URL | WDBN, BiLSTM | PSG | |
Sleep stage classification from heart-rate variability using long short-term memory neural networks | Radha, Mustafa, et al. | 2019 | Scientific reports | URL | LSTM | PSG | |
Deep convolutional neural network for classification of sleep stages from single-channel EEG signals | Mousavi, Z., et al. | 2019 | Journal of neuroscience methods | URL | CNN | EEG | |
An ultra-low-power dual-mode automatic sleep staging processor using neural-network-based decision tree | Chang, Shang-Yuan, et al. | 2019 | IEEE Transactions on Circuits and Systems I: Regular Papers | URL | WPD, DT | EEG, EMG | |
A Novel Sleep Staging Algorithm Based on Hybrid Neural Network | Hao, Jingwei, et al. | 2019 | 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC) | URL | CNN, BiLSTM | EEG | |
Pediatric sleep stage classification using multi-domain hybrid neural networks | Jeon, Yonghoon, et al. | 2019 | IEEE Access | URL | CNN, BiLSTM | EEG | |
Convolutional neural networks for sleep stage scoring on a two-channel EEG signal | Fernandez-Blanco, Enrique, et al. | 2019 | Soft Computing | URL | CNN | EEG | |
A multi-class automatic sleep staging method based on long short-term memory network using single-lead electrocardiogram signals | Wei, Yuhui, et al. | 2019 | IEEE Access | URL | LSTM | PSG | |
End-to-end sleep staging with raw single channel EEG using deep residual convnets | Humayun, Ahmed Imtiaz, et al. | 2019 | 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) | URL | CNN, LSTM | EEG | |
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach | Mousavi, Sajad, et al. | 2019 | PloS one | URL | CNN, BiLSTM | EEG | Github |
Deep learning for automated feature discovery and classification of sleep stages | Sokolovsky, Michael, et al. | 2019 | IEEE/ACM transactions on computational biology and bioinformatics | URL | CNN | EEG, EOG | |
utomatic sleep staging employing convolutional neural networks and cortical connectivity images | Chriskos, Panteleimon, et al. | 2019 | IEEE transactions on neural networks and learning systems | URL | CNN | EEG | |
Investigating the effect of short term responsive VNS therapy on sleep quality using automatic sleep staging | Ravan, Maryam, et al. | 2019 | IEEE Transactions on Biomedical Engineering | URL | SVM | EEG | |
Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals | Michielli, Nicola, et al. | 2019 | Computers in biology and medicine | URL | LSTM | EEG | |
SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging | Phan, Huy, et al. | 2019 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | RNN | PSG | Github |
A deep learning model for automated sleep stages classification using PSG signals | Yildirim, Ozal, et al. | 2019 | International journal of environmental research and public health | URL | CNN | EEG, EOG | |
U-time: A fully convolutional network for time series segmentation applied to sleep staging | Perslev, Mathias, et al. | 2019 | Advances in Neural Information Processing Systems | URL | U-Net | EEG | |
An Image Based Prediction Model for Sleep Stage Identification | Saira Kanwal, Muhammad Uzair, Habib Ullah, Sultan Daud Khan, Mohib Ullah, Faouzi Alaya Cheikh | 2018 | IEEE International Conference on Image Processing (ICIP) | URL | CNN | EEG | |
Deep Convolutional Network Method for Automatic Sleep Stage Classification Based on Neurophysiological Signals | Yudong Sun, Bei Wang, Jing Jin, Xingyu Wang | 2018 | International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) | URL | CNN+LSTM | PSG | |
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms | Alexander N. Olesen†, Poul Jennum, Paul Peppard, Emmanuel Mignot, and Helge B. D. Sorensen | 2018 | IEEE Engineering in Medicine and Biology Society (EMBC) | URL | CNN | PSG | |
Sleep Stage Classification Based on EEG Signal by Using EMD and DFA Algorithm | Zhang, Yan, et al. | 2018 | Proceedings of the 2018 International Conference on Robotics, Control and Automation Engineering | URL | EMD, DFA | EEG | |
A structured learning approach with neural conditional random fields for sleep staging | Aggarwal, Karan, et al. | 2018 | 2018 IEEE International Conference on Big Data (Big Data) | URL | CNN, LSTM | Nasal Airflow | |
Multivariate sleep stage classification using hybrid self-attentive deep learning networks | Yuan, Ye, et al. | 2018 | 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) | URL | Self-Attention | PSG | |
Expert-level sleep scoring with deep neural networks | Biswal, Siddharth , et al. | Dec-2018 | Journal of the American Medical Informatics Association | URL | CNN, LSTM | EEG, EMG | |
Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device | Zhang, Xin, et al. | Nov-2018 | Computers in biology and medicine | URL | BiLSTM | PSG | |
Automatic sleep stage classification using single-channel eeg: Learning sequential features with attention-based recurrent neural networks | Phan, Huy, et al. | 2018 | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | URL | BiGRU, SVM | EEG | |
Joint classification and prediction CNN framework for automatic sleep stage classification | Phan, Huy, et al. | 2018 | IEEE Transactions on Biomedical Engineering | URL | CNN | EEG, EOG | Github |
Recurrent deep neural networks for real-time sleep stage classification from single channel EEG | Bresch, Erik, et al. | 2018 | Frontiers in computational neuroscience | URL | CNN, LSTM | EEG | |
Deep convolutional network method for automatic sleep stage classification based on neurophysiological signals | Sun, Yudong, et al. | 2018 | 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) | URL | CNN, LSTM | EEG | |
DNN filter bank improves 1-max pooling CNN for single-channel EEG automatic sleep stage classification | Phan, Huy, et al. | 2018 | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | URL | DNN, CNN | EEG | |
Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms | Olesen, Alexander N., et al. | 2018 | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | URL | CNN | PSG | |
Complex-valued unsupervised convolutional neural networks for sleep stage classification | Zhang, Junming and Wu, Yan | 2018 | Computer methods and programs in biomedicine | URL | CNN | EEG | |
Multichannel sleep stage classification and transfer learning using convolutional neural networks | Andreotti, Fernando, et al. | 2018 | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | URL | CNN | PSG | |
An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank | Sharma, Manish, et al. | 2018 | Computers in biology and medicine | URL | SVM | EEG | |
A convolutional neural network for sleep stage scoring from raw single-channel EEG | Sors, Arnaud, et al. | 2018 | Biomedical Signal Processing and Control | URL | CNN | EEG | |
An end-to-end framework for real-time automatic sleep stage classification | Patanaik, Amiya, et al. | 2018 | Sleep | URL | CNN | EEG, EOG | |
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series | Chambon, Stanislas, et al. | 2018 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | CNN, LSTM | PSG | |
Personalizing deep learning models for automatic sleep staging | Mikkelsen, Kaare, et al. | 2018 | arXiv | URL | CNN | EEG, EOG | |
Visualising convolutional neural network decisions in automatic sleep scoring | Andreotti, Fernando, et al. | 2018 | CEUR Workshop Proceedings | URL | CNN | PSG | |
ResSleepNet: Automatic sleep stage classification on raw single-channel EEG | Liu, Cheng, et al. | 2018 | IOP Conference Series: Materials Science and Engineering | URL | ResNet | EEG | |
A novel multi-class EEG-based sleep stage classification system | Memar, Pejman, et al. | 2017 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | RF | EEG | |
Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring | Vilamala, Albert, et al. | 2017 | 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) | URL | CNN | EEG | |
A new method for automatic sleep stage classification | Zhang, Junming and Wu, Yan | 2017 | IEEE transactions on biomedical circuits and systems | URL | FDCCNN | EEG | |
Mixed neural network approach for temporal sleep stage classification | Dong, Hao, et al. | 2017 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | MLP, RNN | EEG, EOG | |
SLEEPNET: automated sleep staging system via deep learning | Biswal, Siddharth, et al. | 2017 | arXiv | URL | CNN, RNN | EEG | |
A decision support system for automated identification of sleep stages from single-channel EEG signals | Hassan, Ahnaf Rashik and Subasi, Abdulhamit | 2017 | Knowledge-Based Systems | URL | Bagging | EEG | |
DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG | Supratak, Akara, et al. | 2017 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | CNN, BiLSTM | EEG | |
Deep learning and insomnia: assisting clinicians with their diagnosis | Shahin, Mostafa, et al. | 2017 | IEEE journal of biomedical and health informatics | URL | DNN | EEG | |
Learning sleep stages from radio signals: A conditional adversarial architecture | Zhao, Mingmin, et al. | 2017 | International Conference on Machine Learning | URL | CNN, RNN | EEG | |
Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain | da Silveira, Thiago LT, et al. | May-2016 | Medical & biological engineering & computing | URL | RF | EEG | |
EEG sleep stages classification based on time domain features and structural graph similarity | Diykh, Mohammed, et al. | 2016 | IEEE Transactions on Neural Systems and Rehabilitation Engineering | URL | SGSKM | EEG | |
Nonlinear dynamics measures for automated EEG-based sleep stage detection | Acharya, U Rajendra, et al. | 2016 | European neurology | URL | NDM | EEG | |
Automatic classification of sleep stages based on the time-frequency image of EEG signals | Bajaj, Varun, et al. | 2013 | Computer methods and programs in biomedicine | URL | MC-LS-SVM | EEG | |
Investigation of an automatic sleep stage classification by means of multiscorer hypnogram | Helland, VC Figueroa, et al. | 2010 | Methods Inf Med | URL | LDS | EEG, ECG | |
Analysis and automatic identification of sleep stages using higher order spectra | Acharya, U Rajendra, et al. | 2010 | International journal of neural systems | URL | GMM | EEG | |
Classification of human sleep stages based on EEG processing using hidden Markov models | Doroshenkov, LG , et al. | 2007 | Biomedical Engineering | URL | HMM | PSG |
Yingbin Zhang, Ronghao Zhou, Xiaojun Ning, Xiyang Cai, and Ziyu Jia collaborated to organize and summarize the above papers.