Multilevel Inter-modal and Intra-modal Transformer network with domain adversarial learning for multimodal sleep staging

Alickovic E, Subasi A (2018) Ensemble SVM method for automatic sleep stage classification. IEEE Trans Instrum Meas 67(6):1258–1265

Article  Google Scholar 

Ba J, Kiros JR Hinton GE (2016) Layer normalization. arXiv: abs/1607.06450

Badiei A, Meshgini S, Rezaee K (2023) A novel approach for sleep arousal disorder detection based on the interaction of physiological signals and metaheuristic learning. Comput Intell Neurosci 2023:9379618. https://doi.org/10.1155/2023/9379618

Article  PubMed  PubMed Central  Google Scholar 

Banluesombatkul N et al (2021) MetaSleepLearner: a pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning. IEEE J Biomed Health Inform 25(6):1949–1963

Article  PubMed  Google Scholar 

Breiman L (2001) Random forests. Mach Learn 45(1):5–32

Article  Google Scholar 

Dai Y et al (2023) MultiChannelSleepNet: a transformer-based model for automatic sleep stage classification with PSG. IEEE J Biomed Health Inform 27(9):4204–4215

Article  PubMed  Google Scholar 

Eldele E et al (2021) An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 29:809–818

Article  PubMed  Google Scholar 

Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: 34th international conference on machine learning, Sydney, Australia

Ganin Y et al (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096–2030

Google Scholar 

Ghifary M et al (2017) Scatter component analysis: a unified framework for domain adaptation and domain generalization. IEEE Trans Pattern Anal Mach Intell 39(7):1414–1430

Article  PubMed  Google Scholar 

Goldberger AL et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

Article  CAS  PubMed  Google Scholar 

Guillot A, Thorey V (2021) RobustSleepNet: transfer learning for automated sleep staging at scale. IEEE Trans Neural Syst Rehabil Eng 29:1441–1451

Article  PubMed  Google Scholar 

Jia Z et al (2022) Hybrid spiking neural network for sleep electroencephalogram signals. Sci China Inf Sci 65(4):140403

Article  Google Scholar 

Jia Z et al (2021a) SalientSleepNet: multimodal salient wave detection network for sleep staging. arXiv: abs/2105.13864

Jia Z et al (2021b) GraphSleepNet: adaptive spatial–temporal graph convolutional networks for sleep stage classification. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence, Yokohama, Japan, p Article 184

Jia Z et al (2023) Exploiting interactivity and heterogeneity for sleep stage classification via heterogeneous graph neural network. In: IEEE international conference on acoustics, speech and signal Processing (ICASSP 2023)

Kemp B et al (2000) Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng 47(9):1185–1194

Article  CAS  PubMed  Google Scholar 

Khalighi S et al (2016) ISRUC-Sleep: a comprehensive public dataset for sleep researchers. Comput Methods Programs Biomed 124:180–192

Article  PubMed  Google Scholar 

Khosla P et al (2020) Supervised contrastive learning. arXiv: abs/2004.11362

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

Article  CAS  PubMed  Google Scholar 

Lee S et al (2024) SleePyCo: automatic sleep scoring with feature pyramid and contrastive learning. Expert Syst Appl 240:122551

Article  Google Scholar 

Li C et al (2022) A deep learning method approach for sleep stage classification with EEG spectrogram. Int J Environ Res Public Health 19(10):6322

Article  PubMed  PubMed Central  Google Scholar 

Liang H et al (2023) Teacher assistant-based knowledge distillation extracting multi-level features on single channel sleep EEG. In: Proceedings of the 32nd international joint conference on artificial intelligence, Macao, People’s Republic of China, p Article 439

Lin TY et al (2017) Feature pyramid networks for object detection. In: IEEE conference on computer vision and pattern recognition (CVPR)

Liu Y, Jia Z (2023) BSTT: a Bayesian spatial–temporal transformer for sleep staging. In: International conference on learning representations

Mourtazaev MS et al (1995) Age and gender affect different characteristics of slow waves in the sleep EEG. Sleep 18(7):557–564

Article  CAS  PubMed  Google Scholar 

Mousavi S, Afghah F, Acharya UR (2019) SleepEEGNet: automated sleep stage scoring with sequence to sequence deep learning approach. PLoS ONE 14

O’Reilly C et al (2014) Montreal Archive of Sleep Studies: an open-access resource for instrument benchmarking and exploratory research. J Sleep Res 23(6):628–635

Article  PubMed  Google Scholar 

Perslev M et al (2019) U-time: a fully convolutional network for time series segmentation applied to sleep staging. arXiv: abs/1910.11162

Phan H et al (2019a) SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Trans Neural Syst Rehabil Eng 27(3):400–410

Article  PubMed  PubMed Central  Google Scholar 

Phan H et al (2019b) Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Trans Biomed Eng 66(5):1285–1296

Article  PubMed  Google Scholar 

Phan H et al (2020) XSleepNet: multi-view sequential model for automatic sleep staging. IEEE Trans Pattern Anal Mach Intell 44:5903–5915

Google Scholar 

Phan H et al (2021) Towards more accurate automatic sleep staging via deep transfer learning. IEEE Trans Biomed Eng 68(6):1787–1798

Article  PubMed  Google Scholar 

Phan H et al (2022) SleepTransformer: automatic sleep staging with interpretability and uncertainty quantification. IEEE Trans Biomed Eng 69(8):2456–2467

Article  PubMed  Google Scholar 

Phan H et al (2019) Deep transfer learning for single-channel automatic sleep staging with channel mismatch. In: 27th European signal processing conference (EUSIPCO)

Pradeepkumar J et al (2022) Towards interpretable sleep stage classification using cross-modal transformers. arXiv: abs/2208.06991

She Q et al (2023) Improved domain adaptation network based on Wasserstein distance for motor imagery EEG classification. IEEE Trans Neural Syst Rehabil Eng 31:1137–1148

Article  PubMed  Google Scholar 

Stephansen JB et al (2018) Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy. Nat Commun 9(1):5229

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sun J et al (2023) START: automatic sleep staging with attention-based cross-modal learning transformer. In: IEEE international conference on bioinformatics and biomedicine (BIBM)

Sun Y et al (2018) Deep convolutional network method for automatic sleep stage classification based on neurophysiological signals. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

Sundar GN et al (2021) Automated sleep stage classification in sleep apnoea using convolutional neural networks. Inform Med Unlocked 26:100724

Article  Google Scholar 

Supratak A et al (2017) DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 25(11):1998–2008

Article  PubMed  Google Scholar 

Supratak A, Guo Y (2020) TinySleepNet: an efficient deep learning model for sleep stage scoring based on raw single-channel EEG. In: 42nd annual international conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp 641–644

Wang J et al (2021) Generalizing to unseen domains: a survey on domain generalization. IEEE Trans Knowl Data Eng 35:8052–8072

Google Scholar 

Wang H et al (2022) Automatic sleep staging method of EEG signal based on transfer learning and fusion network. Neurocomputing 488:183–193

Article  Google Scholar 

Yazdi M, Samaee M, Massicotte D (2024) A review on automated sleep study. Ann Biomed Eng 52(6):1463–1491

Article  PubMed  Google Scholar 

Zhang X et al (2024) A review of automated sleep stage based on EEG signals. Biocybern Biomed Eng 44(3):651–673

Article  Google Scholar 

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