These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

166 related articles for article (PubMed ID: 37050506)

  • 21. A hybrid self-attention deep learning framework for multivariate sleep stage classification.
    Yuan Y; Jia K; Ma F; Xun G; Wang Y; Su L; Zhang A
    BMC Bioinformatics; 2019 Dec; 20(Suppl 16):586. PubMed ID: 31787093
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Deep convolutional neural network for classification of sleep stages from single-channel EEG signals.
    Mousavi Z; Yousefi Rezaii T; Sheykhivand S; Farzamnia A; Razavi SN
    J Neurosci Methods; 2019 Aug; 324():108312. PubMed ID: 31201824
    [TBL] [Abstract][Full Text] [Related]  

  • 23. A New Method for Automatic Sleep Stage Classification.
    Zhang J; Wu Y
    IEEE Trans Biomed Circuits Syst; 2017 Oct; 11(5):1097-1110. PubMed ID: 28809709
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Analysis and Classification of Sleep Stages Based on Common Frequency Pattern From a Single-Channel EEG Signal.
    Huang S; Zhu J; Chen Y; Wang T; Ma T
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():3711-3714. PubMed ID: 33018807
    [TBL] [Abstract][Full Text] [Related]  

  • 25. SleepSEEG: automatic sleep scoring using intracranial EEG recordings only.
    von Ellenrieder N; Peter-Derex L; Gotman J; Frauscher B
    J Neural Eng; 2022 May; 19(2):. PubMed ID: 35439736
    [No Abstract]   [Full Text] [Related]  

  • 26. Determination of sleep stage separation ability of features extracted from EEG signals using principle component analysis.
    Vural C; Yildiz M
    J Med Syst; 2010 Feb; 34(1):83-9. PubMed ID: 20192058
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Adaptive Margin Aware Complement-Cross Entropy Loss for Improving Class Imbalance in Multi-View Sleep Staging Based on EEG Signals.
    Miao F; Yao L; Zhao X
    IEEE Trans Neural Syst Rehabil Eng; 2022; 30():2927-2938. PubMed ID: 36223360
    [TBL] [Abstract][Full Text] [Related]  

  • 28. [A hybrid attention temporal sequential network for sleep stage classification].
    Jin Z; Jia K; Yuan Y
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2021 Apr; 38(2):241-248. PubMed ID: 33913283
    [TBL] [Abstract][Full Text] [Related]  

  • 29. A comparative review on sleep stage classification methods in patients and healthy individuals.
    Boostani R; Karimzadeh F; Nami M
    Comput Methods Programs Biomed; 2017 Mar; 140():77-91. PubMed ID: 28254093
    [TBL] [Abstract][Full Text] [Related]  

  • 30. A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning.
    Sun C; Chen C; Li W; Fan J; Chen W
    IEEE J Biomed Health Inform; 2020 May; 24(5):1351-1366. PubMed ID: 31478877
    [TBL] [Abstract][Full Text] [Related]  

  • 31. SleepContextNet: A temporal context network for automatic sleep staging based single-channel EEG.
    Zhao C; Li J; Guo Y
    Comput Methods Programs Biomed; 2022 Jun; 220():106806. PubMed ID: 35461126
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Multivariate analysis of full-term neonatal polysomnographic data.
    Gerla V; Paul K; Lhotska L; Krajca V
    IEEE Trans Inf Technol Biomed; 2009 Jan; 13(1):104-10. PubMed ID: 19129029
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.
    Zhang J; Yao R; Ge W; Gao J
    Comput Methods Programs Biomed; 2020 Jan; 183():105089. PubMed ID: 31586788
    [TBL] [Abstract][Full Text] [Related]  

  • 34. An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model.
    Ghimatgar H; Kazemi K; Helfroush MS; Aarabi A
    J Neurosci Methods; 2019 Aug; 324():108320. PubMed ID: 31228517
    [TBL] [Abstract][Full Text] [Related]  

  • 35. A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.
    Chambon S; Galtier MN; Arnal PJ; Wainrib G; Gramfort A
    IEEE Trans Neural Syst Rehabil Eng; 2018 Apr; 26(4):758-769. PubMed ID: 29641380
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Assessment of Itakura Distance as a valuable feature for computer-aided classification of sleep stages.
    Ebrahimi F; Mikaili M; Estrada E; Nazeran H
    Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():3300-3. PubMed ID: 18002701
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Adversarial learning for semi-supervised pediatric sleep staging with single-EEG channel.
    Li Y; Peng C; Zhang Y; Zhang Y; Lo B
    Methods; 2022 Aug; 204():84-91. PubMed ID: 35364278
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Automated Sleep Staging via Parallel Frequency-Cut Attention.
    Chen Z; Yang Z; Zhu L; Chen W; Tamura T; Ono N; Altaf-Ul-Amin M; Kanaya S; Huang M
    IEEE Trans Neural Syst Rehabil Eng; 2023; 31():1974-1985. PubMed ID: 37022825
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Investigating the contribution of distance-based features to automatic sleep stage classification.
    Gharbali AA; Najdi S; Fonseca JM
    Comput Biol Med; 2018 May; 96():8-23. PubMed ID: 29529528
    [TBL] [Abstract][Full Text] [Related]  

  • 40. MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG.
    Yu R; Zhou Z; Wu S; Gao X; Bin G
    J Neural Eng; 2022 Dec; 19(6):. PubMed ID: 36379059
    [No Abstract]   [Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.