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Cited by in F6Publishing
For: Zhang X, Xu M, Li Y, Su M, Xu Z, Wang C, Kang D, Li H, Mu X, Ding X, Xu W, Wang X, Han D. Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data. Sleep Breath 2020;24:581-90. [PMID: 31938990 DOI: 10.1007/s11325-019-02008-w] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Shen H, Ran F, Xu M, Guez A, Li A, Guo A. An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features. Sensors (Basel) 2020;20:E4677. [PMID: 32825024 DOI: 10.3390/s20174677] [Cited by in Crossref: 13] [Cited by in F6Publishing: 5] [Article Influence: 6.5] [Reference Citation Analysis]
2 Zhu T, Luo W, Yu F. Multi-Branch Convolutional Neural Network for Automatic Sleep Stage Classification with Embedded Stage Refinement and Residual Attention Channel Fusion. Sensors (Basel) 2020;20:E6592. [PMID: 33218040 DOI: 10.3390/s20226592] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Kiss T, Morairty S, Schwartz M, Kilduff T, Buhl D, Volfson D. Automated Sleep Stage Scoring Using k-Nearest Neighbors Classifier. JOSS 2020;5:2377. [DOI: 10.21105/joss.02377] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
4 Loh HW, Ooi CP, Vicnesh J, Oh SL, Faust O, Gertych A, Acharya UR. Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020). Applied Sciences 2020;10:8963. [DOI: 10.3390/app10248963] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 4.5] [Reference Citation Analysis]
5 Wang H, Lin G, Li Y, Zhang X, Xu W, Wang X, Han D. Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network. Nat Sci Sleep 2021;13:2101-12. [PMID: 34876865 DOI: 10.2147/NSS.S336344] [Reference Citation Analysis]
6 Satapathy SK, Loganathan D. Automated classification of multi-class sleep stages classification using polysomnography signals: a nine- layer 1D-convolution neural network approach. Multimed Tools Appl. [DOI: 10.1007/s11042-022-13195-2] [Reference Citation Analysis]
7 Kuo C, Chen G, Liao P. An EEG spectrogram-based automatic sleep stage scoring method via data augmentation, ensemble convolution neural network, and expert knowledge. Biomedical Signal Processing and Control 2021;70:102981. [DOI: 10.1016/j.bspc.2021.102981] [Reference Citation Analysis]
8 Tama BA, Kim DH, Kim G, Kim SW, Lee S. Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery. Clin Exp Otorhinolaryngol 2020;13:326-39. [PMID: 32631041 DOI: 10.21053/ceo.2020.00654] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Hong JK, Lee T, Delos Reyes RD, Hong J, Tran HH, Lee D, Jung J, Yoon IY. Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring. Nat Sci Sleep 2021;13:2239-50. [PMID: 35002345 DOI: 10.2147/NSS.S333566] [Reference Citation Analysis]