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For: Rempe MJ, Clegern WC, Wisor JP. An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters. Nat Sci Sleep 2015;7:85-99. [PMID: 26366107 DOI: 10.2147/NSS.S84548] [Cited by in Crossref: 14] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Miladinović Đ, Muheim C, Bauer S, Spinnler A, Noain D, Bandarabadi M, Gallusser B, Krummenacher G, Baumann C, Adamantidis A, Brown SA, Buhmann JM. SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species. PLoS Comput Biol 2019;15:e1006968. [PMID: 30998681 DOI: 10.1371/journal.pcbi.1006968] [Cited by in Crossref: 16] [Cited by in F6Publishing: 10] [Article Influence: 5.3] [Reference Citation Analysis]
2 Allocca G, Ma S, Martelli D, Cerri M, Del Vecchio F, Bastianini S, Zoccoli G, Amici R, Morairty SR, Aulsebrook AE, Blackburn S, Lesku JA, Rattenborg NC, Vyssotski AL, Wams E, Porcheret K, Wulff K, Foster R, Chan JKM, Nicholas CL, Freestone DR, Johnston LA, Gundlach AL. Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data. Front Neurosci 2019;13:207. [PMID: 30936820 DOI: 10.3389/fnins.2019.00207] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 5.3] [Reference Citation Analysis]
3 Ellen JG, Dash MB. An artificial neural network for automated behavioral state classification in rats. PeerJ 2021;9:e12127. [PMID: 34589305 DOI: 10.7717/peerj.12127] [Reference Citation Analysis]
4 Harkness JH, Bushana PN, Todd RP, Clegern WC, Sorg BA, Wisor JP. Sleep disruption elevates oxidative stress in parvalbumin-positive cells of the rat cerebral cortex. Sleep 2019;42. [PMID: 30371896 DOI: 10.1093/sleep/zsy201] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 4.0] [Reference Citation Analysis]
5 Barger Z, Frye CG, Liu D, Dan Y, Bouchard KE. Robust, automated sleep scoring by a compact neural network with distributional shift correction. PLoS One 2019;14:e0224642. [PMID: 31834897 DOI: 10.1371/journal.pone.0224642] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
6 Katsageorgiou VM, Sona D, Zanotto M, Lassi G, Garcia-Garcia C, Tucci V, Murino V. A novel unsupervised analysis of electrophysiological signals reveals new sleep substages in mice. PLoS Biol 2018;16:e2003663. [PMID: 29813050 DOI: 10.1371/journal.pbio.2003663] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
7 Yamabe M, Horie K, Shiokawa H, Funato H, Yanagisawa M, Kitagawa H. MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks. Sci Rep 2019;9:15793. [PMID: 31672998 DOI: 10.1038/s41598-019-51269-8] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.7] [Reference Citation Analysis]
8 Cary BA, Turrigiano GG. Stability of neocortical synapses across sleep and wake states during the critical period in rats. Elife 2021;10:e66304. [PMID: 34151775 DOI: 10.7554/eLife.66304] [Reference Citation Analysis]