201 related articles for article (PubMed ID: 34822945)
1. Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning.
Zhang X; Landsness EC; Chen W; Miao H; Tang M; Brier LM; Culver JP; Lee JM; Anastasio MA
J Neurosci Methods; 2022 Jan; 366():109421. PubMed ID: 34822945
[TBL] [Abstract][Full Text] [Related]
2. Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data.
Zhang X; Landsness EC; Miao H; Chen W; Tang M; Brier LM; Culver JP; Lee JM; Anastasio MA
ArXiv; 2024 Jan; ():. PubMed ID: 38313204
[TBL] [Abstract][Full Text] [Related]
3. Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram.
Li Q; Li Q; Liu C; Shashikumar SP; Nemati S; Clifford GD
Physiol Meas; 2018 Dec; 39(12):124005. PubMed ID: 30524025
[TBL] [Abstract][Full Text] [Related]
4. Automated sleep classification with chronic neural implants in freely behaving canines.
Mivalt F; Sladky V; Worrell S; Gregg NM; Balzekas I; Kim I; Chang SY; Montonye DR; Duque-Lopez A; Krakorova M; Pridalova T; Lepkova K; Brinkmann BH; Miller KJ; Van Gompel JJ; Denison T; Kaufmann TJ; Messina SA; St Louis EK; Kremen V; Worrell GA
J Neural Eng; 2023 Aug; 20(4):. PubMed ID: 37536320
[No Abstract] [Full Text] [Related]
5. Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.
Zhang L; Fabbri D; Upender R; Kent D
Sleep; 2019 Oct; 42(11):. PubMed ID: 31289828
[TBL] [Abstract][Full Text] [Related]
6. Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.
Abou Jaoude M; Sun H; Pellerin KR; Pavlova M; Sarkis RA; Cash SS; Westover MB; Lam AD
Sleep; 2020 Nov; 43(11):. PubMed ID: 32478820
[TBL] [Abstract][Full Text] [Related]
7. Spike-Based Functional Connectivity in Cerebral Cortex and Hippocampus: Loss of Global Connectivity Is Coupled to Preservation of Local Connectivity During Non-REM Sleep.
Olcese U; Bos JJ; Vinck M; Lankelma JV; van Mourik-Donga LB; Schlumm F; Pennartz CM
J Neurosci; 2016 Jul; 36(29):7676-92. PubMed ID: 27445145
[TBL] [Abstract][Full Text] [Related]
8. Design and validation of a computer-based sleep-scoring algorithm.
Louis RP; Lee J; Stephenson R
J Neurosci Methods; 2004 Feb; 133(1-2):71-80. PubMed ID: 14757347
[TBL] [Abstract][Full Text] [Related]
9. An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects.
Sharma M; Yadav A; Tiwari J; Karabatak M; Yildirim O; Acharya UR
Int J Environ Res Public Health; 2022 Jun; 19(12):. PubMed ID: 35742426
[TBL] [Abstract][Full Text] [Related]
10. The reliability and functional validity of visual and semiautomatic sleep/wake scoring in the Møll-Wistar rat.
Neckelmann D; Olsen OE; Fagerland S; Ursin R
Sleep; 1994 Mar; 17(2):120-31. PubMed ID: 8036366
[TBL] [Abstract][Full Text] [Related]
11. Automated determination of wakefulness and sleep in rats based on non-invasively acquired measures of movement and respiratory activity.
Zeng T; Mott C; Mollicone D; Sanford LD
J Neurosci Methods; 2012 Mar; 204(2):276-87. PubMed ID: 22178621
[TBL] [Abstract][Full Text] [Related]
12. A novel machine learning system for identifying sleep-wake states in mice.
Fraigne JJ; Wang J; Lee H; Luke R; Pintwala SK; Peever JH
Sleep; 2023 Jun; 46(6):. PubMed ID: 37021715
[TBL] [Abstract][Full Text] [Related]
13. A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram.
Li C; Qi Y; Ding X; Zhao J; Sang T; Lee M
Int J Environ Res Public Health; 2022 May; 19(10):. PubMed ID: 35627856
[TBL] [Abstract][Full Text] [Related]
14. Noninvasive three-state sleep-wake staging in mice using electric field sensors.
Kloefkorn H; Aiani LM; Lakhani A; Nagesh S; Moss A; Goolsby W; Rehg JM; Pedersen NP; Hochman S
J Neurosci Methods; 2020 Oct; 344():108834. PubMed ID: 32619585
[TBL] [Abstract][Full Text] [Related]
15. Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anaesthesia depth.
Bagur S; Lacroix MM; de Lavilléon G; Lefort JM; Geoffroy H; Benchenane K
PLoS Biol; 2018 Nov; 16(11):e2005458. PubMed ID: 30408025
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24 x 7 utilizing the Siesta database.
Anderer P; Gruber G; Parapatics S; Woertz M; Miazhynskaia T; Klosch G; Saletu B; Zeitlhofer J; Barbanoj MJ; Danker-Hopfe H; Himanen SL; Kemp B; Penzel T; Grozinger M; Kunz D; Rappelsberger P; Schlogl A; Dorffner G
Neuropsychobiology; 2005; 51(3):115-33. PubMed ID: 15838184
[TBL] [Abstract][Full Text] [Related]
18. The Visual Scoring of Sleep in Infants 0 to 2 Months of Age.
Grigg-Damberger MM
J Clin Sleep Med; 2016 Mar; 12(3):429-45. PubMed ID: 26951412
[TBL] [Abstract][Full Text] [Related]
19. 3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning.
Ji X; Li Y; Wen P
IEEE Trans Neural Syst Rehabil Eng; 2023; 31():3513-3523. PubMed ID: 37639413
[TBL] [Abstract][Full Text] [Related]
20. Automatic sleep-stage classification of heart rate and actigraphy data using deep and transfer learning approaches.
Ma YJX; Zschocke J; Glos M; Kluge M; Penzel T; Kantelhardt JW; Bartsch RP
Comput Biol Med; 2023 Sep; 163():107193. PubMed ID: 37421734
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
