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.
Pubmed for Handhelds
PUBMED FOR HANDHELDS
Journal Abstract Search
301 related items for PubMed ID: 32030843
1. A novel sleep stage scoring system: Combining expert-based features with the generalized linear model. Gunnarsdottir KM, Gamaldo C, Salas RM, Ewen JB, Allen RP, Hu K, Sarma SV. J Sleep Res; 2020 Oct; 29(5):e12991. PubMed ID: 32030843 [Abstract] [Full Text] [Related]
2. A Novel Sleep Stage Scoring System: Combining Expert-Based Rules with a Decision Tree Classifier. Gunnarsdottir KM, Gamaldo CE, Salas RME, Ewen JB, Allen RP, Sarma SV. Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():3240-3243. PubMed ID: 30441082 [Abstract] [Full Text] [Related]
3. Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters. Stepnowsky C, Levendowski D, Popovic D, Ayappa I, Rapoport DM. Sleep Med; 2013 Nov; 14(11):1199-207. PubMed ID: 24047533 [Abstract] [Full Text] [Related]
4. 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 11; 19(12):. PubMed ID: 35742426 [Abstract] [Full Text] [Related]
5. Automatic sleep stage classification based on a two-channel electrooculogram and one-channel electromyogram. Li Y, Xu Z, Zhang Y, Cao Z, Chen H. Physiol Meas; 2022 Jul 25; 43(7):. PubMed ID: 35487205 [Abstract] [Full Text] [Related]
6. Performance evaluation of the open-source Yet Another Spindle Algorithm sleep staging algorithm against gold standard manual evaluation of polysomnographic records in adolescence. Benedetti D, Frati E, Kiss O, Yuksel D, Faraguna U, Hasler BP, Franzen PL, Clark DB, Baker FC, de Zambotti M. Sleep Health; 2023 Dec 25; 9(6):910-924. PubMed ID: 37709595 [Abstract] [Full Text] [Related]
7. Automatic identification of sleep and wakefulness using single-channel EEG and respiratory polygraphy signals for the diagnosis of obstructive sleep apnea. Sabil A, Vanbuis J, Baffet G, Feuilloy M, Le Vaillant M, Meslier N, Gagnadoux F. J Sleep Res; 2019 Apr 25; 28(2):e12795. PubMed ID: 30478923 [Abstract] [Full Text] [Related]
8. 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 21; 42(11):. PubMed ID: 31289828 [Abstract] [Full Text] [Related]
9. 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 Oct 21; 51(3):115-33. PubMed ID: 15838184 [Abstract] [Full Text] [Related]
10. Automatic sleep scoring with LSTM networks: impact of time granularity and input signals. Tăuțan AM, Rossi AC, Ionescu B. Biomed Tech (Berl); 2022 Aug 26; 67(4):267-281. PubMed ID: 35660133 [Abstract] [Full Text] [Related]
11. The Visual Scoring of Sleep in Infants 0 to 2 Months of Age. Grigg-Damberger MM. J Clin Sleep Med; 2016 Mar 26; 12(3):429-45. PubMed ID: 26951412 [Abstract] [Full Text] [Related]
12. Development of a human-computer collaborative sleep scoring system for polysomnography recordings. Liang SF, Shih YH, Chen PY, Kuo CE. PLoS One; 2019 Mar 26; 14(7):e0218948. PubMed ID: 31291270 [Abstract] [Full Text] [Related]
13. Process and outcome for international reliability in sleep scoring. Zhang X, Dong X, Kantelhardt JW, Li J, Zhao L, Garcia C, Glos M, Penzel T, Han F. Sleep Breath; 2015 Mar 26; 19(1):191-5. PubMed ID: 24801137 [Abstract] [Full Text] [Related]
14. Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults. Fonseca P, Weysen T, Goelema MS, Møst EIS, Radha M, Lunsingh Scheurleer C, van den Heuvel L, Aarts RM. Sleep; 2017 Jul 01; 40(7):. PubMed ID: 28838130 [Abstract] [Full Text] [Related]
15. Automatic scoring of sleep stages and cortical arousals using two electrodes on the forehead: validation in healthy adults. Popovic D, Khoo M, Westbrook P. J Sleep Res; 2014 Apr 01; 23(2):211-21. PubMed ID: 24313630 [Abstract] [Full Text] [Related]
16. Interrater agreement between American and Chinese sleep centers according to the 2014 AASM standard. Deng S, Zhang X, Zhang Y, Gao H, Chang EI, Fan Y, Xu Y. Sleep Breath; 2019 Jun 01; 23(2):719-728. PubMed ID: 30783913 [Abstract] [Full Text] [Related]
17. Interrater reliability of sleep stage scoring: a meta-analysis. Lee YJ, Lee JY, Cho JH, Choi JH. J Clin Sleep Med; 2022 Jan 01; 18(1):193-202. PubMed ID: 34310277 [Abstract] [Full Text] [Related]
18. Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals. Sharma M, Tiwari J, Acharya UR. Int J Environ Res Public Health; 2021 Mar 17; 18(6):. PubMed ID: 33802799 [Abstract] [Full Text] [Related]
19. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines. Lajnef T, Chaibi S, Ruby P, Aguera PE, Eichenlaub JB, Samet M, Kachouri A, Jerbi K. J Neurosci Methods; 2015 Jul 30; 250():94-105. PubMed ID: 25629798 [Abstract] [Full Text] [Related]
20. Automatic sleep staging using multi-dimensional feature extraction and multi-kernel fuzzy support vector machine. Zhang Y, Zhang X, Liu W, Luo Y, Yu E, Zou K, Liu X. J Healthc Eng; 2014 Jul 30; 5(4):505-20. PubMed ID: 25516130 [Abstract] [Full Text] [Related] Page: [Next] [New Search]