144 related articles for article (PubMed ID: 38063156)
61. The use of two-channel electro-oculography in automatic detection of unintentional sleep onset.
Virkkala J; Hasan J; Värri A; Himanen SL; Härmä M
J Neurosci Methods; 2007 Jun; 163(1):137-44. PubMed ID: 17376536
[TBL] [Abstract][Full Text] [Related]
62. Automated sleep staging in rat with a standard spreadsheet.
Costa-Miserachs D; Portell-Cortés I; Torras-Garcia M; Morgado-Bernal I
J Neurosci Methods; 2003 Nov; 130(1):93-101. PubMed ID: 14583408
[TBL] [Abstract][Full Text] [Related]
63. Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification.
Jeong J; Yoon W; Lee JG; Kim D; Woo Y; Kim DK; Shin HW
Sleep; 2023 Dec; 46(12):. PubMed ID: 37703391
[TBL] [Abstract][Full Text] [Related]
64. Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.
Korkalainen H; Aakko J; Duce B; Kainulainen S; Leino A; Nikkonen S; Afara IO; Myllymaa S; Töyräs J; Leppänen T
Sleep; 2020 Nov; 43(11):. PubMed ID: 32436942
[TBL] [Abstract][Full Text] [Related]
65. Performance of a Portable Sleep Monitoring Device in Individuals with High Versus Low Sleep Efficiency.
Markwald RR; Bessman SC; Reini SA; Drummond SP
J Clin Sleep Med; 2016 Jan; 12(1):95-103. PubMed ID: 26285110
[TBL] [Abstract][Full Text] [Related]
66. Hearables: Automatic Sleep Scoring from Single-Channel Ear-EEG in Older Adults.
Hammour G; Atzori G; Monica CD; Ravindran KKG; Revell V; Dijk DJ; Mandic DP
Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083340
[TBL] [Abstract][Full Text] [Related]
67. 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
[TBL] [Abstract][Full Text] [Related]
68. Comparison of manual sleep staging with automated neural network-based analysis in clinical practice.
Caffarel J; Gibson GJ; Harrison JP; Griffiths CJ; Drinnan MJ
Med Biol Eng Comput; 2006 Mar; 44(1-2):105-10. PubMed ID: 16929927
[TBL] [Abstract][Full Text] [Related]
69. The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging.
Arnal PJ; Thorey V; Debellemaniere E; Ballard ME; Bou Hernandez A; Guillot A; Jourde H; Harris M; Guillard M; Van Beers P; Chennaoui M; Sauvet F
Sleep; 2020 Nov; 43(11):. PubMed ID: 32433768
[TBL] [Abstract][Full Text] [Related]
70. Attention-Based LSTM for Non-Contact Sleep Stage Classification Using IR-UWB Radar.
Kwon HB; Choi SH; Lee D; Son D; Yoon H; Lee MH; Lee YJ; Park KS
IEEE J Biomed Health Inform; 2021 Oct; 25(10):3844-3853. PubMed ID: 33848253
[TBL] [Abstract][Full Text] [Related]
71. Differentiating between light and deep sleep stages using an ambulatory device based on peripheral arterial tonometry.
Bresler M; Sheffy K; Pillar G; Preiszler M; Herscovici S
Physiol Meas; 2008 May; 29(5):571-84. PubMed ID: 18460762
[TBL] [Abstract][Full Text] [Related]
72. Computerized scoring of abnormal human sleep: a validation.
Sangal RB; Semery JP; Belisle CL
Clin Electroencephalogr; 1997 Apr; 28(2):64-7. PubMed ID: 9137869
[TBL] [Abstract][Full Text] [Related]
73. A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning.
Olesen AN; Christensen JA; Sorensen HB; Jennum PJ
Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():3769-3772. PubMed ID: 28269109
[TBL] [Abstract][Full Text] [Related]
74. Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography.
Kawamura A; Yoshiike T; Matsuo M; Kadotani H; Oike Y; Kawasaki M; Kurumai Y; Nagao K; Takami M; Yamada N; Kuriyama K
Sleep Biol Rhythms; 2023 Jan; 21(1):85-95. PubMed ID: 38468906
[TBL] [Abstract][Full Text] [Related]
75. Non-constraining sleep/wake monitoring system using bed actigraphy.
Choi BH; Seo JW; Choi JM; Shin HB; Lee JY; Jeong DU; Park KS
Med Biol Eng Comput; 2007 Jan; 45(1):107-14. PubMed ID: 17146691
[TBL] [Abstract][Full Text] [Related]
76. 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; 250():94-105. PubMed ID: 25629798
[TBL] [Abstract][Full Text] [Related]
77. 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]
78. Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance.
van Gilst MM; Wulterkens BM; Fonseca P; Radha M; Ross M; Moreau A; Cerny A; Anderer P; Long X; van Dijk JP; Overeem S
BMC Res Notes; 2020 Nov; 13(1):513. PubMed ID: 33168051
[TBL] [Abstract][Full Text] [Related]
79. Automated NREM sleep staging using the electro-oculogram: a pilot study.
Garcia-Molina G; Abtahi F; Lagares-Lemos M
Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():2255-8. PubMed ID: 23366372
[TBL] [Abstract][Full Text] [Related]
80. Slow-Wave Sleep Estimation for Healthy Subjects and OSA Patients Using R-R Intervals.
Yoon H; Hwang SH; Choi JW; Lee YJ; Jeong DU; Park KS
IEEE J Biomed Health Inform; 2018 Jan; 22(1):119-128. PubMed ID: 28600268
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]