207 related articles for article (PubMed ID: 17946514)
1. EOG and EMG: two important switches in automatic sleep stage classification.
Estrada E; Nazeran H; Barragan J; Burk JR; Lucas EA; Behbehani K
Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():2458-61. PubMed ID: 17946514
[TBL] [Abstract][Full Text] [Related]
2. Automatic REM sleep detection associated with idiopathic rem sleep Behavior Disorder.
Kempfner J; Sorensen GL; Sorensen HB; Jennum P
Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():6063-6. PubMed ID: 22255722
[TBL] [Abstract][Full Text] [Related]
3. Itakura Distance: A Useful Similarity Measure between EEG and EOG Signals in Computer-aided Classification of Sleep Stages.
Estrada E; Nava P; Nazeran H; Behbehani K; Burk J; Lucas E
Conf Proc IEEE Eng Med Biol Soc; 2005; 2005():1189-92. PubMed ID: 17282405
[TBL] [Abstract][Full Text] [Related]
4. Adaptive automatic sleep stage classification under covariate shift.
Khalighi S; Sousa T; Nunes U
Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():2259-62. PubMed ID: 23366373
[TBL] [Abstract][Full Text] [Related]
5. An improved algorithm for the automatic detection and characterization of slow eye movements.
Cona F; Pizza F; Provini F; Magosso E
Med Eng Phys; 2014 Jul; 36(7):954-61. PubMed ID: 24768562
[TBL] [Abstract][Full Text] [Related]
6. Blind source separation for ambulatory sleep recording.
Porée F; Kachenoura A; Gauvrit H; Morvan C; Carrault G; Senhadji L
IEEE Trans Inf Technol Biomed; 2006 Apr; 10(2):293-301. PubMed ID: 16617618
[TBL] [Abstract][Full Text] [Related]
7. Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.
Ebrahimi F; Mikaeili M; Estrada E; Nazeran H
Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():1151-4. PubMed ID: 19162868
[TBL] [Abstract][Full Text] [Related]
8. Multivariate analysis of full-term neonatal polysomnographic data.
Gerla V; Paul K; Lhotska L; Krajca V
IEEE Trans Inf Technol Biomed; 2009 Jan; 13(1):104-10. PubMed ID: 19129029
[TBL] [Abstract][Full Text] [Related]
9. Automatic sleep stage classification using two-channel electro-oculography.
Virkkala J; Hasan J; Värri A; Himanen SL; Müller K
J Neurosci Methods; 2007 Oct; 166(1):109-15. PubMed ID: 17681382
[TBL] [Abstract][Full Text] [Related]
10. A rule-based automatic sleep staging method.
Liang SF; Kuo CE; Hu YH; Cheng YS
Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():6067-70. PubMed ID: 22255723
[TBL] [Abstract][Full Text] [Related]
11. Sleep apnea classification based on respiration signals by using ensemble methods.
Avcı C; Akbaş A
Biomed Mater Eng; 2015; 26 Suppl 1():S1703-10. PubMed ID: 26405937
[TBL] [Abstract][Full Text] [Related]
12. Proof of concept: Screening for REM sleep behaviour disorder with a minimal set of sensors.
Cooray N; Andreotti F; Lo C; Symmonds M; Hu MTM; De Vos M
Clin Neurophysiol; 2021 Apr; 132(4):904-913. PubMed ID: 33636605
[TBL] [Abstract][Full Text] [Related]
13. Assessment of Itakura Distance as a valuable feature for computer-aided classification of sleep stages.
Ebrahimi F; Mikaili M; Estrada E; Nazeran H
Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():3300-3. PubMed ID: 18002701
[TBL] [Abstract][Full Text] [Related]
14. Sleep state scoring in infants from respiratory and activity measurements.
Sazonova NA; Sazonov EE; Tan B; Schuckers SA
Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():2462-5. PubMed ID: 17946515
[TBL] [Abstract][Full Text] [Related]
15. Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.
Christensen JA; Zoetmulder M; Koch H; Frandsen R; Arvastson L; Christensen SR; Jennum P; Sorensen HB
J Neurosci Methods; 2014 Sep; 235():262-76. PubMed ID: 25088694
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Cardiorespiratory-based sleep staging in subjects with obstructive sleep apnea.
Redmond SJ; Heneghan C
IEEE Trans Biomed Eng; 2006 Mar; 53(3):485-96. PubMed ID: 16532775
[TBL] [Abstract][Full Text] [Related]
18. A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals.
Sun C; Chen C; Fan J; Li W; Zhang Y; Chen W
J Neural Eng; 2019 Oct; 16(6):066020. PubMed ID: 31394522
[TBL] [Abstract][Full Text] [Related]
19. Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.
Jiang D; Ma Y; Wang Y
Comput Methods Programs Biomed; 2019 Sep; 178():19-30. PubMed ID: 31416548
[TBL] [Abstract][Full Text] [Related]
20. Sleep stage classification with low complexity and low bit rate.
Virkkala J; Värri A; Hasan J; Himanen SL; Müller K
Annu Int Conf IEEE Eng Med Biol Soc; 2009; 2009():2506-9. PubMed ID: 19965209
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
