BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

150 related articles for article (PubMed ID: 37289607)

  • 41. Single-Channel Ecg-Based Sleep Stage Classification With End-To-End Trainable Deep Neural Networks.
    Choi I; Sung W
    Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083334
    [TBL] [Abstract][Full Text] [Related]  

  • 42. 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]  

  • 43. STDP-based adaptive graph convolutional networks for automatic sleep staging.
    Zhao Y; Lin X; Zhang Z; Wang X; He X; Yang L
    Front Neurosci; 2023; 17():1158246. PubMed ID: 37152593
    [TBL] [Abstract][Full Text] [Related]  

  • 44. 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]  

  • 45. MaskSleepNet: A Cross-Modality Adaptation Neural Network for Heterogeneous Signals Processing in Sleep Staging.
    Zhu H; Zhou W; Fu C; Wu Y; Shen N; Shu F; Yu H; Chen W; Chen C
    IEEE J Biomed Health Inform; 2023 May; 27(5):2353-2364. PubMed ID: 37028323
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Reliable automatic sleep stage classification based on hybrid intelligence.
    Shao Y; Huang B; Du L; Wang P; Li Z; Liu Z; Zhou L; Song Y; Chen X; Fang Z
    Comput Biol Med; 2024 May; 173():108314. PubMed ID: 38513392
    [TBL] [Abstract][Full Text] [Related]  

  • 47. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.
    Hassan AR; Bhuiyan MIH
    Comput Methods Programs Biomed; 2017 Mar; 140():201-210. PubMed ID: 28254077
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Classification of bruxism based on time-frequency and nonlinear features of single channel EEG.
    Wang C; Verma AK; Guragain B; Xiong X; Liu C
    BMC Oral Health; 2024 Jan; 24(1):81. PubMed ID: 38221633
    [TBL] [Abstract][Full Text] [Related]  

  • 50. MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG.
    Yu R; Zhou Z; Wu S; Gao X; Bin G
    J Neural Eng; 2022 Dec; 19(6):. PubMed ID: 36379059
    [No Abstract]   [Full Text] [Related]  

  • 51. Automatic sleep staging using state machine-controlled decision trees.
    Imtiaz SA; Rodriguez-Villegas E
    Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():378-81. PubMed ID: 26736278
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Combination of Expert Knowledge and a Genetic Fuzzy Inference System for Automatic Sleep Staging.
    Liang SF; Kuo CE; Shaw FZ; Chen YH; Hsu CH; Chen JY
    IEEE Trans Biomed Eng; 2016 Oct; 63(10):2108-18. PubMed ID: 26700856
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals.
    Ebrahimi F; Setarehdan SK; Ayala-Moyeda J; Nazeran H
    Comput Methods Programs Biomed; 2013 Oct; 112(1):47-57. PubMed ID: 23895941
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Sleep stage and obstructive apneaic epoch classification using single-lead ECG.
    Yilmaz B; Asyali MH; Arikan E; Yetkin S; Ozgen F
    Biomed Eng Online; 2010 Aug; 9():39. PubMed ID: 20723232
    [TBL] [Abstract][Full Text] [Related]  

  • 55. A Residual Based Attention Model for EEG Based Sleep Staging.
    Qu W; Wang Z; Hong H; Chi Z; Feng DD; Grunstein R; Gordon C
    IEEE J Biomed Health Inform; 2020 Oct; 24(10):2833-2843. PubMed ID: 32149700
    [TBL] [Abstract][Full Text] [Related]  

  • 56. A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.
    Dimitriadis SI; Salis C; Linden D
    Clin Neurophysiol; 2018 Apr; 129(4):815-828. PubMed ID: 29477981
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Comparative analysis of different characteristics of automatic sleep stages.
    Zhao D; Wang Y; Wang Q; Wang X
    Comput Methods Programs Biomed; 2019 Jul; 175():53-72. PubMed ID: 31104715
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Sleep staging from single-channel EEG with multi-scale feature and contextual information.
    Chen K; Zhang C; Ma J; Wang G; Zhang J
    Sleep Breath; 2019 Dec; 23(4):1159-1167. PubMed ID: 30863994
    [TBL] [Abstract][Full Text] [Related]  

  • 59. 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]  

  • 60. Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.
    Zhang J; Wu Y
    Biomed Tech (Berl); 2018 Mar; 63(2):177-190. PubMed ID: 28222011
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 8.