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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

109 related articles for article (PubMed ID: 64352)

  • 1. Adaptive segmentation of EEG records: a new approach to automatic EEG analysis.
    Praetorius HM; Bodenstein G; Creutzfeldt OD
    Electroencephalogr Clin Neurophysiol; 1977 Jan; 42(1):84-94. PubMed ID: 64352
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Automatic adaptive segmentation of clinical EEGs.
    Barlow JS; Creutzfeldt OD; Michael D; Houchin J; Epelbaum H
    Electroencephalogr Clin Neurophysiol; 1981 May; 51(5):512-25. PubMed ID: 6165551
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Automatic classification of EEG segments and extraction of representative ones by dynamic clusters method.
    Krajca V
    Act Nerv Super (Praha); 1984 Jun; 26(2):118-28. PubMed ID: 6475475
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Computer characterization of tracé alternant and REM sleep patterns in the neonatal EEG by adaptive segmentation--an exploratory study.
    Barlow JS
    Electroencephalogr Clin Neurophysiol; 1985 Feb; 60(2):163-73. PubMed ID: 2578369
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Automatic EEG analysis during long-term monitoring in the ICU.
    Agarwal R; Gotman J; Flanagan D; Rosenblatt B
    Electroencephalogr Clin Neurophysiol; 1998 Jul; 107(1):44-58. PubMed ID: 9743272
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automatic detection of transient EEG events during sleep can be improved using a multi-channel approach.
    Saccomandi F; Priano L; Mauro A; Nerino R; Guiot C
    Clin Neurophysiol; 2008 Apr; 119(4):959-67. PubMed ID: 18282740
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Computerized EEG pattern classification by adaptive segmentation and probability-density-function classification. Description of the method.
    Bodenstein G; Schneider W; Malsburg CV
    Comput Biol Med; 1985; 15(5):297-313. PubMed ID: 4042635
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Segmentation of EEG during sleep using time-varying autoregressive modeling.
    Amir N; Gath I
    Biol Cybern; 1989; 61(6):447-55. PubMed ID: 2790073
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Time-frequency evaluation of segmentation methods for neonatal EEG signals.
    Wong L; Abdulla W
    Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():1303-6. PubMed ID: 17945631
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [Automatic detection of transient potentials in the EEG].
    Süss M; Rabending G; Heydenreich F
    Psychiatr Neurol Med Psychol (Leipz); 1987 Feb; 39(2):98-102. PubMed ID: 3108916
    [TBL] [Abstract][Full Text] [Related]  

  • 11. [Development of a criterion for the automatic detection of sleep spindles in the infant].
    Delapierre G; Dreano E; Samson-Dollfus D; Senant J; Ménard JF; De Brucq D
    Rev Electroencephalogr Neurophysiol Clin; 1986 Oct; 16(3):311-6. PubMed ID: 3809693
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A new segmentation method of electroencephalograms by use of Akaike's information criterion.
    Inouye T; Toi S; Matsumoto Y
    Brain Res Cogn Brain Res; 1995 Dec; 3(1):33-40. PubMed ID: 8719020
    [TBL] [Abstract][Full Text] [Related]  

  • 13. EEG segmentation for improving automatic CAP detection.
    Mariani S; Grassi A; Mendez MO; Milioli G; Parrino L; Terzano MG; Bianchi AM
    Clin Neurophysiol; 2013 Sep; 124(9):1815-23. PubMed ID: 23643311
    [TBL] [Abstract][Full Text] [Related]  

  • 14. EEG representation using multi-instance framework on the manifold of symmetric positive definite matrices.
    Sadatnejad K; Rahmati M; Rostami R; Kazemi R; Ghidary SS; Müller A; Alimardani F
    J Neural Eng; 2019 Jun; 16(3):036016. PubMed ID: 30844780
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Computerized EEG pattern classification by adaptive segmentation and probability density function classification. Clinical evaluation.
    Creutzfeldt OD; Bodenstein G; Barlow JS
    Electroencephalogr Clin Neurophysiol; 1985 May; 60(5):373-93. PubMed ID: 2580689
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques: a comparative review.
    Barlow JS
    J Clin Neurophysiol; 1985 Jul; 2(3):267-304. PubMed ID: 3916847
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Automatic classification of background EEG activity in healthy and sick neonates.
    Löfhede J; Thordstein M; Löfgren N; Flisberg A; Rosa-Zurera M; Kjellmer I; Lindecrantz K
    J Neural Eng; 2010 Feb; 7(1):16007. PubMed ID: 20075506
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automated detection of neonate EEG sleep stages.
    Piryatinska A; Terdik G; Woyczynski WA; Loparo KA; Scher MS; Zlotnik A
    Comput Methods Programs Biomed; 2009 Jul; 95(1):31-46. PubMed ID: 19233504
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automated detection of EEG artifacts during sleep: preprocessing for all-night spectral analysis.
    Ktonas PY; Osorio PL; Everett RL
    Electroencephalogr Clin Neurophysiol; 1979 Apr; 46(4):382-8. PubMed ID: 85534
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [Partial autocorrelation function as a suitable description of basic EEG activity for use in classification procedures].
    Gundel A
    EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb; 1983 Sep; 14(3):121-7. PubMed ID: 6414800
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.