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 *

153 related articles for article (PubMed ID: 18041276)

  • 21. Classification of Chaotic Squeak and Rattle Vibrations by CNN Using Recurrence Pattern.
    Nam J; Kang J
    Sensors (Basel); 2021 Dec; 21(23):. PubMed ID: 34884057
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Neural network adaptive output feedback control for intensive care unit sedation and intraoperative anesthesia.
    Haddad WM; Bailey JM; Hayakawa T; Hovakimyan N
    IEEE Trans Neural Netw; 2007 Jul; 18(4):1049-66. PubMed ID: 17668661
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals.
    Acharya UR; Sree SV; Alvin AP; Yanti R; Suri JS
    Int J Neural Syst; 2012 Apr; 22(2):1250002. PubMed ID: 23627588
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Classifying depth of anesthesia using EEG features, a comparison.
    Esmaeili V; Shamsollahi MB; Arefian NM; Assareh A
    Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():4106-9. PubMed ID: 18002905
    [TBL] [Abstract][Full Text] [Related]  

  • 25. The Raw and Processed Electroencephalogram as a Monitoring and Diagnostic Tool.
    Montupil J; Defresne A; Bonhomme V
    J Cardiothorac Vasc Anesth; 2019 Aug; 33 Suppl 1():S3-S10. PubMed ID: 31279351
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Using EEG to monitor anesthesia drug effects during surgery.
    Jameson LC; Sloan TB
    J Clin Monit Comput; 2006 Dec; 20(6):445-72. PubMed ID: 17103250
    [TBL] [Abstract][Full Text] [Related]  

  • 27. EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.
    Liu Q; Chen YF; Fan SZ; Abbod MF; Shieh JS
    Med Biol Eng Comput; 2017 Aug; 55(8):1435-1450. PubMed ID: 27995430
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Processed electroencephalogram in depth of anesthesia monitoring.
    Palanca BJ; Mashour GA; Avidan MS
    Curr Opin Anaesthesiol; 2009 Oct; 22(5):553-9. PubMed ID: 19652597
    [TBL] [Abstract][Full Text] [Related]  

  • 29. EEG non-linear feature extraction using correlation dimension and Hurst exponent.
    Geng S; Zhou W; Yuan Q; Cai D; Zeng Y
    Neurol Res; 2011 Nov; 33(9):908-12. PubMed ID: 22080990
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia.
    Tacke M; Kochs EF; Mueller M; Kramer S; Jordan D; Schneider G
    PLoS One; 2020; 15(8):e0238249. PubMed ID: 32845935
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Design of a recognition system to predict movement during anesthesia.
    Sharma A; Roy RJ
    IEEE Trans Biomed Eng; 1997 Jun; 44(6):505-11. PubMed ID: 9151484
    [TBL] [Abstract][Full Text] [Related]  

  • 32. A study of electroencephalographic descriptors and end-tidal concentration in estimating depth of anesthesia.
    Muthuswamy J; Sharma A
    J Clin Monit; 1996 Sep; 12(5):353-64. PubMed ID: 8934342
    [TBL] [Abstract][Full Text] [Related]  

  • 33. A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals.
    Shayegh F; Sadri S; Amirfattahi R; Ansari-Asl K
    Comput Methods Programs Biomed; 2014; 113(1):323-37. PubMed ID: 24113422
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model.
    Xiaoxiao X; Bin L; Ramkumar S; Saravanan S; Balaji MSP; Dhanasekaran S; Thimmiaraja J
    Artif Intell Med; 2020 Jan; 102():101766. PubMed ID: 31980103
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Epileptic EEG classification based on extreme learning machine and nonlinear features.
    Yuan Q; Zhou W; Li S; Cai D
    Epilepsy Res; 2011 Sep; 96(1-2):29-38. PubMed ID: 21616643
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Improved spectrum analysis in EEG for measure of depth of anesthesia based on phase-rectified signal averaging.
    Liu Q; Chen YF; Fan SZ; Abbod MF; Shieh JS
    Physiol Meas; 2017 Feb; 38(2):116-138. PubMed ID: 28033111
    [TBL] [Abstract][Full Text] [Related]  

  • 37. [Automatic analysis of electroencephalogram: what is its value in the year 2000 for monitoring anesthesia depth?].
    Billard V; Constant I
    Ann Fr Anesth Reanim; 2001 Nov; 20(9):763-85. PubMed ID: 11759318
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Approximate entropy-based epileptic EEG detection using artificial neural networks.
    Srinivasan V; Eswaran C; Sriraam N
    IEEE Trans Inf Technol Biomed; 2007 May; 11(3):288-95. PubMed ID: 17521078
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Design and Implementation of a Machine Learning Based EEG Processor for Accurate Estimation of Depth of Anesthesia.
    Saadeh W; Khan FH; Altaf MAB
    IEEE Trans Biomed Circuits Syst; 2019 Aug; 13(4):658-669. PubMed ID: 31180871
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.
    Jiang GJ; Fan SZ; Abbod MF; Huang HH; Lan JY; Tsai FF; Chang HC; Yang YW; Chuang FL; Chiu YF; Jen KK; Wu JF; Shieh JS
    Biomed Res Int; 2015; 2015():343478. PubMed ID: 25738152
    [TBL] [Abstract][Full Text] [Related]  

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