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 *

84 related articles for article (PubMed ID: 17281141)

  • 1. EEG signal processing in anesthesia-using wavelet-based informational tools.
    Ye Z; Tian F; Weng J
    Conf Proc IEEE Eng Med Biol Soc; 2005; 2005():4127-9. PubMed ID: 17281141
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Comparison of Multiscale Permutation Entropy Measures in On-Line Depth of Anesthesia Monitoring.
    Su C; Liang Z; Li X; Li D; Li Y; Ursino M
    PLoS One; 2016; 11(10):e0164104. PubMed ID: 27723803
    [TBL] [Abstract][Full Text] [Related]  

  • 3. EEG complexity as a measure of depth of anesthesia for patients.
    Zhang XS; Roy RJ; Jensen EW
    IEEE Trans Biomed Eng; 2001 Dec; 48(12):1424-33. PubMed ID: 11759923
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals.
    Nguyen-Ky T; Wen PP; Li Y
    IEEE Trans Biomed Eng; 2013 Jun; 60(6):1488-98. PubMed ID: 23314762
    [TBL] [Abstract][Full Text] [Related]  

  • 5. [Analysis of anesthesia characteristic parameters based on the EEG signal].
    Wang F; Li X
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2015 Feb; 32(1):13-8, 31. PubMed ID: 25997259
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. [Monitoring the depth of anesthesia using a fuzzy neural network based on EEG].
    Li M; Ye ZQ
    Zhongguo Yi Liao Qi Xie Za Zhi; 2006 Jul; 30(4):253-5. PubMed ID: 17039930
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Detecting brain activity variation of rat during anesthesia by spectral entropy.
    Xu J; Zheng C; Liu X; Pei X; Jing G
    Conf Proc IEEE Eng Med Biol Soc; 2005; 2005():6985-8. PubMed ID: 17281882
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Derived fuzzy knowledge model for estimating the depth of anesthesia.
    Zhang XS; Roy RJ
    IEEE Trans Biomed Eng; 2001 Mar; 48(3):312-23. PubMed ID: 11327499
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Monitoring the depth of anesthesia using entropy features and an artificial neural network.
    Shalbaf R; Behnam H; Sleigh JW; Steyn-Ross A; Voss LJ
    J Neurosci Methods; 2013 Aug; 218(1):17-24. PubMed ID: 23567809
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia.
    Gu Y; Liang Z; Hagihira S
    Sensors (Basel); 2019 May; 19(11):. PubMed ID: 31159263
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System.
    Shalbaf A; Saffar M; Sleigh JW; Shalbaf R
    IEEE J Biomed Health Inform; 2018 May; 22(3):671-677. PubMed ID: 28574372
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Auto-mutual information function of the EEG as a measure of depth of anesthesia.
    Julitta B; Vallverdu M; Melia US; Tupaika N; Jospin M; Jensen EW; Struys MM; Vereecke HE; Caminal P
    Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():2574-7. PubMed ID: 22254867
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques.
    Nguyen-Ky T; Wen P; Li Y; Malan M
    Comput Biol Med; 2012 Jun; 42(6):680-91. PubMed ID: 22575174
    [TBL] [Abstract][Full Text] [Related]  

  • 16. EEG analysis using wavelet-based information tools.
    Rosso OA; Martin MT; Figliola A; Keller K; Plastino A
    J Neurosci Methods; 2006 Jun; 153(2):163-82. PubMed ID: 16675027
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Analyze the dynamic features of rat EEG using wavelet entropy.
    Feng Z; Chen H
    Conf Proc IEEE Eng Med Biol Soc; 2005; 2006():833-6. PubMed ID: 17282313
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Analysis of depth of anesthesia with Hilbert-Huang spectral entropy.
    Li X; Li D; Liang Z; Voss LJ; Sleigh JW
    Clin Neurophysiol; 2008 Nov; 119(11):2465-75. PubMed ID: 18812265
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM.
    Janjarasjitt S
    Med Biol Eng Comput; 2017 Oct; 55(10):1743-1761. PubMed ID: 28194648
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.