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 *

164 related articles for article (PubMed ID: 32746339)

  • 1. A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.
    Park Y; Han SH; Byun W; Kim JH; Lee HC; Kim SJ
    IEEE Trans Biomed Circuits Syst; 2020 Aug; 14(4):825-837. PubMed ID: 32746339
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A TDM-Based 16-Channel AFE ASIC With Enhanced System-Level CMRR for Wearable EEG Recording With Dry Electrodes.
    Tang T; Goh WL; Yao L; Gao Y
    IEEE Trans Biomed Circuits Syst; 2020 Jun; 14(3):516-524. PubMed ID: 32167908
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition.
    Madanu R; Rahman F; Abbod MF; Fan SZ; Shieh JS
    Math Biosci Eng; 2021 Jun; 18(5):5047-5068. PubMed ID: 34517477
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A 10.13µJ/Classification 2-Channel Deep Neural Network Based SoC for Negative Emotion Outburst Detection of Autistic Children.
    Aslam AR; Altaf MAB
    IEEE Trans Biomed Circuits Syst; 2021 Oct; 15(5):1039-1052. PubMed ID: 34543203
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks.
    Liu Q; Chen YF; Fan SZ; Abbod MF; Shieh JS
    Comput Math Methods Med; 2015; 2015():232381. PubMed ID: 26491464
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Combinatorial Deep Learning Structure for Precise Depth of Anesthesia Estimation From EEG Signals.
    Afshar S; Boostani R; Sanei S
    IEEE J Biomed Health Inform; 2021 Sep; 25(9):3408-3415. PubMed ID: 33760743
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Analysis of EEG to quantify depth of anesthesia using Hidden Markov Model.
    Kim J; Hyub H; Yoon SZ; Choi HJ; Kim KM; Park SH
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():4575-8. PubMed ID: 25571010
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Real-time depth of anaesthesia assessment using strong analytical signal transform technique.
    Palendeng ME; Wen P; Li Y
    Australas Phys Eng Sci Med; 2014 Dec; 37(4):723-30. PubMed ID: 25412884
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network.
    Shi M; Huang Z; Xiao G; Xu B; Ren Q; Zhao H
    Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679805
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Portable brain-computer interface based on novel convolutional neural network.
    Zhang Y; Zhang X; Sun H; Fan Z; Zhong X
    Comput Biol Med; 2019 Apr; 107():248-256. PubMed ID: 30856388
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A 16-Channel CMOS Chopper-Stabilized Analog Front-End ECoG Acquisition Circuit for a Closed-Loop Epileptic Seizure Control System.
    Wu CY; Cheng CH; Chen ZX
    IEEE Trans Biomed Circuits Syst; 2018 Jun; 12(3):543-553. PubMed ID: 29877818
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. Fully Integrated Biopotential Acquisition Analog Front-End IC.
    Song H; Park Y; Kim H; Ko H
    Sensors (Basel); 2015 Sep; 15(10):25139-56. PubMed ID: 26437404
    [TBL] [Abstract][Full Text] [Related]  

  • 18. EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia.
    Ortolani O; Conti A; Di Filippo A; Adembri C; Moraldi E; Evangelisti A; Maggini M; Roberts SJ
    Br J Anaesth; 2002 May; 88(5):644-8. PubMed ID: 12067000
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks.
    Sadrawi M; Fan SZ; Abbod MF; Jen KK; Shieh JS
    Biomed Res Int; 2015; 2015():536863. PubMed ID: 26568957
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Improving the anesthetic process by a fuzzy rule based medical decision system.
    Mendez JA; Leon A; Marrero A; Gonzalez-Cava JM; Reboso JA; Estevez JI; Gomez-Gonzalez JF
    Artif Intell Med; 2018 Jan; 84():159-170. PubMed ID: 29310966
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.