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 *

105 related articles for article (PubMed ID: 12051305)

  • 1. Method for ventricular fibrillation detection in the external electrocardiogram using nonlinear prediction.
    Jekova I; Dushanova J; Popivanov D
    Physiol Meas; 2002 May; 23(2):337-45. PubMed ID: 12051305
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Real time detection of ventricular fibrillation and tachycardia.
    Jekova I; Krasteva V
    Physiol Meas; 2004 Oct; 25(5):1167-78. PubMed ID: 15535182
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Detection of ventricular fibrillation and tachycardia from the surface ECG by a set of parameters acquired from four methods.
    Jekova I; Mitev P
    Physiol Meas; 2002 Nov; 23(4):629-34. PubMed ID: 12450264
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Noise sensitivity of three surface ECG fibrillation detection algorithms.
    Jekova I; Cansell A; Dotsinsky I
    Physiol Meas; 2001 May; 22(2):287-97. PubMed ID: 11411240
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG.
    Jekova I
    Physiol Meas; 2000 Nov; 21(4):429-39. PubMed ID: 11110242
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Irregularity test for very short electrocardiogram (ECG) signals as a method for predicting a successful defibrillation in patients with ventricular fibrillation.
    Jagric T; Marhl M; Stajer D; Kocjancic ST; Jagric T; Podbregar M; Perc M
    Transl Res; 2007 Mar; 149(3):145-51. PubMed ID: 17320800
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Ventricular fibrillation and tachycardia classification using a machine learning approach.
    Li Q; Rajagopalan C; Clifford GD
    IEEE Trans Biomed Eng; 2014 Jun; 61(6):1607-13. PubMed ID: 23899591
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals.
    Hajeb-Mohammadalipour S; Ahmadi M; Shahghadami R; Chon KH
    Sensors (Basel); 2018 Jun; 18(7):. PubMed ID: 29966276
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A stochastic nonlinear autoregressive algorithm reflects nonlinear dynamics of heart-rate fluctuations.
    Armoundas AA; Ju K; Iyengar N; Kanters JK; Saul PJ; Cohen RJ; Chon KH
    Ann Biomed Eng; 2002 Feb; 30(2):192-201. PubMed ID: 11962771
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [Analysis of ventricular fibrillation signals for the evaluation of defibrillation success in the treatment of ventricular fibrillation].
    Lederer W; Rheinberger K; Lischke V; Amann A
    Anasthesiol Intensivmed Notfallmed Schmerzther; 2003 Dec; 38(12):787-94. PubMed ID: 14666442
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Life-threatening ventricular arrhythmia recognition by nonlinear descriptor.
    Sun Y; Chan KL; Krishnan SM
    Biomed Eng Online; 2005 Jan; 4():6. PubMed ID: 15667654
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Detecting ventricular fibrillation by time-delay methods.
    Amann A; Tratnig R; Unterkofler K
    IEEE Trans Biomed Eng; 2007 Jan; 54(1):174-7. PubMed ID: 17260872
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Detecting ventricular tachycardia and fibrillation by complexity measure.
    Zhang XS; Zhu YS; Thakor NV; Wang ZZ
    IEEE Trans Biomed Eng; 1999 May; 46(5):548-55. PubMed ID: 10230133
    [TBL] [Abstract][Full Text] [Related]  

  • 14. [A New Algorithmic Method to Detect Ventricular Fibrillation Using Electrocardiogram Signals During Cardiopulmonary Resuscitation by Artificial Pressing].
    Wang D; Zhang G; Wan Z; Chen F; Song Z; Wang H; Gu B; Yu M; Wu T
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2016 Aug; 33(4):747-54. PubMed ID: 29714916
    [TBL] [Abstract][Full Text] [Related]  

  • 15. See through ECG technology during cardiopulmonary resuscitation to analyze rhythm and predict defibrillation outcome.
    Affatato R; Li Y; Ristagno G
    Curr Opin Crit Care; 2016 Jun; 22(3):199-205. PubMed ID: 27031917
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automated detection of ventricular fibrillation to guide cardiopulmonary resuscitation.
    Li Y; Bisera J; Tang W; Weil MH
    Crit Pathw Cardiol; 2007 Sep; 6(3):131-4. PubMed ID: 17804974
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Detection of ventricular fibrillation in the presence of cardiopulmonary resuscitation artefacts.
    Aramendi E; de Gauna SR; Irusta U; Ruiz J; Arcocha MF; Ormaetxe JM
    Resuscitation; 2007 Jan; 72(1):115-23. PubMed ID: 17088016
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Bispectral energies within electrocardiograms during ventricular fibrillation are correlated with defibrillation shock outcome.
    Patwardhan A; Wang K; Moghe S; Leonelli F
    Ann Biomed Eng; 1999; 27(2):171-9. PubMed ID: 10199693
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An external automatic device to detect ventricular fibrillation.
    Jack CM; Hunter EK; Pringle TH; Wilson JT; Anderson J; Adgey AA
    Eur Heart J; 1986 May; 7(5):404-11. PubMed ID: 3732288
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification.
    Owis MI; Abou-Zied AH; Youssef AB; Kadah YM
    IEEE Trans Biomed Eng; 2002 Jul; 49(7):733-6. PubMed ID: 12083309
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.