Literature DB >> 1287707

Frequency analysis of ventricular fibrillation and resuscitation success.

A J Stewart1, J D Allen, A A Adgey.   

Abstract

In 56 patients, frequency analysis of the electrocardiogram of ventricular fibrillation exhibited power spectra with a distinct dominant frequency. The greatest success for resuscitation from ventricular fibrillation is recorded when ventricular fibrillation develops after the patient comes under coronary care. Of the 41 patients in whom the onset and first 8 s of ventricular fibrillation were artefact-free the mean dominant frequency of primary ventricular fibrillation (no cardiogenic shock or cardiac failure) in 21 patients was 6.2 +/- 0.2 Hz, significantly higher than the mean dominant frequency of the first 8 s of secondary ventricular fibrillation (cardiogenic shock or heart failure) (4.0 +/- 0.2 Hz, 20 patients, p = 0.0001). In these patients the peak-to-trough amplitude (ECG) of the first 8 s of ventricular fibrillation was similar in both primary and secondary ventricular fibrillation as was the mean duration of ventricular fibrillation prior to the first DC shock. There was a significantly lower success rate for resuscitation from secondary ventricular fibrillation (6 of 20 patients) compared with resuscitation from primary ventricular fibrillation (18 of 21 patients, chi 2 17.8, p = 0.001). Of the remaining 15 patients who were collapsed between 3 and 20 min before the arrival of the mobile coronary care unit, the dominant frequency of the first 8 s of ventricular fibrillation fell with increased duration of collapse (from 5.5 Hz at 3 min to a mean of 2.1 Hz at 20 min).(ABSTRACT TRUNCATED AT 250 WORDS)

Entities:  

Mesh:

Year:  1992        PMID: 1287707

Source DB:  PubMed          Journal:  Q J Med        ISSN: 0033-5622


  8 in total

1.  Effects of blockade of fast and slow inward current channels on ventricular fibrillation in the pig heart.

Authors:  A J Stewart; J D Allen; A B Devine; A A Adgey
Journal:  Heart       Date:  1996-12       Impact factor: 5.994

2.  Spectral analysis-based risk score enables early prediction of mortality and cerebral performance in patients undergoing therapeutic hypothermia for ventricular fibrillation and comatose status.

Authors:  David Filgueiras-Rama; Conrado J Calvo; Óscar Salvador-Montañés; Rosalía Cádenas; Jose Ruiz-Cantador; Eduardo Armada; Juan Ramón Rey; J L Merino; Rafael Peinado; Nicasio Pérez-Castellano; Julián Pérez-Villacastín; Jorge G Quintanilla; Santiago Jiménez; Francisco Castells; Francisco J Chorro; J L López-Sendón; Omer Berenfeld; José Jalife; Esteban López de Sá; José Millet
Journal:  Int J Cardiol       Date:  2015-03-14       Impact factor: 4.164

3.  Linear and non-linear analysis of the surface electrocardiogram during human ventricular fibrillation shows evidence of order in the underlying mechanism.

Authors:  R H Clayton; A Murray
Journal:  Med Biol Eng Comput       Date:  1999-05       Impact factor: 2.602

4.  Prompt prediction of successful defibrillation from 1-s ventricular fibrillation waveform in patients with out-of-hospital sudden cardiac arrest.

Authors:  Hiroshi Endoh; Seiji Hida; Satomi Oohashi; Yusuke Hayashi; Hidenori Kinoshita; Tadayuki Honda
Journal:  J Anesth       Date:  2010-11-27       Impact factor: 2.078

5.  Amplitude Changes during Ventricular Fibrillation: A Mechanistic Insight.

Authors:  Jane C Caldwell; Francis L Burton; Stuart M Cobbe; Godfrey L Smith
Journal:  Front Physiol       Date:  2012-05-23       Impact factor: 4.566

6.  Slowing of Electrical Activity in Ventricular Fibrillation is Not Associated with Increased Defibrillation Energies in the Isolated Rabbit Heart.

Authors:  Jane C Caldwell; Francis L Burton; Stuart M Cobbe; Godfrey L Smith
Journal:  Front Physiol       Date:  2011-04-06       Impact factor: 4.566

7.  Resuscitation in the past, the present and the future.

Authors:  A A Jennifer Adgey
Journal:  Ulster Med J       Date:  2002-05

8.  Prediction of Cardiac Mechanical Performance From Electrical Features During Ventricular Tachyarrhythmia Simulation Using Machine Learning Algorithms.

Authors:  Da Un Jeong; Ki Moo Lim
Journal:  Front Physiol       Date:  2020-11-24       Impact factor: 4.566

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.