Literature DB >> 15535183

Defibrillation shock success estimation by a set of six parameters derived from the electrocardiogram.

Irena Jekova1, François Mougeolle, Aude Valance.   

Abstract

It is well known that in some cases defibrillator shocks cannot terminate ventricular fibrillation (VF). Repeated failed shocks often may worsen subsequent response to therapy. This study assesses the ability of six parameters derived from the surface electrocardiogram (ECG) to predict defibrillation shock outcome. Using stepwise discriminant analysis, we obtained several discriminant functions, yielding different combinations of sensitivity and specificity for detection of pre-shock ECG segments corresponding to successful versus unsuccessful shocks. The study was performed consecutively for 3, 4 and 5 s ECG time intervals. The prediction accuracy of 72.3% (61.8% sensitivity and 79.6% specificity) with five parameters and 3 s VF segment analysis prior to defibrillation shock could be considered acceptable for possible practical application in automatic external defibrillators.

Entities:  

Mesh:

Year:  2004        PMID: 15535183     DOI: 10.1088/0967-3334/25/5/008

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  4 in total

1.  Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest.

Authors:  Jason Coult; Jennifer Blackwood; Lawrence Sherman; Thomas D Rea; Peter J Kudenchuk; Heemun Kwok
Journal:  Circ Arrhythm Electrophysiol       Date:  2019-01

2.  Frequency Variation of Ventricular Fibrillation May Help Predict Successful Defibrillation in a Rat Model of Cardiac Arrest.

Authors:  Wei-Ting Chen; Min-Shan Tsai; Shang-Ho Tsai; Yu-Chen Fang Jiang; Teck-Jin Yang; Chien-Hua Huang; Wei-Tien Chang; Wen-Jone Chen
Journal:  J Acute Med       Date:  2019-06-01

3.  Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest.

Authors:  Beatriz Chicote; Unai Irusta; Elisabete Aramendi; Raúl Alcaraz; José Joaquín Rieta; Iraia Isasi; Daniel Alonso; María Del Mar Baqueriza; Karlos Ibarguren
Journal:  Entropy (Basel)       Date:  2018-08-09       Impact factor: 2.524

4.  Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.

Authors:  Carlos Figuera; Unai Irusta; Eduardo Morgado; Elisabete Aramendi; Unai Ayala; Lars Wik; Jo Kramer-Johansen; Trygve Eftestøl; Felipe Alonso-Atienza
Journal:  PLoS One       Date:  2016-07-21       Impact factor: 3.240

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.