Literature DB >> 15582761

Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods.

James N Watson1, Nopadol Uchaipichat, Paul S Addison, Gareth R Clegg, Colin E Robertson, Trygve Eftestol, Petter A Steen.   

Abstract

We report an improved method for the estimation of shock outcome prediction based on novel wavelet transform-based time-frequency methods. Wavelet-based peak frequency, energy, mean frequency, spectral flatness and a new entropy measure were studied to predict shock outcome. Of these, the entropy measure provided optimal results with 60 +/- 6% specificity at 91 +/- 2% sensitivity achieved for the prediction of return of spontaneous circulation (ROSC). These results represent a major improvement in shock prediction in human ventricular fibrillation.

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Year:  2004        PMID: 15582761     DOI: 10.1016/j.resuscitation.2004.06.012

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  7 in total

1.  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

2.  Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning.

Authors:  Sharad Shandilya; Kevin Ward; Michael Kurz; Kayvan Najarian
Journal:  BMC Med Inform Decis Mak       Date:  2012-10-15       Impact factor: 2.796

3.  Logarithm of the absolute correlations of the ECG waveform estimates duration of ventricular fibrillation and predicts successful defibrillation.

Authors:  Lawrence D Sherman; Thomas D Rea; James D Waters; James J Menegazzi; Clifton W Callaway
Journal:  Resuscitation       Date:  2008-07-01       Impact factor: 5.262

4.  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

5.  Reduction of CPR artifacts in the ventricular fibrillation ECG by coherent line removal.

Authors:  Anton Amann; Andreas Klotz; Thomas Niederklapfer; Alexander Kupferthaler; Tobias Werther; Marcus Granegger; Wolfgang Lederer; Michael Baubin; Werner Lingnau
Journal:  Biomed Eng Online       Date:  2010-01-06       Impact factor: 2.819

6.  Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests.

Authors:  Mi He; Yushun Gong; Yongqin Li; Tommaso Mauri; Francesca Fumagalli; Marcella Bozzola; Giancarlo Cesana; Roberto Latini; Antonio Pesenti; Giuseppe Ristagno
Journal:  Crit Care       Date:  2015-12-10       Impact factor: 9.097

7.  Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes.

Authors:  Sharad Shandilya; Michael C Kurz; Kevin R Ward; Kayvan Najarian
Journal:  PLoS One       Date:  2016-01-07       Impact factor: 3.240

  7 in total

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