Fuzzy and Sample Entropies As Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings During out of Hospital Cardiac Arrest

Overview

Journal Entropy (Basel)

Publisher MDPI

Date 2020 Dec 3

PMID 33265680

Citations 5

Authors

Beatriz Chicote

Unai Irusta

Elisabete Aramendi

Raul Alcaraz

Jose Joaquin Rieta

Iraia Isasi

Daniel Alonso

Maria Del Mar Baqueriza

Karlos Ibarguren

Affiliations

Soon will be listed here.

Abstract

Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension () and matching tolerance () for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.

Citing Articles

Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces.

Bernal Onate C, Melgarejo Meseguer F, Carrera E, Sanchez Munoz J, Garcia Alberola A, Rojo Alvarez J Sensors (Basel). 2023; 23(5).

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A method to predict ventricular fibrillation shock outcome during chest compressions.

Coult J, Rea T, Blackwood J, Kudenchuk P, Liu C, Kwok H Comput Biol Med. 2020; 129:104136.

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Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Picon A, Irusta U, Alvarez-Gila A, Aramendi E, Alonso-Atienza F, Figuera C PLoS One. 2019; 14(5):e0216756.

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Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest.

Chicote B, Aramendi E, Irusta U, Owens P, Daya M, Idris A Resuscitation. 2019; 138:74-81.

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

Coult J, Blackwood J, Sherman L, Rea T, Kudenchuk P, Kwok H Circ Arrhythm Electrophysiol. 2019; 12(1):e006924.

PMID: 30626208 PMC: 6532650. DOI: 10.1161/CIRCEP.118.006924.

References

Jekova I, Mougeolle F, Valance A . Defibrillation shock success estimation by a set of six parameters derived from the electrocardiogram. Physiol Meas. 2004; 25(5):1179-88. DOI: 10.1088/0967-3334/25/5/008. View

Weisfeldt M, Becker L . Resuscitation after cardiac arrest: a 3-phase time-sensitive model. JAMA. 2002; 288(23):3035-8. DOI: 10.1001/jama.288.23.3035. View

Perkins N, Schisterman E . The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006; 163(7):670-5. PMC: 1444894. DOI: 10.1093/aje/kwj063. View

Povoas H, Bisera J . Electrocardiographic waveform analysis for predicting the success of defibrillation. Crit Care Med. 2000; 28(11 Suppl):N210-1. DOI: 10.1097/00003246-200011001-00010. View

Eftestol T, Sunde K, Ole Aase S, Husoy J, Steen P . Predicting outcome of defibrillation by spectral characterization and nonparametric classification of ventricular fibrillation in patients with out-of-hospital cardiac arrest. Circulation. 2000; 102(13):1523-9. DOI: 10.1161/01.cir.102.13.1523. View

Gundersen K, Kvaloy J, Kramer-Johansen J, Eftestol T . Identifying approaches to improve the accuracy of shock outcome prediction for out-of-hospital cardiac arrest. Resuscitation. 2007; 76(2):279-84. DOI: 10.1016/j.resuscitation.2007.07.019. View

Fadlallah B, Chen B, Keil A, Principe J . Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information. Phys Rev E Stat Nonlin Soft Matter Phys. 2013; 87(2):022911. DOI: 10.1103/PhysRevE.87.022911. View

Manis G, Aktaruzzaman M, Sassi R . Low Computational Cost for Sample Entropy. Entropy (Basel). 2020; 20(1). PMC: 7512258. DOI: 10.3390/e20010061. View

Rubart M, Zipes D . Mechanisms of sudden cardiac death. J Clin Invest. 2005; 115(9):2305-15. PMC: 1193893. DOI: 10.1172/JCI26381. View

10.

Watson J, Uchaipichat N, Addison P, Clegg G, Robertson C, Eftestol T . Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods. Resuscitation. 2004; 63(3):269-75. DOI: 10.1016/j.resuscitation.2004.06.012. View

11.

Callaway C, Menegazzi J . Waveform analysis of ventricular fibrillation to predict defibrillation. Curr Opin Crit Care. 2005; 11(3):192-9. DOI: 10.1097/01.ccx.0000161725.71211.42. View

12.

Sherman L, Callaway C, Menegazzi J . Ventricular fibrillation exhibits dynamical properties and self-similarity. Resuscitation. 2000; 47(2):163-73. DOI: 10.1016/s0300-9572(00)00229-x. View

13.

Kern K, Garewal H, Sanders A, Janas W, Nelson J, Sloan D . Depletion of myocardial adenosine triphosphate during prolonged untreated ventricular fibrillation: effect on defibrillation success. Resuscitation. 1990; 20(3):221-9. DOI: 10.1016/0300-9572(90)90005-y. View

14.

Gong Y, Lu Y, Zhang L, Zhang H, Li Y . Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest. Biomed Res Int. 2015; 2015:493472. PMC: 4572405. DOI: 10.1155/2015/493472. View

15.

Firoozabadi R, Nakagawa M, Helfenbein E, Babaeizadeh S . Predicting defibrillation success in sudden cardiac arrest patients. J Electrocardiol. 2013; 46(6):473-9. DOI: 10.1016/j.jelectrocard.2013.06.007. View

16.

Salcido D, Menegazzi J, Suffoletto B, Logue E, Sherman L . Association of intramyocardial high energy phosphate concentrations with quantitative measures of the ventricular fibrillation electrocardiogram waveform. Resuscitation. 2009; 80(8):946-50. DOI: 10.1016/j.resuscitation.2009.05.002. View

17.

Jacobs I, Nadkarni V, Bahr J, Berg R, Billi J, Bossaert L . Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries. A statement for healthcare professionals from a task force of the international liaison committee on.... Resuscitation. 2004; 63(3):233-49. DOI: 10.1016/j.resuscitation.2004.09.008. View

18.

Podbregar M, Kovacic M, Podbregar-Mars A, Brezocnik M . Predicting defibrillation success by 'genetic' programming in patients with out-of-hospital cardiac arrest. Resuscitation. 2003; 57(2):153-9. DOI: 10.1016/s0300-9572(03)00030-3. View

19.

ZOLL P . Resuscitation of the heart in ventricular standstill by external electric stimulation. N Engl J Med. 1952; 247(20):768-71. DOI: 10.1056/NEJM195211132472005. View

20.

Howe A, Escalona O, Di Maio R, Massot B, Cromie N, Darragh K . A support vector machine for predicting defibrillation outcomes from waveform metrics. Resuscitation. 2013; 85(3):343-9. DOI: 10.1016/j.resuscitation.2013.11.021. View