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Entropy 2016, 18(9), 313; doi:10.3390/e18090313

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

1
Department of Communications Engineering, University of the Basque Country (UPV/EHU), Bilbao 48013, Spain
2
Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha (UCLM), Ciudad Real 13071, Spain
3
Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia (UPV), Valencia 46022, Spain
4
Emergency Medical System (Emergentziak-Osakidetza), Basque Health Service, Donostia 20014, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 19 July 2016 / Revised: 11 August 2016 / Accepted: 19 August 2016 / Published: 24 August 2016
(This article belongs to the Special Issue Symbolic Entropy Analysis and Its Applications)
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Abstract

Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 μ V . This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment. View Full-Text
Keywords: ventricular fibrillation; defibrillation; shock outcome prediction; out-of-hospital cardiac arrest; non-linear dynamics; entropy measures; regularity-based entropies; predictability-based entropies; fuzzy entropy ventricular fibrillation; defibrillation; shock outcome prediction; out-of-hospital cardiac arrest; non-linear dynamics; entropy measures; regularity-based entropies; predictability-based entropies; fuzzy entropy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chicote, B.; Irusta, U.; Alcaraz, R.; Rieta, J.J.; Aramendi, E.; Isasi, I.; Alonso, D.; Ibarguren, K. Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. Entropy 2016, 18, 313.

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