Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest
AbstractPrediction 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
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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.
Chicote B, Irusta U, Alcaraz R, Rieta JJ, Aramendi E, Isasi I, Alonso D, Ibarguren K. Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. Entropy. 2016; 18(9):313.Chicago/Turabian Style
Chicote, Beatriz; Irusta, Unai; Alcaraz, Raúl; Rieta, José J.; Aramendi, Elisabete; Isasi, Iraia; Alonso, Daniel; Ibarguren, Karlos. 2016. "Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest." Entropy 18, no. 9: 313.
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