Application of entropy-based features to predict defibrillation outcome in cardiac arrest
| Autor: | Daniel Alonso, Unai Irusta, Jose J. Rieta, Raúl Alcaraz, Beatriz Chicote, Karlos Ibarguren, Iraia Isasi, Elisabete Aramendi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
ventricular fibrillation
defibrillation shock outcome prediction out-of-hospital cardiac arrest non-linear dynamics entropy measures regularity-based entropies predictability-based entropies fuzzy entropy Defibrillation medicine.medical_treatment General Physics and Astronomy lcsh:Astrophysics 030204 cardiovascular system & hematology Out of hospital cardiac arrest TECNOLOGIA ELECTRONICA 03 medical and health sciences 0302 clinical medicine Fuzzy entropy lcsh:QB460-466 medicine lcsh:Science Simulation Mathematics Welfare economics 030208 emergency & critical care medicine lcsh:QC1-999 lcsh:Q lcsh:Physics |
| Zdroj: | Addi. Archivo Digital para la Docencia y la Investigación instname Entropy; Volume 18; Issue 9; Pages: 313 RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia Entropy, Vol 18, Iss 9, p 313 (2016) |
| Popis: | 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 μV. 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. This work received financial support from Spanish Ministerio de Economia y Competitividad, projects TEC2013-31928 and TEC2014-52250-R, and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), project TEC2015-64678-R; from Junta de Comunidades de Castilla La Mancha, project PPII-2014-026-P; and from UPV/EHU through the grant PIF15/190 and through its research unit UFI11/16. |
| Databáze: | OpenAIRE |
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