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Sample entropy as a shock outcome predictor during basis life support

Optimizing defibrillation times may improve survival from ventricular fibrillation (VF) cardiac arrest. VF waveform analysis is one of the best non-invasive decision tools for shock outcome prediction. This study introduces a VF-waveform feature based on the computation of the sample entropy (SmpEnt) for shock outcome prediction. A database of 255 shocks were analyzed, using a 5 s preshock ECG segment. 14 classical VF waveform features measuring amplitude, slope, complexity and spectral characteristics were computed in addition to SmpEnt. An optimal detector of successful shocks was designed for each feature minimizing the Balanced Error Rate. Finally, the minimum pres hock segment duration assuring an accurate shock outcome prediction was determined for SmpEnt. SmpEnt is an improved shock outcome predictor, even for VF-segments as short as 1.5-s, and it could be used as a decision support tool to guide optimal timing for defibrillation.