An automated pulse detector during out-of-hospital cardiac arrest (OHCA) is needed. The thoracic impedance (TI) recorded through defibrillation pads presents an impedance circulation component (ICC), hidden among other components, in the form of small fluctuations correlated with each effective heartbeat. This study pre-sentes a method based on the stationary wavelet transform (SWT) to derive the ICC. A dataset with 456 5-s segments, 175 pulseless electrical activity (PEA) and 281 pulse-generating rhythm (PR), with concurrent ECG and TI signals from 49 OHCA patients was used. The SWT was used to decompose the TI into 7 levels. The ICC was derived from soft denoised $d_{6}-d_{7}$ or $d_{7}$ detail coefficients for segments with heart rate ≥93 bpm and <93 bpm, respectively. Six features characterizing the amplitude and area of the ICC and its first derivative (dICC) were calculated. Their PEA/PR discrimination power was measured using the area under the curve (AUC). These AUCs were compared with those obtained for the same features derived from the ICC/dICC extracted using an adaptive recursive least-squares (RLS) algorithm. The six features showed a mean (standard deviation) AUC of 0.91 (0.03) while RLS-based features yielded an AUC of 0.85 (0.07). Combining these ICC/dICC features with ECG features in a machine learning classifier might result in a robust pulse detector.