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It has been suggested to develop automated external defibrillators with the ability to monitor cardiopulmonary resuscitation (CPR) performance online and give corrective feedback in order to improve the resuscitation quality. Thoracic impedance changes are closely correlated to lung volume changes and can be used to monitor the ventilatory activity. We developed a pattern-recognition-based detection system that uses thoracic impedance to accurately detect ventilation during ongoing CPR. The detection system was developed and evaluated on recordings of real-world resuscitation efforts of cardiac arrest patients where ventilations were manually annotated by human experts. The annotated ventilations were detected with an overall positive predictive value of 95.5% for a sensitivity of 90.4%. During chest compressions, the detection system achieved a mean positive predictive value of 94.8% for a sensitivity of 88.7%. The results suggest that accurate ventilation detection during CPR based on the proposed approach is feasible, and that the performance is not significantly degraded in the presence of chest compressions.