Abstract
This dataset was created as part of a study investigating the cardiopulmonary resuscitation (CPR) phase during out-of-hospital cardiac arrest (OHCA) interventions managed by basic life support (BLS) teams. Historically, the confined use of real-time monitoring of ventilatory parameters has limited the evaluation of BLS manual ventilation practices. The dataset includes ventilatory parameters recorded using a dedicated device connected to the bag-valve-mask (BVM) during OHCA treatment. Collected data comprise insufflated and tidal volumes (Vi and Vt), inspiratory and expiratory times (Ti and Te), leakage volumes and ratios, and intervals between ventilations. These parameters, saved in .csv format, were securely transferred for processing. Python scripts were utilized to ensure consistency, reduce noise, and facilitate further analysis. The dataset allows the identification of the first and second ventilations in each 30:2 CPR cycle. Distinguishing between ventilations, chest compressions, and noise remains challenging due to field conditions that may exceed the device's guidelines. Nevertheless, the data offer valuable insights into CPR practices, enabling researchers to monitor ventilations, assess adherence to guidelines, and examine the efficiency of ventilation techniques during resuscitation.