Automatic Detection of Ventilations during Mechanical Cardiopulmonary Resuscitation

Feedback on chest compressions and ventilations during cardiopulmonary resuscitation (CPR) is important to improve survival from out-of-hospital cardiac arrest (OHCA). The thoracic impedance signal acquired by monitor-defibrillators during treatment can be used to provide feedback on ventilations, but chest compression components prevent accurate detection of ventilations. This study introduces the first method for accurate ventilation detection using the impedance while chest compressions are concurrently delivered by a mechanical CPR device. A total of 423 OHCA patients treated with mechanical CPR were included, 761 analysis intervals were selected which in total comprised 5 884 minutes and contained 34 864 ventilations. Ground truth ventilations were determined using the expired CO_{2} channel. The method uses adaptive signal processing to obtain the impedance ventilation waveform. Then, 14 features were calculated from the ventilation waveform and fed to a random forest (RF) classifier to discriminate false positive detections from actual ventilations. The RF feature importance was used to determine the best feature subset for the classifier. The method was trained and tested using stratified 10-fold cross validation (CV) partitions. The training/test process was repeated 20 times to statistically characterize the results. The best ventilation detector had a median (interdecile range, IDR) F_{1}-score of 96.32 (96.26-96.37). When used to provide feedback in 1-min intervals, the median (IDR) error and relative error in ventilation rate were 0.002 (-0.334-0.572) min-1 and 0.05 (-3.71-9.08)%, respectively. An accurate ventilation detector during mechanical CPR was demonstrated. The algorithm could be introduced in current equipment for feedback on ventilation rate and quality, and it could contribute to improve OHCA survival rates.