Aims: High quality cardiopulmonary resuscitation (CPR) plays an important role in survival of out-of-hospital cardiac arrests (OHCAs). We have developed an algorithm to automatically identify the quality of chest compressions from data retrieved from automatic external defibrillators (AEDs).
Methods: Electrocardiographic (ECG) signals retrieved from AEDs were analyzed by a newly developed algorithm to identify fluctuations in CPR. The algorithm contained three steps. First, it decomposed the AED signals into several intrinsic mode fluctuations (IMFs) by empirical mode decomposition (EMD). Second, it identified the dominant IMFs that carried the chest compression signals and weighted the IMFs to both enhance the chest compression oscillations and filter the noise. Third, it calculated the autocorrelation function (ACF) of the reconstructed signals and tested their periodicity. Using this algorithm, several CPR quality indicators were automatically calculated minute-by-minute and compared with those derived by audio and visual review of AED data by experienced physicians.
Results: A total of 77 (29 women, 48 men) OHCA patients were enrolled, and 351 one-min segments were analyzed. The results showed that the CPR quality parameters calculated from the algorithm were highly correlated with those from the manual review (all P<0.001). The limits of agreement by Bland-Altman analysis were acceptable for chest compression number, total flow time, and no flow time, but not for CPR rate. We also demonstrated that only 41.8±29.8% of time was spent in chest compressions and only 7.5±16.8% was spent in adequate chest compressions.
Conclusion: Our results demonstrated that several indicators of CPR quality can be precisely and automatically determined by analyzing the ECG signals from AEDs using EMD and autocorrelograms.
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