Analysing the dynamics of pulseless electrical activity during cardiopulmonary resuscitation

Possible clinical states of a cardiac arrest patient are ventricular fibrillation/tachycardia (VF/VT), asystole (ASY) or pulseless electrical activity (PEA), and the treatment goals are return of spontaneous circulation (ROSC) and neurologic ally intact survival. Waveform analysis has been used in VF to predict treatment outcomes and we hypothesised that similar analysis in PEA could predict transformation to ROSC. We analysed 120 and 83 PEA segments prior to transitions to ROSC and ASY, respectively, to investigate the ability often electrocardiograph (ECG) features to predict transitions to ROSC or ASY using neural networks. The feature combination that yielded the best discrimination had a meanplusmnSD area under the receiver operating characteristics curve of 0.88plusmn0.02. The results suggest that the ECG contains information regarding the dynamics of PEA which can be used to study effects of therapies in cardiac arrest patients.