Predicting outcome of defibrillation by spectral characterization and nonparametric classification of ventricular fibrillation in patients with out-of-hospital cardiac arrest

Background-In 156 patients with out-of-hospital cardiac arrest of cardiac c ause, we analyzed the ability of 4 spectral features of ventricular fibrill ation before a total of 868 shocks to discriminate or not between segments that correspond to return of spontaneous circulation (ROSC). Methods and Results-Centroid frequency, peak power frequency, spectral flat ness, and energy were studied. A second decorrelated feature set was genera ted with the coefficients of the principal component analysis transformatio n of the original feature set. Each feature set was split into training and testing sets for improved reliability in the evaluation of nonparametric c lassifiers for each possible feature combination. The combination of centro id frequency and peak power frequency achieved a mean +/- SD sensitivity of 92 +/- 2% and specificity of 27 +/- 2% in testing. The highest performing classifier corresponded to the combination of the 2 dominant decorrelated s pectral features with sensitivity and specificity equal to 92 +/- 2% and 42 +/- 1% in testing or a positive predictive value of 0.15 and a negative pr edictive value of 0.98. Using the highest performing classifier, 328 of 781 shocks not leading to ROSC would have been avoided, whereas 7 of 87 shocks leading to ROSC would not have been administered. Conclusions-The ECG contained information predictive of shock therapy. This could reduce the delivery of unsuccessful shocks and thereby the duration of unnecessary "hands-off" intervals during cardiopulmonary resuscitation. The low specificity and positive predictive value indicate that other featu res should be added to improve performance.