Predicting Outcome of Defibrillation by Spectral Characterization and Nonparametric Classification of Ventricular Fibrillation in Patients With Out-of-Hospital Cardiac Arrest

Abstract

Background —In 156 patients with out-of-hospital cardiac arrest of cardiac cause, we analyzed the ability of 4 spectral features of ventricular fibrillation 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 flatness, and energy were studied. A second decorrelated feature set was generated with the coefficients of the principal component analysis transformation of the original feature set. Each feature set was split into training and testing sets for improved reliability in the evaluation of nonparametric classifiers for each possible feature combination. The combination of centroid 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 spectral 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 predictive 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 features should be added to improve performance.