This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Eftestøl, T. Articles by Steen, P. A. Search for Related Content PubMed PubMed Citation Articles by Eftestøl, T. Articles by Steen, P. A. Related Collections CPR and emergency cardiac care Other diagnostic testing

(Circulation. 2000;102:1523.)
© 2000 American Heart Association, Inc.

Clinical Investigation and Reports

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

Trygve Eftestøl, SivIng; Kjetil Sunde, MD, PhD; Sven Ole Aase, DrIng; John Håkon Husøy, DrIng; Petter Andreas Steen, MD, PhD

From Høgskolen i Stavanger (T.E., S.O.A., J.H.H.), Department of Electrical and Computer Engineering, Stavanger, Norway; Ulleval University Hospital (K.S.), Institute for Experimental Medical Research and Norwegian Air Ambulance, Oslo, Norway; and Ulleval University Hospital (K.S., P.A.S.), Department of Anesthesiology, Oslo, Norway.

Correspondence to Trygve Eftestøl, Høgskolen i Stavanger, Department of Electrical and Computer Engineering, PO Box 2557, Ullandhaug, 4091 Stavanger, Norway. E-mail trygve-e{at}ux.his.no

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.

Key Words: cardiopulmonary resuscitation • fibrillation • defibrillation • Fourier analysis

This article has been cited by other articles:

				Part 5: Electrical Therapies: Automated External Defibrillators, Defibrillation, Cardioversion, and Pacing Circulation, December 13, 2005; 112(24_suppl): IV-35 - IV-46. [Full Text] [PDF]

				Part 7.2: Management of Cardiac Arrest Circulation, December 13, 2005; 112(24_suppl): IV-58 - IV-66. [Full Text] [PDF]

				Part 3: Defibrillation Circulation, November 29, 2005; 112(22_suppl): III-17 - III-24. [Full Text] [PDF]

				T. Eftestol, L. Wik, K. Sunde, and P. A. Steen Effects of Cardiopulmonary Resuscitation on Predictors of Ventricular Fibrillation Defibrillation Success During Out-of-Hospital Cardiac Arrest Circulation, July 6, 2004; 110(1): 10 - 15. [Abstract] [Full Text] [PDF]

				A. Amann, K. Rheinberger, U. Achleitner, A. C. Krismer, W. Lingnau, K. H. Lindner, and V. Wenzel The Prediction of Defibrillation Outcome Using a New Combination of Mean Frequency and Amplitude in Porcine Models of Cardiac Arrest Anesth. Analg., September 1, 2002; 95(3): 716 - 722. [Abstract] [Full Text] [PDF]

				T. Eftestol, K. Sunde, and P. A. Steen Effects of Interrupting Precordial Compressions on the Calculated Probability of Defibrillation Success During Out-of-Hospital Cardiac Arrest Circulation, May 14, 2002; 105(19): 2270 - 2273. [Abstract] [Full Text] [PDF]

				V. Shusterman, B. Aysin, K. P. Anderson, and A. Beigel Multidimensional Rhythm Disturbances as a Precursor of Sustained Ventricular Tachyarrhythmias Circ. Res., April 13, 2001; 88(7): 705 - 712. [Abstract] [Full Text] [PDF]