Prediction of Countershock Success in Patients Using the Autoregressive Spectral Estimation

C. N. Nowak; A. Neurauter; L. Wieser; V. Wenzel; B. Abella; H. Myklebust; P. A. Steen; H.-U. Strohmenger

doi:10.3414/ME10-01-0033

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2012; 51(01): 13-20
DOI: 10.3414/ME10-01-0033

Original Articles

Schattauer GmbH

Prediction of Countershock Success in Patients Using the Autoregressive Spectral Estimation

Authors

C. N. Nowak

¹Institute of Biomedical Engineering, University for Health Science, Medical Informatics and Technology, Hall, Austria
A. Neurauter

²Department of Anesthesiology and Critical Care Medicine, Innsbruck Medical University, Innsbruck, Austria
L. Wieser

¹Institute of Biomedical Engineering, University for Health Science, Medical Informatics and Technology, Hall, Austria
V. Wenzel

²Department of Anesthesiology and Critical Care Medicine, Innsbruck Medical University, Innsbruck, Austria
B. Abella

³Department of Emergency Medicine Philadelphia, Medical University of Pennsylvania, Philadelphia, PA, USA
H. Myklebust

⁴Laerdal Medical AS, Stavanger, Norway
P. A. Steen

⁵Department of Anesthesiology, Ulleval University Hospital, Oslo, Norway

⁶Institute of Experimental Medical Research, Ulleval University Hospital, Oslo, Norway
H.-U. Strohmenger

²Department of Anesthesiology and Critical Care Medicine, Innsbruck Medical University, Innsbruck, Austria

Abstract

Summary

Objectives: Ventricular fibrillation (VF) is a life-threatening cardiac arrhythmia and within of minutes of its occurrence, optimal timing of countershock therapy is highly warranted to improve the chance of survival. This study was designed to investigate whether the autoregressive (AR) estimation technique was capable to reliably predict countershock success in VF cardiac arrest patients.

Methods: ECG data of 1077 countershocks applied to 197 cardiac arrest patients with out-of-hospital and in-hospital cardiac arrest between March 2002 and July 2004 were retrospectively analyzed. The ECG from the 2.5 s interval of the precountershock VF ECG was used for computing the AR based features Spectral Pole Power (SPP) and Spectral Pole Power with Dominant Frequency weighing (SPPDF) and Centroid Frequency (CF) and Amplitude Spectrum Area (AMSA) based on Fast Fourier Transformation (FFT).

Results: With ROC AUC values up to 84.1 % and diagnostic odds ratio up to 19.12 AR based features SPP and SPPDF have better prediction power than the FFT based features CF (80.5 %; 6.56) and AMSA (82.1 %; 8.79).

Conclusions: AR estimation based features are promising alternatives to FFT based features for countershock outcome when analyzing human data.

Keywords

Ventricular fibrillation - prediction - counter-shock success - autoregressive spectral estimator

Full Text

References

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