Prediction of countershock success: a comparison of autoregressive and fast fourier transformed spectral estimators.
OBJECTIVE
Spectral analysis of the ventricular fibrillation (VF) ECG has been used for predicting countershock success, where the Fast Fourier Transformation (FFT) is the standard spectral estimator. Autoregressive (AR) spectral estimation should compute the spectrum with less computation time. This study compares the predictive power and computational performance of features obtained by the FFT and AR methods.


METHODS
In an animal model of VF cardiac arrest, 41 shocks were delivered in 25 swine. For feature parameter analysis, 2.5 s signal intervals directly before the shock and directly before the hands-off interval were used, respectively. Invasive recordings of the arterial pressure were used for assessing the outcome of each shock. For a proof of concept, a micro-controller program was implemented.


RESULTS
Calculating the area under the receiver operating characteristic (ROC) curve (AUC), the results of the AR-based features called spectral pole power (SPP) and spectral pole power with dominant frequency (DF) weighing (SPPDF) yield better outcome prediction results (85%; 89%) than common parameters based on FFT calculation method (centroid frequency (CF), amplitude spectrum area (AMSA)) (72%; 78%) during hands-off interval. Moreover, the predictive power of the feature parameters during ongoing CPR was not invalidated by closed-chest compressions. The calculation time of the AR-based parameters was nearly 2.5 times faster than the FFT-based features.


CONCLUSIONS
Summing up, AR spectral estimators are an attractive option compared to FFT due to the reduced computational speed and the better outcome prediction. This might be of benefit when implementing AR prediction features on the microprocessor of a semi-automatic defibrillator.
Nowak
Claudia-Nike
CN
Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, 6060 Hall in Tirol, Austria. claudia.nowak@umit.at
Fischer
G
G
Neurauter
A
A
Wieser
L
L
Strohmenger
H U
HU
D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D004195:Disease Models, Animal; D004554:Electric Countershock / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D008838:Microcomputers; D016571:Neural Networks, Computer; D012372:ROC Curve; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D013552:Swine; D016896:Treatment Outcome; D014693:Ventricular Fibrillation / Q000628:therapy*