Modified moving average analysis of T-wave alternans to predict ventricular fibrillation with high accuracy

BD Nearing, RL Verrier - Journal of applied physiology, 2002 - journals.physiology.org
BD Nearing, RL Verrier
Journal of applied physiology, 2002journals.physiology.org
T-wave alternans is a marker of cardiac electrical instability with the potential for arrhythmia
risk stratification. The modified moving average method was developed to measure
alternans in settings with artifacts, noise, and nonstationary data. Algorithms were
developed and performance characteristics were validated with simulated
electrocardiograms (ECGs). Experimental laboratory ECGs with dynamically changing
alternans values were analyzed. Alternans values estimated by modified moving average …
T-wave alternans is a marker of cardiac electrical instability with the potential for arrhythmia risk stratification. The modified moving average method was developed to measure alternans in settings with artifacts, noise, and nonstationary data. Algorithms were developed and performance characteristics were validated with simulated electrocardiograms (ECGs). Experimental laboratory ECGs with dynamically changing alternans values were analyzed. Alternans values estimated by modified moving average analysis correlated strongly with input alternans values (r 2 = 0.9999). Rapidly changing alternans levels and phase reversals did not perturb the measurement. When heart rate was increased from 60 to 180 beats/min, with T-wave alternans apex moving from 237 to 103 ms after the R wave, the measured alternans peak varied <5% from input value. Simulated 50- to 1,000-μV motion artifact spikes typical of treadmill ECGs produced inaccuracies <2%. Alternans values in experimental laboratory study using standard electrodes tracked vulnerability to myocardial ischemia-induced ventricular fibrillation with 100% sensitivity and specificity at a cut point of 0.75 mV. Modified moving average analysis is a robust method that precisely measures T-wave alternans in settings with artifacts, noise, and nonstationary data typical of clinical ECGs and yields an accurate estimate of risk for ventricular fibrillation.
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