A new robust 2-D spectral estimation method and its application in cardiac data analysis

A new algorithm of 2-D spectral estimation is introduced based on unconstrained minimization of the estimated covariance recursion error of the Minimum Variance Models (causal, semicausal, noncausal). The model parameters are estimated directly from the data field in a manner which combines high resolution with robustness in the presence of nonstationarities. Computer-simulated short data fields consisting of two closely spaced travelling waves are employed to demonstrate that the method developed in this paper improves on the standard techniques for high resolution. The application of the new algorithm in intramyocardial data taken from ischemic and hypothermic intact canine preparations is also demonstrated. It is shown that by using this technique, the number and velocity vector of cardiac propagating wavefronts may be reliably determined, though the sets of cardiac data are very short and highly nonstationary. The clinical significance in evaluating prearrhythmic states of the heart and predicting onset of manifest arrhythmias is also discussed.