A high-temporal resolution algorithm for quantifying organization during atrial fibrillation

Atrial fibrillation (AF) has been described as a "random" or "chaotic" rhyt hm. Evidence suggests that AF may have transient episodes of temporal and s patial organization. We introduce a new algorithm that quantifies AF organi zation by the mean-squared error (MSE) in the linear prediction between two cardiac electrograms, This algorithm calculates organization at a finer te mporal resolution (similar to 300 ms) than previously published algorithms. Using canine atrial epicardial mapping data, we verified that the MSE algo rithm showed nonfibrillatory rhythms to be significantly more organized tha n fibrillatory rhythms (p < .00001), Further, we compared the sensitivity o f MSE to that of two previously published algorithms by analyzing AF with s imulated noise and AF manipulated with vagal stimulation or by adenosine ad ministration to alter the character of the AF, MSE performed favorably in t he presence of noise. While all three algorithms distinguished between low and high vagal AF, MSE was the most sensitive in its discrimination. Only M SE could distinguish baseline AF from AF with adenosine, We conclude that o ur algorithm can distinguish different levels of organization during AF wit h a greater temporal resolution and sensitivity than previously described a lgorithms. This algorithm could lead to new ways of analyzing and understan ding AF as well as improved techniques in AF therapy.