Adaptive Multiple Frequency Tracking Algorithm: Detection of Stable Atrial Fibrillation Sources from Standard 12-Lead ECG

In this study we investigate a means of distinguishing between stable and more complex atrial fibrillation (AF) sources by analyzing ECG signals. For this purpose, 21 episodes of AF were generated by using a 3D biophysical model of the atria. The AF episodes were classified into two groups (with or without stable sources) by visual ob-servation of the electrical propagation on the epicardial tissue (gold standard). The simulated 12-lead ECGs of these AF episodes were computed by using a compartmen-tal torso model. The analysis of the ECG signals was per-formed by applying an adaptive multiple frequency track-ing algorithm. The normalized power outputs of the algo-rithm directly provided information concerning the stabil-ity level. The comparison of the results of our method with the gold standard yielded 85.7 % of correct classifications, with a sensitivity of 100 % and 75 % specificity. 1