Automated classification of human atrial fibrillation from intraatrial electrograms.

The assessment of the degree of organization and the classification of atrial fibrillation (AF) according to the types defined by Wells usually resorts to the visual inspection of bipolar intraatrial electrograms. The focus of this study was to test seven parameters aimed to quantify the degree of organization of the electrograms, and then to design a final classification scheme based on a multidimensional, minimum-distance analysis. The following parameters were tested: mean atrial period (AP) and its coefficient of variation (CV); number of points lying at the baseline (NO) and the Shannon entropy (EN) of the amplitude probability density function (APDF); depolarization width (F-WIDTH); and correlation waveform analysis (CWA) and electrogram bandwidth (BW). The signal database consisted in a set of 160 AF strips of Type I, II, and III AF, scored by an expert cardiologist (60 Type I, 40 Type II, 60 Type III) and further divided in a training set (60) and a test set (100). Strips were 6 seconds long and were recorded with 5-mm interspace bipolar catheters from electrically induced (n = 13) and chronic (n = 10) patients. A classification algorithm based on a minimum-distance (Mahalanobis distance) discriminant analysis was tested. Using a single parameter, the best discriminations were provided by NO, F-WIDTH, and CV. F-WIDTH was found strongly inversely correlated to NO (r = -0.90). Of all the two-parameter combinations, CV-NO provided the best classification: 92 of 100 segments were correctly classified with sensitivity > 90% and specificity > 92%. A further improvement was obtained by including BW as a third parameter (93/100 correctly classified). The use of more than three parameters not only failed to improve, but even degraded the classification.