  Introduction: Atrial fibrillation (AF) is the most common heart arrhythmia,
characterized by the presence of fibrillatory waves (f-waves) in the ECG. We
introduce a voting scheme to estimate the dominant atrial frequency (DAF) of
f-waves. Methods: We analysed a subset of Holter recordings obtained from the
University of Virginia AF Database. 100 Holter recordings with manually
annotated AF events, resulting in a total 363 AF events lasting more than 1
min. The f-waves were extracted using four different template subtraction (TS)
algorithms and the DAF was estimated from the first 1-min window of each AF
event. A random forest classifier was used. We hypothesized that better
extraction of the f-wave meant better AF/non-AF classification using the DAF as
the single input feature of the RF model. Results: Performance on the test set,
expressed in terms of AF/non-AF classification, was the best when the DAF was
computed computed the three best-performing extraction methods. Using these
three algorithms in a voting scheme, the classifier obtained AUC=0.60 and the
DAFs were mostly spread around 6 Hz, 5.66 (4.83-7.47). Conclusions: This study
has two novel contributions: (1) a method for assessing the performance of
f-wave extraction algorithms, and (2) a voting scheme for improved DAF
estimation.
