A feature vector combining subband sample entropy of the dominant atrial frequency and relative harmonics energy accurately discriminated paroxysmal from persistent AF (AUC 0.923).
Observational (n=50)
Does a signal characterization approach using subband sample entropy and relative harmonics energy improve the classification of paroxysmal versus persistent atrial fibrillation in short ambulatory ECG segments?
A novel signal characterization approach using subband sample entropy and relative harmonics energy can accurately discriminate between paroxysmal and persistent atrial fibrillation from short 10-second ambulatory ECG segments.
Absolute Event Rate: 0.923% vs 0.826%
The problem of classifying short atrial fibrillatory segments in ambulatory ECG recordings as being either paroxysmal or persistent is addressed by investigating a robust approach to signal characterization. The method comprises preprocessing estimation of the dominant atrial frequency for the purpose of controlling the subbands of a filter bank, computation of the relative subband (harmonics) energy, and the subband sample entropy. Using minimum-error-rate classification of different feature vectors, a data set consisting of 24-h ambulatory recordings from 50 subjects with either paroxysmal (26) or persistent (24) atrial fibrillation (AF) was analyzed on a 10-s segment basis; a total of 212,196 segments were classified. The best performance in terms of area under the receiver operating characteristic curve was obtained for a feature vector defined by the subband sample entropy of the dominant atrial frequency and the relative harmonics energy, resulting in a value of 0.923, whereas that of the dominant atrial frequency was equal to 0.826. It is concluded that paroxysmal and persistent AFs can be discriminated from short segments with good accuracy at any time of an ambulatory recording.
Alcaraz et al. (Fri,) conducted a observational in Atrial Fibrillation (n=50). Signal characterization using subband sample entropy of dominant atrial frequency and relative harmonics energy vs. Dominant atrial frequency alone was evaluated on Classification performance (area under the receiver operating characteristic curve). A feature vector combining subband sample entropy of the dominant atrial frequency and relative harmonics energy accurately discriminated paroxysmal from persistent AF (AUC 0.923).