Key result
Small values of parameters r and n for the EntropyAF algorithm led to a high detection accuracy for atrial fibrillation, achieving a best AUC of 98.15%.
Why the study?
The EntropyAF detector requires tolerance threshold r and similarity weight n to be initialized, but optimal parameter combinations for high AF detection accuracy needed to be determined.
Optimizing the tolerance threshold and similarity weight parameters of the EntropyAF algorithm yields high accuracy (AUC 98.15%) for detecting atrial fibrillation from short-term RR interval time series.
Optimizing EntropyAF parameters may enhance AF detection tools; leaves open clinical adoption without prospective validation.
Entropy algorithm is an important nonlinear method for cardiovascular disease detection due to its power in analyzing short-term time series. In previous a study, we proposed a new entropy-based atrial fibrillation (AF) detector, i.e., EntropyAF, which showed a high classification accuracy in identifying AF and non-AF rhythms. As a variation of entropy measures, EntropyAF has two parameters that need to be initialized before the calculation: (1) tolerance threshold r and (2) similarity weight n. In this study, a comprehensive analysis for the two parameters determination was presented, aiming to achieve a high detection accuracy for AF events. Data were from the MIT-BIH AF database. RR interval recordings were segmented using a 30-beat time window. The parameters r and n were initialized from a relatively small value, then gradually increased, and finally the best parameter combination was determined using grid searching. AUC (area under curve) values from the receiver operator characteristic curve (ROC) were compared under different parameter combinations of parameters r and n, and the results demonstrated that the selection of these two parameters plays an important role in AF/non-AF classification. Small values of parameters r and n can lead to a better detection accuracy than other selections. The best AUC value for AF detection was 98.15%, and the corresponding parameter combinations for EntropyAF were as follows: r = 0.01, n = 0.0625, 0.125, 0.25, or 0.5; r = 0.05 and n = 0.0625, 0.125, or 0.25; and r = 0.10 and n = 0.0625 or 0.125.
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Zhao et al. (2021) studied Atrial fibrillation. EntropyAF parameter optimization (tolerance threshold r and similarity weight n) vs. Different parameter combinations was evaluated on AUC for AF detection. Small values of parameters r and n for the EntropyAF algorithm led to a high detection accuracy for atrial fibrillation, achieving a best AUC of 98.15%.
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