Abstract
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.
Highlights
Atrial fibrillation (AF) is a one of the most common arrhythmias, and usually refers to the rapid and irregular fibrillation of the atrium [1]
N was set as 30 and m was set as 1, because of the small value of N, i.e., as m and N were suggested to meet the requirement of N ≈ 10m ∼ 10m+1 from the previous studies [25,26,27,28]
The value of EntropyAF was dependent on the vector distances when the embedding dimension increased to m + 1 = 2, i.e., from the comparison between two vectors, Xim+1 and X jm+1
Summary
Atrial fibrillation (AF) is a one of the most common arrhythmias, and usually refers to the rapid and irregular fibrillation of the atrium [1]. The commonly used Holter monitor may miss many cases of paroxysmal AF [7], so the recently developed wearable and long-term electrocardiogram (ECG) monitoring strategies need to have the capability to scan AF and AF-related complications [8,9], which will require a more robust AF detector. There are normally two kinds of approaches for AF scanning—atrial activity analysis-based and ventricular response analysis-based methods [10]. The atrial activity analysis-based method analyses P waves or detects f waves in the ECG data [11,12,13], which requires high quality ECG signals [14]. The ventricular response analysis-based method analyses the irregularity of RR intervals, and has a relatively better tolerance for signal quality, and can be more suitable for AF scanning in the daily environment. Many AF detectors have been proposed in the past few decades, like density histograms [15], Poincaré plot [16], and median absolute deviation [17], as well as various entropy methods [10,18]
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