## 1 Introduction
Finally, classification is a technique used in grouping features according to the type of arrhythmia, such as: premature atrial contraction (PAC), premature ventricular contraction (PAC), AF, or ventricular tachycardia/ventricular fibrillation (VT/VF). Many studies have used artificial intelligence, such as machine learning algorithms, to classify the feature data of arrhythmia, such as in [PERSON], et al. [27], [PERSON], et al. [28], [PERSON], et al. [29], [PERSON], et al. [30], to achieve accurate detection results. Feature extraction is the key to success in arrhythmia detection (including AF). There are several feature extraction techniques have been proposed for detecting AF features, such as in [PERSON], et al. [18], [PERSON], et al. [22], [PERSON], et al. [23], [PERSON], et al. [31], [PERSON], et al. [32], [PERSON], et al. [33], [PERSON], et al. [34], [PERSON], et al. [35]. In general, ECG-based AF extraction techniques can be classified into two categories [36], i.e., feature extraction (FE) based on dynamic features of the signal [16; 22; 37; 38; 39; 40; 41] and FE based on the signal morphology [33; 34; 35; 31; 18; 31]. In the dynamic feature category, there are several AF features that can be identified from ECG signals, such as RR interval, QT segments, and QRS duration. In 2014, RR intervals and the Shannon entropy of AF-confirmed ECG signals were used by [PERSON], et al. [22] as features for detecting AF. Both features were then classified using a proposed classification algorithm. The experimental results showed that the AF detection accuracy reached 99.5%. In 2019, [PERSON], et al. [37] combined both RR intervals and the variability of QT segments as parameters of AF identification. The accuracy in detecting AF was 94%. Unfortunately, [PERSON], et al. [37] did not share information about sensitivity and specificity in their report. [PERSON], et al. [26] revealed the relationship between AF occurrence and QRS duration. They found that in a large population, QRS duration was an independent predictor of AF incidence in women but not men. In the FE group based on signal morphology, researchers have attempted to eliminate the method for detecting P peaks, R peaks, or T peaks so that the performance of AF detection does not depend on the quality of beat detection. In 2015, [PERSON], et al. [31] used stationary wavelet transform (SWT) as a method to extract AF features from ECG signals. They used the support vector machine (SVM) classifier to classify the AF features. Experiments showed that the sensitivity achieved was 97.0% and the specificity of detection was 97.1%. [PERSON], et al. [33] also proposed using a wavelet to obtain AF features. However, unlike [PERSON], et al. [31], the features used were continuous wavelet transform (CWT), and the classifier chosen was the convolutional neural network (CNN). The experiments carried out resulted in a sensitivity of 99.41%, with a specificity of 98.91% and an accuracy of 99.23%. In 2018, [PERSON], et al. [18] analyzed log energy entropy (LEE) and permutation entropy (PEn) features to identify AF. Both features were calculated from a sub-band signal generated by flexible analytic wavelet transform (FAWT). Experimental results showed that LEE was superior to PEn, with the accuracy, sensitivity, and specificity being 96.864%, 95.8%, and 97.8%, respectively. In the experiment, the classifier chosen was random forest (RF). In general, morphological-based feature extraction studies, as explained in the previous paragraph, only focus on proposing new methods without an in-depth exploration of the effects of wavelet parameters, such as wavelet basis functions (WBFs) and decomposition levels, on overall system performance. As a consequence, the results are not optimal, and there is uncertainty about whether it is the best result or not. [PERSON], et al. [18] only observed the effect of the number of features derived from LEE and PEn based on accuracy, sensitivity, and specificity. The features of the wavelet function were not explored. Almost similar to [PERSON], et al.