## 1 Introduction  
Atrial fibrillation (AF) occurs at any age, but the incidence of AF is far more common in older adults than in children [1, 2, 3]. In the European Union, the number of subjects with AF (those aged \(\geq\) 55) is estimated to be doubled from 2010 to 2060, i.e., from 8.8 to 17.9 million [4]. Meanwhile, in the US, the number of subjects with AF will reach more than 5.6 million in 2050. Half of them will be over 80 years old. AF is a heart rhythm disorder that is characterized by irregular heart contractions [5; 6]. It is triggered by two conditions, i.e., the deceleration of conduction velocity in various atrial regions and an increase in the heterogeneity of atrial refractoriness. Other conditions, such as an overactive thyroid (thyroid gland) or the excessive use of alcohol, can also cause AF. Although there is an insignificant relationship, AF is also often associated with an increased risk of chronic kidney disease, ischemic heart disease, and sudden cardiac death [7; 8]. AF is not classified as a severe disease; however, it may induce paralysis complications, such as heart failure and atrial thrombosis, with a risk of stroke [9]. One in three patients with AF will not have any symptoms. The early management of AF needs to be performed in the detection and screening of AF. In 2020, the European Society of Cardiology (ESC), through its guidelines, recommended that wearable devices be employed for the self-detection of AF in the general population [10]. Along with advances in telecommunications technology and mobile apps, several portable AF detectors have been proposed and developed [11]. Based on the type of signal used, AF detectors can be categorized into two groups, namely, photoplethysmogram (PPG)-based detectors and electrocardiogram (ECG)-based detectors. [PERSON], et al. [12] developed an Android app on Huawei smartphones to detect AF. The application utilizes the smartphone's camera to detect blood volume changes in accordance with the principle of the PPG sensor. Then, the validation is carried out using the ECG-12 Leads Standard. The analysis results have shown that the Android application is able to work well; the accuracy in detecting AF was 94%. [PERSON], et al. [13] used a PPG-based commercial smartwatch to identify AF. Evaluation results showed that the accuracy of AF detection in patients undergoing cardioversion treatment was high. Unfortunately, it is not for outpatient use. [PERSON], et al. [14] evaluated the accuracy of ECG-based off-the-shelf Kardia-Mobile products from AliveCor [15] for detecting arrhythmias (including AF) in the pediatric population. The results of the study showed that Kardia-Mobile could well distinguishing ECG signals in a population of healthy children and populations of children with heart disease. The false-positive rate in detecting AF was 13 percent (4/30). Using the same type of device as [PERSON], et al. [14], [PERSON], et al. [16] conducted research on different subjects. The subjects used in the research were elite athletes and cardiac clinic patients. The results of the study concluded that Kardia-Mobile was suitable for general conduction and arrhythmia screening. There are several steps that must be performed to detect atrial fibrillation. These steps are shown in detail in Figure 1, consisting of signal preprocessing (denoising), segmentation, feature extraction/selection, and classification [17; 18; 19]. As shown in Figure 1, the first step of detecting AF is denoising, which is the process of removing noise using either analog or digital filters. It can be performed in both hardware and software [20]. The second step is signal segmentation for recognizing a complete cycle of the signal [21]. Feature extraction/selection is a further process after the signals can be identified. In ECG-based detectors, the interval of RR is frequently used as a feature for detecting AF [22; 23; 24; 25]. Another feature that is often utilized for AF detection purposes is the QRS signal duration, as used by [PERSON], et al. [26].