## 1 Introduction
### Review of Existing Literature  
Remarkable advances in computing power have been achieved, and the latest developments in deep learning technologies have revolutionized the healthcare sector and medical practice. The growing accessibility of wearable gadgets that can monitor pulse and blood oxygen saturation or even capture a single-lead ECG makes it possible to collect data on the functioning and well-being of the heart. Wearable devices, especially in terms of their ECG signal monitoring capabilities combined with AI methods, are already gaining popularity [11]. Personal ECG devices such as the AliveCor Kardia, Apple Watch, or Samsung Galaxy Watch allow one to detect various arrhythmias on a larger scale than ever before and in a non-ambulatory environment [12]. The work of [PERSON] et al. [13] discussed the possibility of applying wearable devices for AF detection, focusing on the benefits and limitations of pairing wearable devices with machine learning/deep learning algorithms. The electrocardiogram is a fundamental tool for the clinical diagnosis of atrial activity and the assessment of the heart's electrical behavior. By recording and analyzing the electrical signals within the human body, the ECG provides a reliable and non-invasive method for assessing cardiac function and detecting abnormalities [4, 8, 14]. Non-ECG-based measurements, such as blood pressure, are also feasible, as [PERSON] et al. [15] demonstrated the ability to obtain a recall of 98% and a specificity of 92% in detecting AF using a blood pressure monitor. Another possibility is the use of a photoplethysmographic signal [16, 17], which has achieved a sensitivity and specificity of, respectively, 97% (\(\pm\)3%) and 99% (\(\pm\)3%) [16], or even cardiac dynamics signals, such as the ballistocardiogram signal or the seismocardiogram signal [18]. However, most reports in the literature on AF detection are based on the ECG signal. [PERSON] et al. [8] presented an interesting and valid concept for dividing AF detection methods into four groups. These groups of methods were: (i) AF detection based only on the RR interval features [4, 19, 20, 21, 22, 23], (ii) AF detection based only on the atrial activity (AA) features [2, 24, 25], (iii) AF detection based on the combination of the RR intervals and the AA features [8, 26], and (iv) AF detection based on no manually crafted features from the ECG [7, 27, 28, 29, 30, 31]. The most significant publications are also presented in the last table of this paper, which displays the results obtained using the MIT-BIH atrial fibrillation reference benchmark database. Different methods have been used to determine the characteristics of the RR intervals and atrial activity in the ECG signal. One of them is based on estimating the changes in the interval between two consecutive R-waves (RR) with various irregularity measures. The others are based on observing a lack of or abnormal P-waves (replaced by rapid, irregular, and disordered fibrillatory waves, called F-waves) [2, 32]. It can be concluded that the absence of P-waves is a crucial indicator of the presence of AF [6]. The occurrence of P-waves makes many methods such as the wavelet transform (WT) [2, 24, 25] or empirical mode decomposition (EMD) [33, 34] useful for the creation of feature sets. [PERSON] et al. [25] calculated the peak-to-average power ratio and log-energy entropy for each wavelet coefficient. An interesting approach was proposed by [PERSON] et al. [7]. It was based on the operation of spatio-temporal filtering to magnify and detect the prominent spatio-temporal patterns within the P-waves in multichannel ECG recordings. In the work of [PERSON] et al. [35], the ECG signal and its time-frequency representation were regardedas an image, and for the detection of AF, the bidirectional long- and short-term memory network was applied.