## 1 Introduction
### Review of Existing Literature
The ability to accurately assess RR intervals provides many benefits, including the capacity for long-term monitoring [32], as well as the ability to use different parameters, such as those used to evaluate fetal heart rate [4]. An interesting approach is the use of non-linear entropy coefficients in the description of RR intervals [8] or discrete-state Markov models [22]. The non-linear fractal dimension (FD) helps discriminate between physiological and pathological ECG [36]. An audio-inspired technique was proposed in [37]. It involved extracting RR-value sequences from filtered ECG signals and applying various audio spectral characteristics to these sequences. The resulting input vectors were then used with different types of manifold estimators. The authors also employed least absolute shrinkage and selection operator (LASSO) regression, a feature selection technique. Such an approach showed that spectral representations of AF segments can induce better-defined low-dimensional embedding manifolds and a sufficient intrinsic separability of AF from sinus rhythm. It is only sometimes feasible to directly compare previously proposed techniques due to the use of different databases of ECG signals. Artificial intelligence, such as deep learning or machine learning approaches, has significantly impacted the advancement in AF detection methods. A frequently used classifier in AF detection methods is the support vector machine (SVM) classifier [4; 19; 20; 25; 26], as well as the K-nearest neighbor (KNN) classifier [38]. A simple linear classifier was used on heart rhythm data in [32]. In ref. [3], the detection of AF was based on the receiver operating characteristic (ROC), which allowed the authors to estimate the optimal threshold for discriminating between episodes of AF and non-AF. A model combining convolutional and recurrent neural networks was proposed to extract high-level features from segments of RR intervals (RRIs) in order to classify them as AF or normal sinus rhythm in ref. [27]. The features of an ECG were extracted by a convolutional neural network (CNN) and loaded into a long short-term memory (LSTM) model in ref. [39]. The work of [PERSON] et al. [40] paired a variational auto-encoder (VAE) with a bidirectional long short-term memory network (BiLSTM) back end to perform unsupervised anomaly detection on heart rate sequences acquired from wearable devices, obtaining better results than typically used anomaly detection methods. [PERSON] coupled a discrete wavelet transform (DWT) with one-dimensional convolutional neural networks (1D-CNNs) to classify three categories: normal sinus rhythm (NSR), AF, and non-AF [41]. A lightweight CNN-based AF event detector was presented in ref. [42]. The accurate adjustment of the CNN parameters achieved promising AF detection results. The optimization of the parameter values made it possible to run the classification on a Raspberry Pi computer. A combination of atrial activity characteristics and RR interval characteristics was used as input for three popular classifiers (boosted trees (BoT), random forest (RF), and linear discriminant analysis (LDA)) with the random subspace method (RSM) in ref. [8]. [PERSON] et al. [43] used statistical descriptions of RR intervals to train a custom 2-layer neural network (NN) and a bagged tree ensemble (BT), while [PERSON] et al. [44] applied a 13-layer one-dimensional CNN. Ref. [45] described five well-known pre-trained convolutional neural networks, namely AlexNet, VGG16, GoogLeNet, RenNet50, SqueezeNet, and ShuffleNet, applied to atrial fibrillation detection. The authors verified the detection quality in proprietary and PhysioNet/CinC Challenge 2017 databases. [PERSON] et al. [46] proposed a dual-domain attention cascade, D2 AFNet, for accurate and interpretable AF detection by cascading attention-based bidirectional gated recurrent units and densely connected networks embedded with channel-spatial information fusion modules.