## 1 Introduction  
Accurate identification and classification of cardiac arrhythmias remains paramount in clinical cardiology, given their well-established association with significant morbidity and mortality [1]. Electrocardiograms (ECGs) offer a noninvasive and readily obtainable window into the electrical activity of the heart, allowing physicians to visually assess for arrhythmias by analyzing characteristic wave morphology, timing, and presence/absence (e.g., P wave, QRS complex, T wave, Figure 1). However, manual analysis, particularly of extended recordings such as Holter ECGs, can be time-consuming, prone to fatigue-induced errors, and potentially limit diagnostic efficiency. To alleviate the burden of time-consuming manual analysis, automatic classifiers that extend the capabilities of human experts have been proposed. Researchers have developed a plethora of automatic arrhythmia detectors and classifiers following a four-step process. Initially, the ECG signal undergoes a preprocessing stage to eliminate undesirable noise. This endeavor is fulfilled via diverse techniques, including digital filters [2; 3], wavelet transforms [4], adaptive filtering [5], and Bayesian filtering [6]. Subsequently, each heartbeat is delineated and discerned on the basis of its QRS complex. The extracted features are utilized for the classifier training process, which represents the third step. The classification process itself represents the final step. Classification performance is strongly related to the effectiveness of the extracted features and the type of classifier. Traditional arrhythmia classifiers leverage handcrafted features extracted from the raw ECG signal across various domains. Morphological features, such as the amplitude and duration of different waves, can be extracted from the time domain of the ECG signal [7; 8; 9; 10; 11]. Various classifiers trained on this category of features have been proposed. For instance, a decision tree-based classifier trained to recognize six arrhythmia types achieved an accuracy of 99.51% [7]. Similarly, a linear discriminant analysis (LDA)-based classifier produced an accuracy of 94.03% when used to discriminate between six types of heartbeat [8]. In [12], authors use cluster analysis to separate the five arrhythmia types. An accuracy of 94.00% is reported. An accuracy of 99.40% is realized using a regression neural network-based classifier to distinguish 5 types of arrhythmia [9]. Authors in [13] performed a comparative study between four linear classifiers using a combination of R-R interval and morphological handcraft features. Detailed characteristics of the ECG signals can be obtained by applying the time-frequency domain through wavelet coefficients. In general, classifiers trained on wavelet-based features achieve better performance [14]. Several studies have found that incorporating rhythmic characteristics of the ECG signal, such as R-R intervals, into the feature vector enhances classification performance [15; 16]. The nonlinear dynamics of the heartbeat ECG signal were obtained from statistical metrics such as high-order statistics (HOS), which were used to train a fuzzy hybrid neural network-based classifier. An accuracy of roughly 96.00% is registered [17]. Combining features from various domains into a single feature vector is a popular approach for leveraging complementary information captured by each domain. Several studies were developed with various classifiers, achieving enhanced results [18; 19]. Comparative studies between morphological-based and frequency-based classifiers have also been presented [20]. These techniques (i.e., classifiers based on handcrafted features) have been largely superseded by emerging approaches, particularly those based on deep learning [21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. These novel approaches automatically extract relevant features that enhance classification accuracy, despite their elusive nature.