## 1 Introduction  
Arrhythmia affects heart rhythm, characterized by an irregular heartbeat that can be either faster or slower than usual. Electrocardiogram (ECG) is the primary diagnostic tool used to detect such abnormalities, but detecting and classifying arrhythmias can be challenging due to the nature of ECG signals. Automated arrhythmia analysis could provide a faster and more accurate solution to these challenges [1; 2; 3; 4; 5; 6; 7]. Several studies have been conducted to diagnose ECGs using signal processing, machine learning, and deep learning techniques. For example, [PERSON] et al. [8] developed a patient-specific electrocardiogram classification and monitoring system using an adaptive implementation of 1D convolutional neural networks (CNNs). By training a separate CNN for each patient, patient-specific features were extracted, leading to increased classification performance. The researchers in [9] used the n-beat-score map (n-BSM), a 2D ECG representation, in an adversarial framework to improve arrhythmia classification. They trained a beat classifier to generate a patient-independent n-BSM (PI-BSM), reducing the impact of patient-specific features. [PERSON] et al. [10] used a nine-layer deep convolutional neural network (CNN) to automatically classify five different heartbeat arrhythmias in the MIT-BIH arrhythmia dataset. After applying a lowpass filter to the original data, they performed experiments on both noisy and noise-free data. In another study by [PERSON] et al. [11], they developed a 34-layer deep neural network (DNN) that classifies 12 rhythm classes using ECG data. The developed network achieved an area under the ROC curve (AUC) of 0.97, which exceeded the average accuracy level of cardiologists. Similarly, [PERSON] et al. [12] developed a ResNet-LSTM-based deep learning model for arrhythmia classification that combined cross-validation with genetic algorithms for model optimization. The proposed technique achieved an average accuracy of 98.0% in arrhythmia detection on the MIT-BIH arrhythmia dataset. In another study, [PERSON] et al. [13] proposed a novel hybrid neural model utilizing focal loss to address training data imbalance. Using the MIT-BIH atrial fibrillation database with four classes, the model achieved a sensitivity of 97.87% and a specificity of 99.29% using a ten-fold cross-validation strategy. [PERSON] et al. [14] proposed a method for ECG signal recognition based on a discrete wavelet-concatenated mesh tree (DW-CMT) and ternary chess pattern (TCP). The features are extracted from the sub-bands of the ECG signal using the TCP. The MIT-BIH and St. Petersburg datasets were used for system evaluation using k-NN and SVM classifiers. The researchers in [15] proposed using convolutional block attention modules with ResNet (CBAM-ResNet) to classify cardiac arrhythmias. Time series were converted into Gramian angular summation field (GASF) images to extract rich information from ECG signals. They employed the conditional Wasserstein generative competitive network model and gradient penalty (CWGAN-GP) model to increase the representation of smaller categories. This method was tested on the MIT-BIH arrhythmia database. It achieved a classification accuracy of 99.23%. [PERSON] et al. [16] proposed a new method for the sensitive detection of the Delta wave in an electrocardiogram (ECG) signal using machine learning algorithms and signal peak scanning. The average accuracies were 99.25% and 99.11% for neural networks and k-NNs, respectively. A model called ArrhyMon is presented in [17], which uses the self-considering LSTM-FCN structure to directly detect arrhythmias in ECG signals without additional preprocessing or feature extraction. The average accuracy of the ArrhyMon model on three different publicly available arrhythmia datasets was 99.63%. It is interesting to note that [PERSON] et al. [18] proposed an integrated DenseNet together with bidirectional long short-term memory (BiLSTM) for arrhythmia detection from ECG signals.