## 2 Literature Review  
Numerous studies have been carried out on the application of ML/DL for the analysis of ECG signals [3; 11] and the detection of cardiac arrhythmias [16; 17]. In [18], authors recommended a Residual CNN-GRU-based deep learning model with an attention mechanism for classifying cardiac disorders. In this work, they utilized 24 groups for classification out of 27. The proposed approach attained a test accuracy of 12.2% and obtained 30 th position among 41 teams in the official ranking. The authors of [19] introduced a modified ResNet model that includes numerous basic blocks and four modified residual blocks. The updated model is a 1D-CNN that supports attention across feature maps. The authors employed fine-tuning on the pre-trained model and their proposed algorithm attained a test accuracy of 20.8%. The authors of [20] presented a 1D-CNN with global skip connections to classify ECG signals from 12-lead ECG recordings into numerous classes. The authors have also utilized many preprocessing and learning methods such as a customized loss function, Bayesian threshold optimization, and a dedicated classification layer. Their implemented technique produced an accuracy of 20.2% on the test dataset. The authors of [21] introduced an SE-ResNet model with 34 layers in the DL model to classify cardiac arrhythmias. They assigned different weights to different classes based on their similarity and utilized these weights in metric calculations. The utilized DL model attained a validation accuracy of 65.3% and test accuracy of 35.9%. In [22], the authors presented a hybrid Recurrent Convolutional Neural Network (CRNN) with 49 1D convolutional layers, along with 16 skip connections and one Bi-LSTM layer to detect heart abnormalities using a 12-Lead ECG signal. Utilizing the proposed model with 10-fold cross validation and without preprocessing, the authors achieved 62.3% validation accuracy, and 38.2% test accuracy. The authors of [23] utilized an SE-ECGNet to detect arrhythmias from 12-lead ECG recordings. In their model design, they utilized squeeze-and-excitation networks in each model path and an attention mechanism which learns feature weights corresponding to loss. In this study, the authors achieved 64.0% validation accuracy and 41.1% test accuracy using the proposed model. In [24], the authors presented a deep 1D-CNN model consisting of exponentially dilated causal convolutions in its structure. Their proposed model achieved a challenge score of 0.565 \(\pm\) 0.005 and an AU-ROC of 0.939 \(\pm\) 0.004 using 10-fold cross validation techniques. In this work, they achieved 41.7% accuracy on the test dataset utilizing the proposed model. In [25], the authors introduced an 18-layer residual CNN for arrhythmia classification which has four stages for abnormality classification. The authors have also utilized preprocessing techniques, 10-fold cross validation, and post-training procedure refinement. The proposed approach achieved 69.5% validation score and 42% test accuracy. The authors of [26] utilized a hybrid DL model by integrating a CNN with LSTM along with adversarial domain generalization for the detection of arrythmias from 12-lead ECG signals. In this study, the proposed model obtained 43.7% accuracy on the test dataset. In [27], the authors presented a method for ECG signal classification in which they utilized scatter transform in combination with deep residual networks (ResNets). In their study, the authors obtained 48% test accuracy utilizing their proposed methodology. In [28], the authors designed an SE-ResNet-based DL model which is a variant of the ResNet architecture. In their model design, SE blocks are utilized to learn from the first 10 and 30 s segments of ECG signals. The authors also utilized an external open-source dataset for model validation. To correct and verify the output, they developed a rule-based bradycardia model based on clinical knowledge.