## 2 Related Work  
In recent studies, researchers investigated ways to improve ECG classification using deep learning models, most of which were focused on morphological features of the ECG signals. For example, [PERSON] et al. [11] developed a convolutional neural network-based feature extraction method to obtain morphological features from the ECG waveforms for their classification. These CNN-based techniques have shown better performance than classical machine-based learning methods, such as support vector machines. These approaches have been discredited, however, since they fail to capture the latent temporal features of ECGs [1]. Consequently, researchers have tried to bypass this limitation by investigating the use of sequence-based models such as transformer architectures and recurrent neural networks. These have proved much better in regard to data classification by performing better at using the temporal dependencies inherent in ECG signals [12]. Of these, the transformer architecture has lately been found very efficient in the analysis of ECG due to the attention mechanism and the ability to describe long-range dependencies [13]. For instance, [PERSON] et al. [13] proposed a low-dimensional denoising embedding transformer model for the classification of ECGs. In this approach, they leveraged the self-attention mechanism of the transformer, which enables the model to focus on partsof the signal that are relevant. On the other hand, [PERSON] et al. combined rule-based features with neural network methodology for ECG abnormality detection and demonstrated how domain knowledge can be combined with deep learning models to great advantage [14]. Deep learning has further been explored in other applications related to ECG, such as for classification tasks, including representation learning and chronic disease prediction. For example, [PERSON] et al. provides a new deep learning architecture that leverages deep CNNs with advanced mechanisms, such as residual learning and temporal attention, to enhance the detection performance of AF by leaps and bounds [15]. Particularly, there are considerable limitations to the existing methods for deep learning-based ECG classification and non-uniform sampling that put forward a number of motives for developing more advanced neural network-based approaches [16]. Specifically, such methods do not completely help the traditional techniques in capturing the complex temporal dependencies inherent in an ECG signal, which is very important in making correct diagnoses or classifications. Such restrictions of the existing ECG classification and non-uniform sampling techniques in capturing and employing temporal dependencies in ECG lead to the necessity of more sophisticated methods. In this paper, neural network-based models for the optimal selection and reconstruction of ECG signals are projected, overtly modeling the temporal features with motivation through these encounters. This may, in turn, vastly increase the accuracy and consistency of ECG-diagnosis systems, improving patient outcomes in medical situations. Lately, transformers and temporal convolutional neural networks have subjugated the huge potential of optimizing the non-uniform sampling of ECG signals. These models are competent at learning complex representations and relations within the ECG data, which can advance expressive features extracted under different conditions of sampling rates and henceforth be used to significantly improve diagnostic performance and reliability in clinical applications. Although there are still a number of challenges to be surmounted, the integration of deep learning with domain expertise in ECG analysis has returned encouraging outcomes. In particular, the identification and description of ECG waveforms with high precision are crucial in the diagnosis and treatment of cardiovascular disorders. Further research in deep learning models can help realize their full potential in improving ECG-based diagnosis systems.