## 5 Conclusions and Limitations  
ConclusionA potent approach for the reconstruction of ECG signals with TCN and the transformer model has been proposed. The models demonstrate non-uniform sampling situations of ECG signals, reducing reconstruction errors while retaining vital diagnostic information. Due to its convolutional layers, the TCN model is able to process long-range temporal dependencies and optimize the selection of time points for sampling. Contrarily, the transformer model uses an attention mechanism to capture complex temporal patterns, increasing orientation with the original ECG signals. Experimental results of different sampling rates demonstrate that the TCN model performed better at higher sampling rates--for instance, producing an MSE of 0.0174 and a Pearson's r of 0.7661 at optimum settings. However, the transformer model proved to be efficient in keeping high accuracy, specifically at low sampling rates. It kept the MSE as low as 0.0005, with a Pearson's r value of up to 0.9805, thereby keeping the integrity of the signal intact. Comparative analysis of the state-of-the-art models proves that the methods proposed in this paper outperform existing techniques both in accuracy and reliability. At optimal lower sampling rates, the transformer model achieved an accuracy of 98.05% with a very high Pearson's r of 0.9805 and 0.9904 at optimal higher sampling rates. The TCN model also showed significant improvements in the alignment and fidelity of the signal, which would particularly be useful in applications supporting higher sampling rates. These results further confirm that both TCN and transformer models provide substantial improvement in ECG signal reconstruction; each has different advantages depending on the sampling rate and the application requirements. In the near future, efforts will be focused on investigating the potential of these models and extending applications to arrhythmia detection techniques. Proposed models hold supreme clinical relevance for better precision and dependability in the processing of ECG signals. This degree of accuracy is vital in detecting slight changes in heart rhythms that otherwise might go undetected by traditional methods of diagnosis, as in cases of arrhythmia or myocardial infarction. Additionally, these models overcome the limitations of uniform sampling and conventional CNNs, which could have missed certain temporal patterns, thereby assuring the integrity of the ECG signal even in the presence of noise. The robustness of the proposed models to such instabilities makes them suitable for ICU continuous monitoring or outpatient ambulatory monitoring. This also significantly impacts telemedicine, whereby reconstruction of ECG signals from models at different sampling rates allows high-fidelity ECG data to be transmitted across limited bandwidths without loss of diagnostic quality, simplifying remote patient monitoring. Such a capacity could be enormously helpful in managing chronic cardiovascular diseases in underserved or rural areas where convenience to specialized care is not to the mark. Furthermore, the model's capability to adaptively sample and reconstruct ECG signals could be connected to develop personalized diagnostic tools. Patients with identified cardiac conditions could have their ECG data managed with models optimized for noticing specific incongruities, thereby refining continuing care. Similarly, wearables prepared with TCN or transformer models could deliver consistent data for long-termhealth monitoring, notifying both patients and healthcare providers of probable issues prior to them becoming perilous. Reduction in diagnostic errors allows the models to not only advance patient outcomes but also facilitate the proficiency of healthcare delivery. Their successful application thus opens the way for further studying of non-uniform sampling methods and ways of integrating them into other diagnostic modalities.