## 3 Quantitative comparison of methods  
Since the key emphasis of the proposed models lies in enhancing the accuracy of ECG signals using non-uniform sampling and deep learning-based models, compressive sensing is an essential baseline for efficiency evaluation. The comparison performance of existing CS-based methods with the proposed Transformer and TCN models (Table 4) is used to show how modern neural networks can further improve signal fidelity while maintaining or even exceeding efficacy and accuracy achieved by the traditional techniques. The accuracy results, in particular of the transformer model, show values on par with or exceeding [PERSON] et al.'s outcomes [31].  
Computational efficiency is emphasized using neural networks. Though direct execution times are not compared, emphasis on iterative gradient descent optimization suggests a balance in computational complexity and accuracy.  
The TCN model's performance metrics clearly improve with the number of samples, demonstrating the need for more samples in order to fully capture the temporal dependencies and intricate details present in the ECG signal. The model's capacity to improve the fidelity of the reconstructed signal with additional samples is demonstrated by the steady decline in mean squared error (MSE) and root mean square error (RMSE), as well as the rising trend in Pearson's correlation coefficient (r). The performance levels out at about 120 samples, indicating that this quantity offers the best balance for precise signal reconstruction. This suggests that although the TCN model gains a lot from larger sample sizes, it needs a large number of samples to function at its best, which may be taken into account in situations where sampling resources are limited.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Papers** & **Algorithms** & **Signal-to-Noise Ratio** & \begin{tabular}{c} **Percentage** \\ **Root-Mean-Square** \\ **Difference** \\ \end{tabular} & **Compression Ratio** \\ \hline [PERSON] et al. [32] & RLS and RLS-DL & 39 dB & Not explicitly provided & 86\% \\ \hline [PERSON] et al. [33] & \
\begin{tabular}{c} MMB-IHT and \\ MMD-CoSaMP \\ \end{tabular} & 25–28 dB & 2.98–25.58\% & CR up to 8.0 \\ \hline [PERSON] et al. [31] & TPMP &
\begin{tabular}{c} Low PRD indicates \\ high-quality signal \\ reconstruction. \\ \end{tabular} & 0.72–0.86\% & CR between 3.5 and 8.0 \\ \hline Proposed methods & TCN Model & & 1.61\% at CR = 4.16 (120 sps) & CR between 4.16 and 27.8 (18 sps) & 27.8 (18 sps) & 26.8 between 4.16 and 27.8 Does not focus on compression, instead minimizing data points without affecting quality \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of proposed methods against state-of-the-art non uniform ECG representations.  
Conversely, the transformer model exhibits a noteworthy ability to attain elevated precision in ECG signal reconstruction with a reduced amount of data. It continues to retain high standards of accuracy and fidelity at lower sample counts, but its performance metrics show noticeable gains with rising sample counts. The effectiveness of the transformer model in capturing the key characteristics of the ECG signal is demonstrated by its ability to consistently attain Pearson's r values above 0.9, even with fewer samples. This implies that the transformer model is more effective at delivering precise signal reconstruction by making better use of the samples that are available, which makes it especially appropriate for applications where sampling rate limits are prevalent. Its performance reaches its highest point at 120 samples, with a unique 0.0005 MSE and 0.9904 Pearson's r, demonstrating its robustness and reliability for medical diagnostics and monitoring applications.