## 4 Results and Discussion  
Table 4 presents the performance evaluation of four methods [17; 18; 19; 23]: Sym4, 1D-ResCNN, IC-U-Net, and _ECGDnet_, using four metrics: Signal-to-Noise Ratio (SNR), Normalized Mean Squared Error (NMSE), Relative Error (RE), and Pearson Correlation Coefficient (PCC). These metrics assess the quality of denoising, with higher SNR and PCC values and lower NMSE and RE values indicating better performance. The results show that _ECGDnet_ outperforms all other methods, achieving the highest SNR value (19.83 dB), the lowest NMSE (0.9842), the smallest RE (0.0158), and the strongest PCC (0.9924). IC-U-Net follows closely, while Sym4 and 1D-ResCNN show comparatively lower performance. This analysis underscores the effectiveness of modern deep learning architectures such as _ECGDnet_ in achieving superior denoising performance for medical signals like ECG, surpassing traditional and earlier neural network-based methods. Among the models, _ECGDnet_ demonstrates great performance across all metrics. It achieves the highest SNR of 19.83, indicating its ability to produce the cleanest recon \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SNR** & **NMSE** & **RE** & **PCC** \\ \hline Sym4 [23] & 18.91 & 0.9699 & 0.0191 & 0.9809 \\
1D-ResCNN [17] & 18.51 & 0.9779 & 0.0221 & 0.9893 \\ IC-U-Net [19] & 19.33 & 0.9823 & 0.0177 & 0.9921 \\
**ECGDnet** & **19.83** & **0.9842** & **0.0158** & **0.9924** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of ECG denoising methods. The best results for each metric are highlighted in bold. structed signals. Furthermore, it records the highest NMSE of 0.9842, reflecting the model's capability to preserve the original signal structure with high fidelity. The RE for _ECGDnet_ is the lowest (0.0158), signifying minimal reconstruction error, while the PCC of 0.9924 confirms a near-perfect correlation between the reconstructed and original signals. Comparatively, IC-U-Net and 1D-ResCNN show slightly lower performance, with IC-U-Net having an SNR of 19.33 and PCC of 0.9921, while 1D-ResCNN lags behind with an SNR of 18.51 and PCC of 0.9893. The results suggest that the design of _ECGDnet_, incorporating a multi-head attention mechanism, positional embeddings, and Transformer-inspired blocks, significantly enhances its ability to capture temporal dependencies and denoise ECG signals effectively [24]. In contrast, IC-U-Net, with its U-Net-inspired structure, and 1D-ResCNN, relying on residual CNN blocks, provide competitive but less optimal results. This performance comparison underscores the potential of _ECGDnet_ as a State-of-the-Art solution for ECG signal denoising tasks [9; 20; 25; 26]. While the absolute differences may appear numerically small (e.g., _ECGDnet_ vs. _IC-U-Net_), these gains are significant in the context of biomedical signal processing, where even marginal improvements can translate to clinically meaningful differences, such as improved delineation of low-amplitude P-waves or enhanced fidelity in QRS complex morphology critical for tasks like arrhythmia detection or ischemia assessment. For example, a higher Pearson correlation coefficient (PCC) reflects stronger temporal alignment with the original ECG waveform, improving downstream interpretability by cardiologists or automated diagnostic algorithms. Similarly, lower Relative Error (RE) and NMSE indicate reduced distortion, which enhances diagnostic accuracy, particularly in edge cases involving subtle waveform anomalies. The experimental findings indicate that _ECGDnet_ exhibits superior performance compared to IC-U-Net and 1D-ResCNN in the context of ECG denoising, particularly in its ability to capture long-range temporal dependencies and reduce the impact of EMG noise. The enhanced temporal feature extraction and noise suppression capabilities of _ECGDnet_ contribute to its superiority.