## 6 Conclusions  
This study assessed four techniques for ECG signal denoising: a traditional method, Sym4, alongside three deep learning models--1D-ResCNN, IC-U-Net, and _ECGDnet_--emphasizing their effectiveness in reconstructing clean signals from contaminated data. The comparative analysis, utilizing essential performance metrics including Signal-to-Noise Ratio (SNR), Normalized Mean Squared Error (NMSE), Relative Error (RE), and Pearson Correlation Coefficient (PCC), revealed that _ECGDnet_ surpassed the other methods across all evaluated criteria. Specifically, _ECGDnet_ achieved the highest SNR (19.83), lowest NMSE (0.9842), lowest RE (0.0158), and highest PCC (0.9924), underscoring its superior capability to reduce noise while preserving the physiological fidelity of ECG signals.  
Sym4, a wavelet-based technique, demonstrated the least effective performance, showing limited ability to capture the intricate, non-linear dynamics characteristic of ECG signals--particularly when compared to data-driven deep learning approaches. The superior performance of _ECGDnet_ is attributed to its novel architecture, which incorporates multi-head attention mechanisms and Transformer-inspired positional embeddings to effectively capture long-range temporal dependencies. This makes _ECGDnet_ particularly adept at managing the complex structure of ECG waveforms.  
In contrast, IC-U-Net and 1D-ResCNN, although still effective, showed comparatively lower performance. These limitations are likely tied to the inherent constraints of their U-Net-based and convolutional structures, which may not fully exploit long-range dependencies. While Sym4 maintains the advantage of computational efficiency, it falls short when tasked with the denoising of complex biomedical signals, reaffirming the advantages of deep learning-based, attention-driven architectures.  
In summary, this research underscores the promise of sophisticated attention-based frameworks like _ECGDnet_ for ECG signal denoising and broader biomedical signal processing applications. Future work should focus on optimizing computational efficiency for real-time and resource-constrained deployments, as well as expanding the model's scope to include other physiological signals, multi-modal datasets, or hybrid methodologies that integrate traditional signal processing with modern deep learning techniques.  
Conceptualization, F.-E.B.-B., B.J. and A.E.; methodology, F.-E.B.-B., I.T. and A.E.; software, I.T. and A.E.; validation, F.-E.B.-B., B.J., O.M. and A.E.; formal analysis, I.T. and A.E.; investigation, F.-E.B.-B. and A.E.; resources, O.M. and A.E.; data curation, A.E.; writing--original draft preparation, F.-E.B.-B., B.J. and A.E.; writing--review and editing, I.T., O.M. and A.E.; visualization, A.E.; supervision, F.-E.B.-B., B.J. and A.E.; project administration, B.J. and A.E.; funding acquisition, A.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are openly available in Physionet and Mendeley data [MIT-BIH] [[[https://physionet.org/content/mitdb/1.0.0/](https://physionet.org/content/mitdb/1.0.0/)]([https://physionet.org/content/mitdb/1.0.0/](https://physionet.org/content/mitdb/1.0.0/))].  
**Institutional Review Board Statement:** This study utilized the widely recognized MIT-BIH dataset. Ethical review and approval were waived for this study, due to the use of publicly available datasets.  
**Informed Consent Statement:** This study used publicly available dataset where informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.