## 4 Results and Discussion
In contrast to IC-U-Net, which is primarily designed for spatial representation in medical imaging tasks, _ECGDnet_ is specifically engineered for sequential signal processing, integrating architectures that proficiently capture long-term dependencies. The documented improvements in performance are largely attributed to its multi-scale feature extraction mechanism, which allows the network to differentiate ECG components from high-frequency EMG artifacts. Moreover, _ECGDnet_ likely utilizes dilated convolutions alongside residual connections, enhancing the receptive field while preserving signal integrity. This design enables efficient noise filtering without compromising the fidelity of clinically significant waveforms. In contrast, IC-U-Net, despite its hierarchical feature extraction capability, may struggle to capture the sequential dependencies inherent in one-dimensional ECG signals, leading to suboptimal noise suppression. Similarly, 1D-ResCNN, while leveraging residual learning to improve feature propagation, lacks explicit mechanisms for addressing transient noise components, which diminishes its effectiveness against EMG artifacts. These performance improvements are further validated through quantitative metrics and visual analysis. The quantitative assessment illustrated in Figure 4a,b confirms that _ECGDnet_ outperforms alternative models, achieving higher PCC and SNR, while maintaining a lower NMSE. Additionally, the visual evaluation in Figure 4c,d provides further evidence of _ECGDnet_'s superiority, demonstrating better preservation of the morphology of PQRST complexes and effective suppression of high-frequency noise, ensuring that the waveform integrity is maintained. Given these performance advantages, _ECGDnet_ proves to be particularly well suited for real-time ECG denoising, a crucial requirement in wearable and remote health monitoring systems. In these applications, it is essential to minimize signal distortion while effectively eliminating noise to ensure accurate clinical interpretation. The ability to process signals efficiently and reliably makes _ECGDnet_ an ideal choice for continuous health monitoring, where robust denoising mechanisms are necessary to maintain signal quality in dynamic environments. LSTMs are indeed a well-established baseline for time-series modeling tasks, including ECG signal processing. However, our study primarily explores the advantages of transformer-based architectures, which offer improved capacity to capture long-range dependencies and parallel processing capabilities, leading to better scalability and performance in denoising applications. Moreover, one of the key objectives of our work is to design a model suitable for deployment on resource-constrained edge devices. LSTM networks, while effective, require sequential data processing and tend to exhibit higher GPU memory usage and latency during inference compared to models optimized for parallelism, such as the proposed architecture. These computational constraints make LSTMs less suitable for low-power or real-time embedded scenarios. Figure 4: **(a)** shows the Validation PCC Curves (**b**) shows the Validation SNRdb Curves (**c**) shows the Validation NMSE Curves (**d**) shows the Validation Loss Curves. While _ECGDnet_ is highly effective in ECG denoising, its capabilities extend far beyond this specific application. Its strengths in capturing long-range temporal dependencies and suppressing high-frequency noise make it a promising tool for various biomedical signal processing tasks. One particularly promising application is in electroencephalography (EEG) artifact removal, where EEG signals frequently suffer from EMG contamination, ocular artifacts (EOG), and power line interference. The multi-scale feature extraction and temporal modeling capabilities of _ECGDnet_ could significantly enhance EEG-based brain-computer interfaces (BCIs), improve epilepsy detection, and optimize sleep monitoring by effectively isolating neural activity from artifacts.