## 4 Results and Discussion
Beyond EEG artifact removal, _ECGDnet_'s robust noise suppression capabilities can also be leveraged in photoplethysmography (PPG) signal enhancement. PPG signals, commonly used in wearable health devices and pulse oximetry, are particularly susceptible to motion artifacts and ambient light interference. By applying _ECGDnet_'s advanced denoising techniques, the accuracy of heart rate variability (HRV) analysis, blood oxygen level estimation, and continuous health monitoring could be significantly improved. Moreover, _ECGDnet_ presents potential for adaptation in electromyography (EMG) signal processing, particularly in contexts such as neuromuscular disease diagnosis, prosthetic device control, and rehabilitation monitoring, where precise signal interpretation is crucial. Another significant application lies in the extraction of fetal electrocardiogram (fECG) from maternal ECG, a challenging task due to the low amplitude of fetal signals and the dominance of maternal ECG activity. _ECGDnet_'s deep feature extraction capabilities offer an opportunity to enhance non-invasive fetal monitoring, contributing to advancements in prenatal diagnostics. In addition to these applications, _ECGDnet_ has the potential to improve signal processing in seismocardiography (SCG) and ballistocardiography (BCG), two novel methodologies in cardiovascular monitoring that are highly affected by motion-induced noise. By refining these signals, _ECGDnet_ can enhance heart rate estimation, support comprehensive cardiac function analysis, and contribute to the early detection of cardiovascular diseases. Given _ECGDnet_'s broad applicability across multiple biomedical domains, further architectural optimizations could enhance its effectiveness even further. Future research should explore attention mechanisms and transformer-based models to improve its ability to generalize across diverse physiological time-series data. By integrating such advancements, _ECGDnet_ could be further optimized for real-world biomedical applications, ensuring superior performance across a wide range of health monitoring and diagnostic systems [27; 28; 29; 30]. Figures 5-7 provide a comparative visualization of ECG denoising performance using three different deep learning architectures: IC-U-Net, _ECGDnet_, and 1D-ResCNN. Each plot includes four signals: the clean ECG, the noise, the contaminated ECG, and the denoised ECG output from the respective model, and Figure 8 shows the Comparison Between Real ECG Signal (MIT-BIH) and ECGDnet Denoised Output. In all visualizations, the models show a strong ability to recover the clean signal from the contaminated input, closely approximating the ground truth--particularly in critical regions such as the QRS complex [23; 31; 32]. _ECGDnet_ exhibits a smoother and more precise recovery, maintaining waveform morphology with minimal residual noise. IC-U-Net and 1D-ResCNN also perform effectively, though with minor differences in noise attenuation and waveform preservation. These comparisons underscore each model's capability in managing noise in ECG signals and emphasize _ECGDnet_'s superior denoising fidelity. Figure 5: Comparative Visualization of ECGDnet Denoising Performance Against Ground Truth ECG. Figure 6: Comparative Visualization of 1D-ResCNN Denoising Performance Against Ground Truth ECG. Figure 7: Comparative Visualization of IC-U-Net Denoising Performance Against Ground Truth ECG. The performance metrics shown in Figure 9 highlight the advantages of _ECGDnet_ in the field of TinyML [33; 34], showcasing its suitability for deployment in resource-constrained environments. In terms of central processing unit (CPU) utilization, _ECGDnet_ demonstrates consistently lower usage compared to IC-U-Net and 1D-ResCNN, indicating reduced energy consumption--an essential factor for edge devices operating on limited power [35; 36; 37]. Figure 8: Comparison Between Real ECG Signal (MIT-BIH) and ECGnet Denoised Output Illustrating High-Fidelity Reconstruction.