## 3 Results and Discussion
### ECG Denoising  
To evaluate the effectiveness of the proposed model in ECG denoising, this study conducted a series of experiments and analyzed the results with two scenarios of data distribution, namely shuffled and non-shuffled. In the shuffled scenario, the training and validation datasets are separated randomly using a fixed seed to ensure the experimental results are reproducible. Conversely, in the non-shuffled scenario, the training and validation datasets are separated sequentially. This categorization is performed to verify the model's robustness to varying data distributions. Three feature engineering (FE) methods are employed during the padding process at the ECG signal noise removal stage: raw padding, amplitude, and amplitude-isoelectric. Padding is a technique used to ensure that \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Aim**} & \multicolumn{2}{c}{**ECG Records**} \\ \cline{2-4}  & & **Training Set** & **Validation Set** & **Testing Set (Unseen)** \\ \hline NSTDB & Denoising & Noise1 (BW) & - & Noise 2 (BW) \\ \hline \multirow{5}{*}{QTDB} & \multirow{5}{*}{Denoising and delineation (unseen)} & \multirow{5}{*}{Remaining records} & \multirow{5}{*}{Remaining records} & MIT-BIH Arrhythmia (sel123 and sel233), MIT-BIH ST Change (sel302 and sel307), MIT-BIH Supraventricular Arrhythmia (sel820 and sel853), MIT-BIH Normal Sinus Rhythm (sel16420 and sel1679), European ST-T database (sele0106 and sele0121), Sudden death patients (sel32 and sel49), MIT-BIH Long-Term ECG (sel14046 and sel15814). \\ \hline \multirow{5}{*}{LUDB} & \multirow{5}{*}{Delineation} & \multirow{5}{*}{Remaining records} & \multirow{5}{*}{Remaining records} & Sinus rhythm (2), Sinus bradycardia (2), Sinus arrhythmia (2), Sinus tachycardia (70), Irregular sinus rhythm (108), Atrial Fibrillation (8, 38, 44, 51, 83, 88, 93, 95, 96, 101, 110, 112, 129, 173), and Atrial Flutter (35, 52, 103). \\ \hline \end{tabular}
\end{table} 
Table 2: The record distributions of NSTDB, QTDB, and LUDB. all input sequences have the same length, which is essential for accurate feature extraction and ECG signal delineation. This helps in preserving the morphology of the ECG signal, which is critical for diagnosing heart conditions. The performance of various model noise removal processes is depicted in Table 3.  
Table 3 indicates the highest SNR improvement is achieved with the amplitude-isoelectric line centered padding method with the shuffle scenario. It can be observed that the model of ECG signal denoising demonstrated outstanding performance with the following average evaluation metrics and standard deviations (Stdev): SSD (4.81 \(\pm\) 7.29), SNR (23.46 \(\pm\) 7.22), MAD (0.43 \(\pm\) 0.30), PRD (53.05 \(\pm\) 38.42), and COS_SIM (0.90 \(\pm\) 0.10). The shuffled scenario approaches help in understanding the model's generalization capa  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**FE**} & \multirow{2}{*}{ \begin{tabular}{c} **Metrics** \\ **Evaluation** \\ \end{tabular}  } & \multicolumn{3}{c}{**Mean \(\pm\) Standard Deviation**} \\ \cline{3-6}  & & & **Training** & **Validation** & **Testing** \\ \hline \multirow{8}{*}{Raw} & \multirow{8}{*}{Raw} & SSD & 1665.42 \(\pm\) 5994.15 & 1399.01 \(\pm\) 2697.22 & 163.23 \(\pm\) 370.85 \\ \cline{3-6}  & & SNR & 14.17 \(\pm\) 10.  
bilities when exposed to diverse data. Shuffling ensures that the model encounters a wide range of examples during each epoch, enhancing its learning process. This diversity in training examples typically leads to better overall performance metrics. The sample plots of ECG signals in shuffled and non-shuffled scenarios with raw padding, amplitude, and amplitude-isoelectric methods are presented in Figures 4-6, respectively. The isoelectric line is the baseline of the ECG signal, representing periods of electrical inactivity in the heart.