## 2 Materials and Methods
### Data Preprocessing  
In the preprocessing stage, we implemented an advanced signal integration strategy that combines clean ECG signals from the MIT-BIH Arrhythmia Database with surface EMG (sEMG) noise signals recorded from chest muscles,a comprehensive comparison of MIT-BIH and sEMG databases is shown in Table 1. To ensure compatibility between the two signal sources as shown in Figure 1, the EMG recordings were first _resampled_ to match the sampling frequency and temporal length of the ECG signals. This resampling procedure preserved the spectral and temporal integrity of the EMG signal while enabling _dimensional alignment_, resulting in both signals having shape \((N,T)\), where \(N\) is the number of signal segments and \(T=360\) is the number of time points per segment. After alignment, the EMG noise \(\hat{n}\in\mathbb{R}^{T}\) was added to the clean ECG signal \(\hat{x}\in\mathbb{R}^{T}\) to generate a physiologically realistic contaminated signal \(\hat{c}\), using the following formulation:  
\[\hat{c}=\hat{x}+\lambda\cdot\hat{n} \tag{1}\]  
Here, \(\lambda\) is a _scaling factor_ that adjusts the noise amplitude to simulate a target signal-to-noise ratio (SNR). Given an SNR value in decibels, \(SNR_{\text{dB}}\), the corresponding linear SNR is computed as:  
\[\text{SNR}=10^{SNR_{\text{dB}}/10} \tag{2}\] \begin{table}
\begin{tabular}{c l l} \hline \hline
**Feature** & **MIT-BIH Database** & **sEMG Database** \\ \hline
**Database Purpose** & \
\begin{tabular}{l} Study of cardiac arrhythmias and other \\ ECG-related conditions. \\ \end{tabular} &
\begin{tabular}{l} Study of human–computer interaction via \\ hand gesture recognition and EMG-based \\ movement control. \\ \end{tabular} \\ \hline
**Records** & 48 records, each lasting 30 min. &
\begin{tabular}{l} Data from 40 participants with an equal \\ gender distribution. \\ \end{tabular} \\ \hline
**Leads** & 2 leads (bipolar limb leads). &
\begin{tabular}{l} 4 leads (surface electrodes placed on forearm \\ muscles). \\ \end{tabular} \\ \hline
**Sample Rate (Hz)** & 360 & 512 \\ \hline
**Signal Type** & ECG & EMG \\ \hline
**Equipment Used** & \
\begin{tabular}{l} Holter monitor or standard ECG equipment. \\ \end{tabular} &
\begin{tabular}{l} BIOPAC MP36 device with Ag/AgCl surface \\ bipolar electrodes. \\ \end{tabular} \\ \hline
**Participants** & \
\begin{tabular}{l} Various individuals (patients and healthy \\ individuals) with arrhythmias. \\ \end{tabular} &
\begin{tabular}{l} 40 participants, balanced gender distribution, \\ simulating various hand gestures. \\ \end{tabular} \\ \hline
**Recording Setup** & \
\begin{tabular}{l} Continuous 30 min ECG recordings of heart \\ electrical activity. \\ \end{tabular} &
\begin{tabular}{l} Hand gestures recorded from forearm muscles \\ during simulation of 10 distinct gestures. \\ \end{tabular} \\ \hline
**Data Collected** & \
\begin{tabular}{l} Includes normal heart rhythms, arrhythmias, \\ and noise due to movement or electrode \\ issues. \\ \end{tabular} &
\begin{tabular}{l} Resting state and various hand gestures (e.g., \\ wrist extension, flexion, grip). \\ \end{tabular} \\ \hline
**Data Accessibility** & \
\begin{tabular}{l} Widely available for benchmarking ECG \\ analysis algorithms. \\ \end{tabular} &
\begin{tabular}{l} Recommended for benchmarking \\ gesture-related datasets and validating \\ machine learning models. \\ \end{tabular} \\ \hline
**Risks/Limitations** & \
\begin{tabular}{l} May include artifacts due to motion or poor \\ electrode contact. \\ \end{tabular} &
\begin{tabular}{l} Variability in muscle activity between \\ participants; EMG noise may interfere with \\ model training. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comprehensive comparison of MIT-BIH and sEMG databases. Figure 1: Time Series of Clean ECG signal, Combined, and EMG Artifact Signals.