## 4 Results and Discussion
### Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Datasets** & **Algorithm** & \begin{tabular}{c} **Train/** \\ **TestRatio** \\ \end{tabular} & \
\begin{tabular}{c} **No. of** \\ **Classes** \\ \end{tabular} & **Precision** & **Recall** &
\begin{tabular}{c} **F1-** \\ **Score** \\ \end{tabular} & **Accuracy** \\ \hline
[13] & 2021 & MIT-BIH AF & CNN + LSTM & 90/10 & 4 & - & 97.87 & - & - \\
[14] & 2022 & St. Petersburg & DW-CMT + TCP+SVM & 90/10 & 4 & 97.80 & 97.80 & 97.80 & 97.80 \\
[42] & 2022 & MIT-BIH AF & CNN + Transformers & 90/10 & 4 & 95.38 & 92.51 & 93.88 & 99.49 \\ \hline
[10] & 2017 & MIT-BIH & 9 layers CNN & 90/10 & 5 & 97.86 & 96.71 & 97.28 & 94.03 \\
[12] & 2021 & MIT-BIH & CNN + GA & 80/20 & 5 & 95.80 & 99.70 & 89.70 & 98.00 \\
[14] & 2022 & MIT-BIH & DW-CMT + TCP + kNN & 90/10 & 5 & 95.18 & 98.51 & 96.69 & 96.60 \\
[15] & 2022 & MIT-BIH & CBAM-ResNet & 80/20 & 5 & 99.13 & 97.50 & 98.29 & 99.23 \\
[19] & 2024 & MIT-BIH & FT + CNN-LSTM & 80/20 & 5 & 97.30 & 97.40 & 97.30 & 97.40 \\
[36] & 2022 & MIT-BIH & CNN + RF & 80/20 & 5 & 76.00 & 78.00 & 74.00 & 96.00 \\
[37] & 2023 & MIT-BIH & CNN & 90/10 & 5 & 92.86 & 92.41 & 92.63 & 98.63 \\
[38] & 2021 & MIT-BIH & CWT + CNN & 50/50 & 5 & 70.75 & 67.47 & 68.76 & 98.74 \\
[43] & 2018 & MIT-BIH AF & SWT + DCNN & 90/10 & 5 & - & 98.79 & - & 98.63 \\
[44] & 2022 & MIT-BIH & ResNet + BiLSTM & 80/20 & 5 & 92.23 & 91.23 & 91.69 & 99.20 \\
[45] & 2022 & MIT-BIH & CNN + TTM & 90/10 & 5 & 48.10 & 70.60 & 57.12 & 96.36 \\
[46] & 2022 & MIT-BIH & SE-ResNet & 90/10 & 5 & 93.87 & 93.78 & 93.82 & 99.61 \\
[47] & 2023 & MIT-BIH & RPM + Gam-Resnet18 & 80/20 & 5 & 98.76 & 98.90 & - & 99.30 \\ Ours & 2024 & MIT-BIH & 3-Channel CNN + GRU & 80/20 & 5 & 99.76 & 99.76 & 99.76 & 99.76 \\ \hline
[48] & 2022 & MIT-BIH + BIDMC & LSTM & 80/20 & 3 & - & - & 96.00 \\
[49] & 2021 & MIT-BIH + BIDMC & CWT + AlexNet & - & 3 & 97.70 & 97.80 & 97.70 & 97.8 \\
[50] & 2022 & MIT-BIH + BIDMC & CWT + CNN + LSTM & 90/10 & 3 & 98.00 & 98.00 & 97.30 & 98.90 \\
[51] & 2023 & MIT-BIH + BIDMC & ResNet50 & 80/20 & 3 & 99.20 & 99.20 & 99.20 & 99.20 \\ Ours & 2024 & MIT-BIH + BIDMC & 3-Channel CNN + GRU & 80/20 & 3 & 99.80 & 99.80 & 99.80 & 99.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the proposed model against other models from the literature. the best performer, delivering 99.80% across all metrics. This demonstrates the model's robustness in both three-class and five-class arrhythmia detection, achieving near-perfect results and surpassing previous state-of-the-art techniques. While the proposed method appears to have potentially greater computational complexity compared to existing approaches, this complexity can be justified by the noticeable improvement in performance. With advances in modern hardware, such as more powerful processors (e.g., GPUs and TPUs) and specialized hardware for machine learning, the relative difference in computation time between the proposed method and less complex approaches may be reduced to mere milliseconds. This minimal difference in runtime, particularly on state-of-the-art hardware, becomes a negligible trade-off when considering the performance gains. Moreover, in many practical applications, small improvements in accuracy or reliability can have significant impacts, making the slight increase in computational complexity well worth the benefits, especially in high-throughput systems where precision matters.