## 4 Results
### Statistical Significance of the Results  
We ran statistical significance tests to assess the performance differences between models across various datasets (real, synthetic, and real + synthetic). The method employed was the Wilcoxon signed-rank test. The Wilcoxon signed-rank test, a non-parametric test, was used for pairwise comparisons. It is particularly effective for comparing matched data when the assumption of normality cannot be guaranteed. The test identifies whether differences between paired observations are symmetric to zero, providing \(p\)-values to evaluate statistical significance. The results of the statistical significance tests are summarized in Table 7 and further visualized in Figures 10 and 11. **1. Real data comparisons:**  
* GPT-2 + SVM vs. GPT-2 + RF: the difference in accuracies (90.84% vs. 88.14%) was not statistically significant (\(p\) > 0.05), suggesting similar performance for these two models on real data. * GPT-2 + SVM vs. WGAN-GP + CNN: WGAN-GP + CNN significantly outperformed GPT-2 + SVM (92% vs. 90.84%, \(p\) < 0.05). * GPT-2 + RF vs. WGAN-GP + CNN: the WGAN-GP + CNN model significantly surpassed GPT-2 + RF (92% vs. 88.14%, \(p\) < 0.01), indicating its superior reliability. **2. Synthetic data comparisons:**  
* GPT-2 + SVM vs. GAN + CNN: GAN + CNN significantly outperformed GPT-2 + SVM (85.78% vs. 66.88%, \(p\) < 0.01). * GPT-2 + SVM vs. WGAN-GP + CNN: WGAN-GP + CNN achieved markedly higher accuracy (98.42% vs. 66.88%, \(p\) < 0.01). * GAN + CNN vs. WGAN-GP + CNN: WGAN-GP + CNN was significantly better than GAN + CNN (98.42% vs. 85.78%, \(p\) < 0.01), reinforcing the robustness of the WGAN-GP model for synthetic data generation. **3. Real + synthetic data comparisons:** \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Classifier** & **Real Data** & **Synthetic Data** & **Real + Synthetic Data** \\ \hline \multirow{2}{*}{WGAN-GP} & Support vector & \multirow{2}{*}{98} & \multirow{2}{*}{95.8} & \multirow{2}{*}{97} \\  & machine (SVM) & & & \\ \multirow{2}{*}{WGAN-GP} & Random factor & & & \\  & (RF) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ECG classification of real, synthetic, and combined datasets using SVM and RF classifiers. * GPT-2 + SVM vs. GAN + CNN: the performance difference between GPT-2 + SVM and GAN + CNN was not statistically significant (93.71% vs. 94.11%, \(p\) > 0.05). * GPT-2 + SVM vs. WGAN-GP + CNN: WGAN-GP + CNN significantly outperformed GPT-2 + SVM (98.45% vs. 93.71%, \(p\) < 0.01). * GAN + CNN vs. WGAN-GP + CNN: WGAN-GP + CNN significantly outperformed GAN + CNN (98.45% vs. 94.11%, \(p\) < 0.01). The bar chart shown in Figure 10 provides a visual comparison of model accuracies across the datasets, with statistical significance denoted as * (_p_ < 0.05) or ** (_p_ < 0.01). It highlights the consistently superior performance of WGAN-GP + CNN, particularly for synthetic and real + synthetic data. The heatmap in Figure 11 illustrates the pairwise _p_-values from the Wilcoxon signed-rank test, where darker shades represent lower _p_-values, indicating stronger statistical significance. The clear contrast between WGAN-GP + CNN and other models reinforces its performance advantages. These results demonstrate the effectiveness of WGAN-GP + CNN, particularly for scenarios involving synthetic and augmented datasets. Its superior accuracy and statistical significance highlight its potential for applications requiring robust and reliable classification models.