# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 3 Results and Discussion
### Comparison of Model Accuracies Based on Data Generation  
Following the evaluation of the MMD metric, validation with SVM, and visual inspection, we selected data generated at 2000 epochs as the final training dataset. We then compared the accuracy of the training results with prior results, as shown in Table 3. The experimental outcomes demonstrated that the accuracy of the CNN on the test set increased significantly from 86.3% before data augmentation to 94.2%, an enhancement of 7.9%, making it the most notable among the four classification models. Deep learning models generally require substantial amounts of data for training, and the additional data provided by augmentation enabled these models to better capture the underlying data features, achieving near-optimal accuracy. The test set accuracy of KNN improved by 7.1%, rising from 77.5% to 84.6%; DT improved by 5.8%, from 87.2% to 81.4%; and SVM improved by 6.9%, from 78.7% to 85.6%. These significant improvements can be attributed to data augmentation, which not only increased the diversity of training samples but also enhanced the generalization capabilities of the models, leading to better performance on new data. Further analysis indicated that data augmentation enabled deep learning models to learn data features more comprehensively, thereby reducing the risk of overfitting. Particularly for CNN, the augmented dataset provided a larger pool of training samples, allowing the model to generalize more effectively rather than being constrained by the characteristics  
Figure 4: Generated E1 waveform: (**a**) E1-1, (**b**) E1-2, (**c**) E1-3, and (**d**) E1-4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Without DCGAN**} & \multicolumn{2}{c}{**With DCGAN**} \\ \cline{2-5}  & **Training Set** & **Testing Set** & **Training Set** & **Testing Set** \\ \hline CNN & 87.6 & 86.3 & 95.3 & 94.2 \\ KNN & 78.1 & 77.5 & 85.2 & 84.6 \\ DT & 82.2 & 81.4 & 88 & 87.2 \\ SVM & 78.8 & 78.7 & 87.5 & 85.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification accuracy before and after data augmentation. of the training set. This increased diversity and richness in the data significantly enhanced the model's stability and predictive accuracy. Moreover, the improvements in accuracy for KNN, DT, and SVM on the augmented dataset further validated the effectiveness of data augmentation in addressing data imbalance issues. For high-dimensional data, the increased sample diversity from data augmentation enhanced the accuracy of KNN's neighborhood search; DT benefited from reduced bias due to more diverse samples, improving overall model performance; and SVM achieved better-defined decision boundaries with more support vectors, thereby increasing classification accuracy. In summary, data augmentation not only enhanced the training outcomes of the models but also significantly improved the performance of all classification models on the test set. This validated its effectiveness in resolving data imbalance and improving model generalization. These findings offer robust support and reference for the further optimization and application of deep learning models. Figure 5 illustrates the ROC curves of four classification models for comparing their performance. The KNN model performed best for the A-wave, with an AUC value of 0.9309. This result may have been due to the irregular nature of the A-wave, allowing the DT to adapt well to the complexity and non-linear relationships of the data. All four classifiers achieved good AUC values for the B-wave. For C-wave recognition, KNN and SVM showed relatively good performance. Both PD-wave and E1-wave, which have distinct features, were well classified by all four classifiers. SVM performed the best for G-wave classification, with an AUC of 0.9819. Except for the DT model, the other three classifiers had AUC scores above 0.9. This result may be attributed to the advantage in handling high-dimensional and linearly separable data of SVM.