# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 3 Results and Discussion
### Accuracy Evolution of Generated E1 Waveform  
The characteristics of E1 waveforms included upward peaks, relatively low voltage levels, and frequencies ranging from 4 to 7.5 Hz. As shown in Figure 4, four randomly selected E1 waveforms generated with Epoch = 2000 are presented. The generated waveforms in Figure 4 exhibited typical E1 waveform features. In Figure 4a, the waveform showed upward peaks with four prominent peaks, and the waveform length was 1 s, consistent with the frequency characteristics of E1 waves. The most prominent feature was observed in Figure 4c, where the upward curve of the waveform peak was nearly vertical, followed by a slightly descent, with a frequency ranging from 4 to 7.5 Hz, and the tail of the peak exhibited a certain shape. Consistent with Figure 3, the SVD plot of the generated data showed a more balanced and widespread distribution with Epoch = 2000. Therefore, considering the MMD indicator, SVD plot, and the corresponding classification accuracy of Epochs, the waveforms generated from training at Epoch = 2000 were selected as the required samples for subsequent experiments.  
The visual inspection accuracy of the generated data was further analyzed (as shown in Table 2). The visual inspection accuracy rates of the three experts were 40%, 42.9%, and 40%, respectively, indicating that it was difficult for experts to distinguish between real and generated data. Additionally, it was found that the average rating of the generated data was higher than that of the real data, suggesting that the probability of generated data being judged as real was higher than that of real data. This indicates that the generated data can be used as a supplement to real data. Furthermore, the inspection results of the three experts were consistent for 113 samples (out of 175 samples), achieving a consistency rate of 64.6%, further validating the effectiveness and usability of the generated data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experts**} & \multirow{2}{*}{**Labels**} & \multicolumn{4}{c}{**Expert Inspection**} & \multicolumn{4}{c}{**Results**} \\ \cline{3-8}  & & **T1** & **F1** & **Total Number** & **ACCd** & **TPRd** & **FPRd** \\ \hline Expert A & T & 45 & 30 & 75 & 40\% & 61\% & 76\% \\  & F & 74 & 26 & 100 & 42.9\% & 67\% & 75\% \\ \hline Expert B & T & 48 & 27 & 75 & 42.9\% & 67\% & 75\% \\  & F & 77 & 23 & 100 & 40\% & 65.7\% & 76\% \\ \hline Expert C & T & 46 & 29 & 75 & 40\% & 65.7\% & 76\% \\ \hline \hline \end{tabular} Note: T rows and T1 column: The number of E1 that were originally true and judged to be true, represented by TT1. T rows and F1 column: The number of E1 that were originally genuine but were determined to be forged, represented by TFI. F rows and T1 column: The number of E1 that were originally forged but judged to be genuine, represented by FT1. F rows and F1 column: The number of E1 that were originally forged and judged as forged, represented by FT1. ACCd, TPRd, and FPRd were calculated by Equation (8), Equation (9), and Equation (10), respectively.  
\end{table}
Table 2: Expert visual inspection results of generated E1 waveforms.  
Figure 3: The SVD graphs of DCGAN with different Epoch values: (**a**) Epoch = 500, (**b**) Epoch = 1000, (**c**) Epoch = 2000, and (**d**) Epoch = 4000.