## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Input Type**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Epoch**} & \multicolumn{3}{c}{**Accuracy**} \\ \cline{3-5}  & & & **Training** & **Validation** & **Test** \\ \hline \multirow{4}{*}{MFCC} & VGGNet-19 & 20 & 0.47 & 0.47 & 0.47 \\ \cline{2-5}  & Xception & 20 & 99.72 & 99.45 & 98.84 \\ \cline{2-5}  & ResNet-101 & 20 & 99.46 & 99.13 & 97.84 \\ \cline{2-5}  & DenseNet-201 & 5 & 99.77 & 99.61 & 98.99 \\ \hline \multirow{4}{*}{Spectrogram} & VGGNet-19 & 20 & 98.80 & 97.71 & 96.58 \\ \cline{2-5}  & Xception & 20 & 99.76 & 99.53 & 98.85 \\ \cline{2-5}  & ResNet-101 & 20 & 99.57 & 99.37 & 98.08 \\ \cline{2-5}  & DenseNet-201 & 5 & 98.90 & 98.50 & 97.83 \\ \hline \multirow{4}{*}{Log spectrogram} & VGGNet-19 & 20 & 98.03 & 96.52 & 94.99 \\ \cline{2-5}  & Xception & 20 & 99.18 & 98.50 & 97.16 \\ \cline{2-5}  & ResNet-101 & 20 & 99.45 & 97.95 & 96.79 \\ \cline{2-5}  & DenseNet-201 & 5 & 97.12 & 95.26 & 94.38 \\ \hline \multirow{4}{*}{Mel spectrogram} & VGGNet-19 & 20 & 95.08 & 91.00 & 89.11 \\ \cline{2-5}  & Xception & 20 & 98.68 & 98.18 & 96.92 \\ \cline{2-5}  & ResNet-101 & 20 & 97.80 & 98.42 & 96.85 \\ \cline{2-5}  & DenseNet-201 & 5 & 97.48 & 96.92 & 95.98 \\ \hline \multirow{4}{*}{Scalogram} & VGGNet-19 & 20 & 98.46 & 98.66 & 97.02 \\ \cline{2-5}  & Xception & 20 & 98.75 & 98.82 & 97.33 \\ \cline{1-1} \cline{2-5}  & ResNet-101 & 20 & 99.43 & 98.74 & 97.83 \\ \cline{1-1} \cline{2-5}  & DenseNet-201 & 5 & 99.13 & 98.74 & 97.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracies achieved by various models based on the CNN using different time-frequency representations on PTB-ECG. initial learning rate of 0.0001, the Adam training optimizer, and an epoch varied according to the model. PCA-L2 was executed by entering vectors reshaped from the diminished input image and measuring Euclidean distance. The PCA dimension was 20. Table 4 shows the accuracies achieved by PCA-L2 using time-frequency representations on CU-ECG. PCANet was executed with 4 as the patch size, 4 filters, 4 as the block size, and 0.5 as the block overlap ratio. Table 5 shows the accuracies achieved by PCANet using time-frequency representations on CU-ECG. Table 6 presents the accuracies achieved by the different models based on the CNN using time-frequency representations on CU-ECG. The best accuracies achieved by the time-frequency representations on CU-ECG were 93.82% for DenseNet-201 in MFCC, 94.03% for Xception in spectrogram, 90.65% for Xception in log spectrogram, 88.84% for Xception in mel spectrogram, and 93.49% for Xception in scalogram. The spectrogram input to the Xception model achieved the best accuracy for the CU-ECG dataset. Figure 14 shows the training processes that achieved the best accuracies for the different time-frequency representations applied to CU-ECG. Figure 12: _Cont._  
Figure 12: Training processes that achieved the best accuracies for the time-frequency representations applied to PTB-ECG. (**a**) Accuracy of DenseNet-201 using MFCC; (**b**) loss of DenseNet-201 using MFCC; (**c**) accuracy of Xception using spectrogram; (**d**) loss of Xception using spectrogram; (**e**) accuracy of Xception using log spectrogram; (**f**) loss of Xception using log spectrogram; (**g**) accuracy of Xception using mel spectrogram; (**h**) loss of Xception using mel spectrogram; (**i**) accuracy of ResNet-101 using scalogram; (**j**) loss of ResNet-101 using scalogram. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Type** & **Model** & **Test Accuracy** \\ \hline MFCC & & 90.36 \\ \hline Spectrogram & & 89.41 \\ \hline Log spectrogram & & 86.43 \\ \hline Mel spectrogram & & 87.39 \\ \hline Scalogram & & 90.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracies achieved by PCA-L2 using time-frequency representations on CU-ECG. Figure 13: Structure of CU-ECG.