## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Type** & **Model** & \multicolumn{2}{c}{**Accuracy**} \\ \cline{3-3}  & & **Training** & **Test** \\ \hline MFCC & & 96.05 & 89.88 \\ \hline Spectrogram & & 95.99 & 90.88 \\ \hline Log spectrogram & & 96.03 & 89.94 \\ \hline Mel spectrogram & & 95.99 & 89.10 \\ \hline Scalogram & & 95.92 & 92.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracies achieved by PCANet using time-frequency representations on CU-ECG. \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{3-6}  & & & **Training** & **Validation** & **Test** \\ \hline \multirow{4}{*}{MFCC} & VGGNet-19 & 20 & 93.35 & 90.24 & 86.21 \\ \cline{2-6}  & Xception & 20 & 96.07 & 95.56 & 93.62 \\ \cline{2-6}  & ResNet-101 & 20 & 95.56 & 95.15 & 92.46 \\ \cline{2-6}  & DenseNet-201 & 5 & 96.17 & 95.08 & 93.82 \\ \hline \multirow{4}{*}{Spectrogram} & VGGNet-19 & 20 & 91.95 & 91.99 & 87.87 \\ \cline{2-6}  & Xception & 20 & 96.01 & 95.62 & 94.03 \\ \cline{2-6}  & ResNet-101 & 20 & 95.69 & 94.48 & 91.64 \\ \cline{2-6}  & DenseNet-201 & 5 & 94.71 & 94.01 & 91.83 \\ \hline \multirow{4}{*}{Log spectrogram} & VGGNet-19 & 20 & 93.76 & 91.92 & 87.56 \\ \cline{2-6}  & Xception & 20 & 95.26 & 93.80 & 90.65 \\ \cline{2-6}  & ResNet-101 & 20 & 94.01 & 92.53 & 89.29 \\ \cline{2-6}  & DenseNet-201 & 5 & 93.92 & 92.79 & 88.75 \\ \hline \multirow{4}{*}{Mel spectrogram} & VGGNet-19 & 20 & 88.80 & 89.29 & 86.28 \\ \cline{2-6}  & Xception & 20 & 92.65 & 91.99 & 88.84 \\ \cline{2-6}  & ResNet-101 & 20 & 91.55 & 91.45 & 88.42 \\ \cline{2-6}  & DenseNet-201 & 5 & 90.76 & 90.03 & 86.77 \\ \hline \multirow{4}{*}{Scalogram} & VGGNet-19 & 20 & 93.55 & 93.60 & 90.56 \\ \cline{2-6}  & Xception & 20 & 95.68 & 95.56 & 93.49 \\ \cline{2-6}  & ResNet-101 & 20 & 95.71 & 94.75 & 92.11 \\ \cline{2-6}  & DenseNet-201 & 5 & 95.58 & 95.08 & 92.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracies achieved by various models based on the CNN using time-frequency representations on CU-ECG. Figure 14: _Cont._  
Figure 14: Training processes that achieved the best accuracies for the time-frequency representations applied to CU-ECC. (**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 using Xception mel spectrogram; (**h**) loss of Xception using mel spectrogram; (**i**) accuracy of Xception using scalogram; (**j**) loss of Xception using scalogram. The MIT(Massachusetts Institute of Technology)-BIH(Beth Israel Hospital) arrhythmia database consists of ECG signals from 48 subjects. The data had a sample rate of 360 Hz and a 10 s length. Most data had the MLII (Modified Limb lead II) signal except two subjects. Therefore, MLII data from 46 subjects were used for biometrics. Noise was eliminated from the signal, and the R peaks were detected by the Pan-Tompkins algorithm. The number of detected R peaks was small because the length of MIT-BIH arrhythmia had a short length signal. The R peak points from 46 people were compared and adjusted to ensure that the maximum number of data per class was five. Data were captured with 289 samples putting the R peak as the center. The size of the MIT-BIH-arrhythmia-ECG database was \(289\times 230\) (5 data/class \(\times\) 46 classes). The row represents the dimension of the data, while the column indicates the number of data. To train the CNNs, MIT-BIH-arrhythmia-ECG was divided into the training and test datasets with ratios of 0.6 and 0.4, respectively. The sizes of the training and test datasets were \(289\times 138\) and \(289\times 92\), respectively. The CNNs were trained using a mini-batch size of 10, an initial learning rate of 0.0001, a max epoch of 5, and the Adam training optimizer.