## 5 Results and Discussion
### On the Effect of Various Ensemble Configurations  
As described in Section 4.2, ensemble of depthwise separable CNNs has been experimented with various input sizes and various numbers of ensembles. The result is shown in Table 5, in terms of sensitivity, specificity, positive predictivity, and accuracy, in which the [256 256 256] ensemble configuration shows the best performance. Hence, the [256 256 256] ensemble configuration is used for benchmarking purposes with other algorithms.  
In the ensemble configuration, the CNN model was trained separately. For example, the [256 256] ensemble is trained three times. Therefore, the training time is increased by three times with increased accuracy. As expected, the classification stage (testing time) will be increased around three times as well but within the range of 150 \(\upmu\)s. Hence, we can conclude that the proposed two stage ECG classification requires around 180 \(\upmu\)s for QRS detection and classification using the computing platform described in Section 4.1.  
For more detailed evaluation, the [256 256 256] ensemble confusion matrix is shown in Table 6. The performance assessment of each class using the [256 256 256] ensemble configuration can be seen in Table 7, in which the ECG class is sorted from the largest to the smallest available samples in the MIT-BIH database. As can be seen from the table, fewer samples of data cause inaccurate classification  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Zero Padding**} & \multirow{2}{*}{**Input Size**} & \multirow{2}{*}{**TN**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{_Sn_} & \multirow{2}{*}{_Sp_} & \multirow{2}{*}{_Pp_} & \multirow{2}{*}{_Acc_} \\ \hline
1 & No & 64 & 1,291,190 & 1180 & 84,978 & 1180 & 98.63 & 99.91 & 98.63 & 99.83 \\
2 & No & 128 & 1,291,413 & 957 & 85,201 & 957 & 98.89 & 99.93 & 98.89 & 99.86 \\
3 & No & 256 & 1,291,411 & 959 & 85,199 & 959 & 98.89 & 99.93 & 98.89 & 99.86 \\
4 & Yes & 64 & 1,291,207 & 1163 & 84,995 & 1163 & 98.65 & 99.91 & 98.65 & 99.83 \\
5 & Yes & 128 & 1,291,389 & 981 & 85,177 & 981 & 98.86 & 99.92 & 98.86 & 99.86 \\
6 & Yes & 256 & 1,291,531 & 839 & 85,319 & 839 & **99.03** & **99.94** & **99.03** & **99.88** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of zero padding on various input sizes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Deep Configuration**} & \multirow{2}{*}{**TN**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{_Sn_} & \multirow{2}{*}{_Sp_} & \multirow{2}{*}{_Pp_} & \multirow{2}{*}{_Acc_} & \multirow{2}{*}{**Testing Time**} \\ \hline
1 & [128 64] & 1,291,194 & 1176 & 84,982 & 1176 & 98.64 & 99.91 & 98.64 & 99.83 & 111 \(\upmu\)s \\
2 & [256 128] & 1,291,369 & 1001 & 85,157 & 1001 & 98.84 & 99.92 & 98.84 & 99.85 & 113 \(\upmu\)s \\
3 & [256 256] & 1,291,460 & 910 & 85,248 & 910 & 98.94 & 99.93 & 98.94 & 99.87 & 111 \(\upmu\)s \\
4 & [64 64 64] & 1,291,207 & 1163 & 84,995 & 1163 & 98.65 & 99.91 & 98.65 & 99.83 & 143 \(\upmu\)s \\
5 & [256 256] & 1,291,389 & 981 & 85,177 & 981 & 98.86 & 99.92 & 98.86 & 99.86 & 146 \(\upmu\)s \\
6 & [256 256 256] & 1,291,531 & 839 & 839 & **99.03** & **99.94** & **99.03** & **99.88** & 150 \(\upmu\)s \\
7 & [256 256 64] & 1,291,408 & 962 & 85,196 & 962 & 98.88 & 99.93 & 98.88 & 99.86 & 150 \(\upmu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of various ensemble of depthwise separable CNN configurations.  
In conclusion, the [256 256 256] ensemble achieves the best performance in terms of sensitivity, specificity, positive predictivity, and accuracy.