## 4 Results and Discussions  
The results evaluated using the proposed MMDCNN for MI detection and localization using VCG signals are shown in this section. In Table 3, we have shown the accuracy, sensitivity, specificity, and kappa score values for our proposed MMDCNN model with hold-out CV. Similarly, for MI detection, the accuracy vs. epoch plots for training and validation VCG instances obtained using MMDCNN are illustrated in Figure 5. It is evident from this plot that both training and validation accuracy values are 100% after 10 th epoch. Similarly, we have shown the confusion matrix obtained using the proposed MMDCNN for MI detection using VCG signals for one random hold-out trial in Table 4. The number of false-positive and false-negative values are 1 in the confusion matrix table. The accuracy, sensitivity, specificity, and kappa values for this random hold-out validation are 99.9%, 99.89%, 99.92%, and 0.998, respectively. The average values of accuracy, sensitivity, specificity, and kappa scores over five trial-based random validation are more than 99% (as seen from Table 3). \begin{table}
\begin{tabular}{|c c c c c c c|} \hline \hline  & \multicolumn{3}{c|}{Hold-out (MI Detection)} & \multicolumn{3}{c|}{10-fold (MI Detection )} \\ \hline Parameters & Optimizer & Batch size & Epochs & Learning rate & Batch size & Epochs & Learning rate \\ \hline Values & Adam & 1024 & 15 & 0.0001 & 1024 & 15 & 0.0001 \\ \hline  & \multicolumn{3}{c|}{Hold-out (MI Localization)} & \multicolumn{3}{c|}{10-fold (MI Localization )} \\ \hline Parameters & Optimizer & Batch size & Epochs & Learning rate & Batch size & Epochs & Learning rate \\ \hline Values & Adam & 1024 & 15 & 0.00004 & 256 & 15 & 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyper-parameters used MI detection and localization using our proposed MMDCNN model. Figure 4: Proposed MMDCNN model to detect and localize MI using VCG beats. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Predicted**} \\ \cline{2-4}  & \multicolumn{1}{c}{**Healthy**} & \multicolumn{1}{c}{**MI**} \\ \hline \multirow{2}{*}{Actual} & Healthy & 978 & 1 \\ \cline{2-4}  & MI & 1 & 1390 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for one-trail of hold-out CV for MI detection. Figure 5: Graphs of accuracy vs. epochs of multi-channel multi-scale deep CNN obtained using training and validation VCG instances for MI detection. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Folds** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Value (\(\mathbf{\mu\pm\sigma}\))** \\ \hline Accuracy (\%) & 99.7 & 100 & 99.95 & 100 & 99.74 & 99.95 & 100 & 100 & 100 & 100 & 99.93 \(\pm\) 0.11 \\ \hline Sensitivity (\%) & 100 & 100 & 100 & 100 & 99.38 & 100 & 100 & 100 & 100 & 99.93 \(\pm\) 0.19 \\ \hline Specificity (\%) & 99.49 & 100 & 99.99 & 100 & 100 & 99.92 & 100 & 100 & 100 & 100 & 99.94 \(\pm\) 0.16 \\ \hline Kappa & 0.993 & 1 & 0.999 & 1 & 0.994 & 0.999 & 1 & 1 & 1 & 1 & 0.998 \(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results obtained using multi-channel multi-scale deep CNN with 10-fold CV. The confusion matrix obtained using one random trial-based hold-out validation for MI localization with second stage MMDCNN is shown in Table 6. Similarly, we have shown the accuracy vs. epoch plots for training and validation of multi-scale VCG tensor instances in Figure 6. It can be observed from these plots that both training and validation accuracy values obtained are more than 99% after 10 th epoch using the second stage MMDCNN model. It can be seen from Table 6 that the number of true positives for AMI, IMI, ALMI, ASMI, ILMI, and IPLMI classes are obtained as 162, 284, 185, 287, 301, and 201, respectively. Three multi-scale VCG tensor instances, which belong to IMI, are classified as ALMI class.