## 4 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Folds** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & \(\mu\pm\sigma\)** \\ \hline IA\({}_{\text{AMI}}\) (\%) & 100 & 98.79 & 100 & 99.4 & 100 & 100 & 100 & 100 & 100 & 99.81 \(\pm\) 0.40 \\ \hline IA\({}_{\text{ALMI}}\) (\%) & 99.43 & 97.75 & 97.75 & 96.57 & 99.43 & 98.31 & 97.19 & 99.43 & 97.75 & 98.3 & 98.19 \(\pm\) 0.99 \\ \hline IA\({}_{\text{ASMI}}\) (\%) & 97.38 & 95.09 & 100 & 97.37 & 100 & 100 & 99.67 & 98.69 & 99.01 & 100 & 98.72 \(\pm\)1.64 \\ \hline IA\({}_{\text{MI}}\) (\%) & 100 & 99.64 & 100 & 99.28 & 98.93 & 99.64 & 98.57 & 100 & 99.64 & 99.28 & 99.49 \(\pm\) 0.48 \\ \hline IA\({}_{\text{ILMI}}\) (\%) & 99.67 & 99.66 & 99.34 & 99.67 & 99.01 & 99.67 & 99.01 & 99.67 & 100 & 100 & 99.57 \(\pm\)0.34 \\ \hline IA\({}_{\text{IPLMI}}\) (\%) & 99.49 & 100 & 98.98 & 98.48 & 99.49 & 99.49 & 98.48 & 100 & 98.98 & 98.48 & 99.18 \(\pm\) 0.59 \\ \hline OA(\%) & 99.72 & 99.09 & 99.44 & 99.23 & 99.37 & 99.65 & 98.88 & 99.58 & 99.37 & 99.44 & 99.37 \(\pm\) 0.25 \\ \hline Kappa & 0.990 & 0.978 & 0.993 & 0.979 & 0.993 & 0.994 & 0.986 & 0.994 & 0.991 & 0.993 & 0.989 \(\pm\) 0.006 \\ \hline \end{tabular}
\end{table} 
Table 8: Classification results obtained for MI localization using proposed MMDCNN model with 10-fold CV. modes of high-pass filtered VCG signals composed with the MMDCNN classifier. Moreover, we have also evaluated the classification results of the MMDCNN classifier using all modes of VCG signal with leave one out (LOO) CV strategy. The VCG beats of one recording are considered during testing of the MMDCNN model, whereas the VCG beats of all other VCG recordings are used to train the MMDCNN classifier. The same procedure is applied to all VCG recordings, and it can also be interpreted as a 171-fold CV strategy. The LOO CV or pre-recording-based MI detection results are shown in Figure 7. It is observed that out of 172 VCG recordings, 114 recordings are correctly classified with 100% accuracy. The OA value obtained using MMDCNN classifier with LOO CV strategy is 87.65%. We have also formulated the seven-class classification scheme as (Healthy vs. AMI vs. ALMI vs. AMI vs. ILMI vs. IPLMI) using MMDCNN classifier with all modes of VCG signals. The seven class classification results obtained using the MMDCNN model are shown in Table 11. It can be observed that for healthy, ALMI, IMI, and IPLMI classes, the IA values are 69.87%, 83.22%, 82.61%, and 41.03%, respectively. The OA value of MMDCNN classifier obtained is 81.48%, which is less than the proposed two-stage MMDCNN model for MI detection and localization. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Mode 1 and Mode 2 from** & **All Modes from High-Pass** \\  & **Each Lead VCG Signals** & **Filtered VCG Signals** \\ \hline IA\({}_{\text{AMI}}\) (\%) & \(100\pm 0\) & \(100\pm 0\) \\ \hline IA\({}_{\text{ALMI}}\) (\%) & \(99.17\pm 0.29\) & \(98.55\pm 2.22\) \\ \hline IA\({}_{\text{ASMI}}\) (\%) & \(93.09\pm 5.16\) & \(99.26\pm 0.27\) \\ \hline IA\({}_{\text{IMI}}\) (\%) & \(99.30\pm 0.28\) & \(100\pm 0\) \\ \hline IA\({}_{\text{ILMI}}\) (\%) & \(99.50\pm 0.19\) & \(99.86\pm 0.14\) \\ \hline IA\({}_{\text{IPLMI}}\) (\%) & \(99.25\pm 0.50\) & \(98.45\pm 2.45\) \\ \hline OA (\%) & \(98.37\pm 1.43\) & \(99.44\pm 0.56\) \\ \hline Kappa & \(0.976\pm 0.014\) & \(0.992\pm 0.006\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification results obtained for MI localization using proposed MMDCNN model with mode selection.