## 4 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Mode Selection**} & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **Kappa** \\ \hline Mode 1 and mode 2 of each & \multirow{2}{*}{\(99.58\pm 0.37\)} & \multirow{2}{*}{\(99.17\pm 0.89\)} & \multirow{2}{*}{\(99.86\pm 0.06\)} & \multirow{2}{*}{\(0.991\pm 0.007\)} \\ lead VCG signals & & & & \\ \hline All modes from high-pass & \multirow{2}{*}{\(99.92\pm 0.02\)} & \multirow{2}{*}{\(99.84\pm 0.05\)} & \multirow{2}{*}{\(100\pm 0\)} & \multirow{2}{*}{\(0.998\pm 0.0005\)} \\ filtered VCG signals & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification results of MMDCNN obtained using selected modes and all modes for the detection of MI. Figure 7: Accuracy values obtained using the proposed MMDCNN classifier for MI detection with leave one out CV strategy. The classification performance of the proposed first stage MMDCNN classifier is compared with the existing techniques for MI detection using VCG signals with a 10-fold CV-based technique. The comparison results are shown in Table 12. The work reported in [15] has computed features from each lead of VCG signal using multi-scale recurrent quantification analysis (MRQA). The Gaussian discriminant analysis (GDA) based classification model has been used to detect MI using MRQA based VCG features. The sensitivity and specificity values of 96.50% and 75% have been obtained in their work. Similarly, in [14], the combination of octant and vector-based features have been obtained using VCG signal. The classification and regression tree (CART) based model has been used for the detection of MI. The classification performance, such as the sensitivity and specificity values of 97.28% and 96%, respectively, are reported. The complex wavelet sub-band features of VCG coupled with the RVM classifier have obtained the sensitivity and specificity values of 98.40%, and 98.66%, respectively, for MI detection [28]. The proposed MMDCNN model has obtained better classification performance than the existing machine learning-based methods for MI detection using VCG signals. The advantages of our proposed MMDCNN based approach are given as follows:  
* A novel two-stage based MMDCNN model is proposed to detect and localize MI using VCG beats;
* The multi-scale analysis of VCG signal is performed using MVMD based multi-variate signal driven approach;
* The approach has demonstrated more than 99% accuracy for MI detection;
* The extraction of raw features from VCG signals are not required using the proposed approach for both detection and localization stages;
* The second stage MMDCNN model successfully classified six types of MI with an accuracy of more than 99%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value (\(\mu\pm\sigma\))** \\ \hline Healthy (\%) & 69.87 \(\pm\) 28.87 \\ \hline IA\({}_{\text{AMI}}\) (\%) & 99.46 \(\pm\) 0.38 \\ \hline IA\({}_{\text{ALMI}}\) (\%) & 83.22 \(\pm\) 10.14 \\ \hline IA\({}_{\text{ASMI}}\) (\%) & 99.63 \(\pm\) 0.25 \\ \hline IA\({}_{\text{IMI}}\) (\%) & 82.61 \(\pm\) 7.93 \\ \hline IA\({}_{\text{ILMI}}\) (\%) & 98.44 \(\pm\) 2.28 \\ \hline IA\({}_{\text{IPLMI}}\) (\%) & 41.03 \(\pm\) 18.18 \\ \hline OA (\%) & 81.48 \(\pm\) 0.63 \\ \hline Kappa & 0.777 \(\pm\) 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Classification results obtained using MMDCNN classifier for seven class classification scheme with hold-out validation.