## 3 Results  
Experimental studies for all MI and normal classes in 15 leads were carried out separately using the same 1D-CNN architecture, without changing the model parameters with the modified data set and the implemented application. In model evaluation, all ECG signals from all leads were partitioned 70% for training, 15% for validation and 15% for testing. Keras library is used with Adam optimizer algorithm and cross loss entropy function. The 1D-CNN model was individually trained for all ECG signals across all MI classes in 15 leads over a 50 epoch period. Table 2 shows a detailed specification of each parameter of the 1D-CNN architecture layer. The model successfully completed the learning process for all ECG signals in a short time (50 epochs). During the training phase, no overfitting problems occurred in the 1D \begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & \multicolumn{1}{c}{**Layer Parameter**} & **Output Shape** \\ \hline Conv 1D & 128 \(\times\) 11, Strides = 1, Input shape = (700, 1), Activation = ReLU & 639 \(\times\) 128 \\ MaxPooling1D & Pool size = 2 & 345 \(\times\) 128 \\ Dropout & Rate = 0.2 & 345 \(\times\) 128 \\ Conv 1D & 128 \(\times\) 13, Strides = 1, Input shape = (700, 1), Activation = ReLU & 333 \(\times\) 128 \\ Flatten & - & 42,624 \\ Dense & 128, Activation = ReLU & 128 \\ Dropout & Rate = 0.2 & 128 \\ Softmax & 11, Activation = Softmax & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CNN Model layer details. Figure 3: 10 MI and Normal classes in lead v2. CNN model. After the training phase, the proposed 1D-CNN model was applied to the test data. The 1D-CNN architectural model is built to provide optimized ECG signal recognition performance of all ECG leads. The proposed architecture only has 2 convolution layers. This due to we want to avoid the vanishing gradient problem. Besides in every convolutional layer we generate 128 unique features from ECG signal. Moreover, we use single stride to generate more unique and detailed feature. Moreover, we also use dropout layer in fully connected layer to avoid overfitting. The block representation of proposed 1D-CNN model is summarized in Figure 4.  
ECG signals 10 MI and Normal class with 700 samples are input into this model layer. They pass through hierarchically arranged convolution layers and turn into feature maps of different sizes. In this model, the dropout technique is used to avoid overfitting while the model is in training. The model will check all training data at each epoch and the model will record training data if the selected epoch number is too large. Table 3 show the performance of the trained 1D-CNN model at the end of the 50 th epoch on test data in 15 ECG leads. The proposed 1D-CNN model recognizes test data for all vector groupings in all MI and Normal classes with a testing accuracy of more than 99.5% for each ECG lead. From Table 3, it can be seen that the highest classification performance results for the 10 MI classes and normal classes is in lead v2 with a testing accuracy value of 99.87% while the lowest accuracy level is in lead aVR with an accuracy of 99.58%. Moreover, the test set performance was slightly better than train set performance was due to the number of test set is lower than train set. This resulting training data more vary than testing data, makes the model covers the distribution on test data. Hence, the testing data has a slightly better performance compared to training data. In addition the detailed Graph of Training and Validation (Accuracy and Loss) for v2 leads was displayed on Figure 5.  
Figure 4: 1D-CNN Architecture. A confusion matrix is a detailed tabulation that describes the classification results in a model. From the results of the confusion matrix in lead v2 (Figure 6), it shows that the model built has a very good accuracy value in almost all MI classes. The CNN model used recognizes well (100%) in classes A, AL, IP, L, P, and PL, while the normal class has a value of 99.94% with only one data having an error in labeling.