## 4 Discussion  
Many literatures describe approaches to MI signal classification. The difference in the number of MI classes used as well as the number of leads used must be taken into account when comparing the performance of each. Table 6 presents some of the published research results for the current approach. Research [PERSON] et al. [19], detected MI and Normal for 12 lead ECG signals with KNN classification and achieved 98.74% performance on lead v3. [PERSON] et al. [16], classified MI and Normal classes for 12 ECG lead signals with the LS-SVM classification. The results obtained are 99.31% accuracy in lead ii. [PERSON] et al. [20], achieved an accuracy of 98.33% using the SVM classification to distinguish MI from healthy classes. [PERSON] et al. [9], used CNN model 10 layer to classify 10 MI and Normal classes on the 12 lead ECG signals. The model output obtained reaches a performance of 99.78%. [PERSON] et al. [21], used ML-ResNet for the diagnosis of 6 classes of MI and normal with an accuracy of 99.72% and an F1-score of 99.67%. [PERSON] et al. [22] classified 12 ECG signal leads for MI and Non MI classes using CNN and bi LSTM models and the performance evaluation results obtained were 99.246%. While [PERSON] et al. [4] classified MI and Normal classes for 12 lead ECG signals with the CNN model which achieved an accuracy of 99.82%. Research Li [23], proposed an automatic MI detection model SLC-GAN which synthesizes single ECG data with high morphological similarity through Generative Aversive Network (GAN). MI detection is automated using the Convolutional Neural Network (CNN) with the original ECG data and the synthetic ECG from the GAN. Empirical results show that the SLC-GAN method performs very well compared to other MI classification methodologies on single lead ECG from PTB Diagnostic ECG Database. The MI SLC-GAN classification accuracy reaches 99.06% with 5-fold cross validation. For our proposed model, we use a 15 lead ECG signal with 1D-CNN architecture for 10 MI and Normal classes. In Table 6, it can be seen that the MI classification problem can be proposed as a binary classification problem, namely distinguishing ECG signals for Normal and MI classes. The difference between this study and previous studies (1,2,3,5,6,7) lies not only in the number of MI classes used, but also in the number of ECG signal leads. All previous studies generally used 12 standard leads, while this study used 15 ECG signals (12 standard leads and 3 frank leads). It can be seen that the performance results for 3 lead franks (vx, vy, vz) reached an average accuracy of 99.97%, 99.96% and 99.93% and an average sensitivity of 99.76%, 99.80% and 99.52% (Table 4). From the performance results, it can be seen that the three leads (vx, vy, vz) were able to give the best contribution to the classification performance for 10 MI and Normal classes. Research conducted by [PERSON] et al. [8], using the same number of MI and Normal classes as this study. The difference lies in the segmentation for the length of the ECG signal ([PERSON] [8] 651 pulse samples, while for this study using a longer signal of 700 pulse \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Study** & **Year** & **Lead/Class** & **Classifier** & **Acc. Rate** \\ \hline
1. [PERSON] et al. [19] & 2016 & 12 lead, Normal and MI & KNN & 98.74\% \\
2. [PERSON] et al. [16] & 2018 & 12 lead, Normal and MI & LS-SVM & 99.31\% \\
3. [PERSON] at al [20] & 2018 & 12 lead, Normal and MI & SVM & 98.33\% \\
4. [PERSON] et al. [9] & 2019 & 12 lead, Normal and 10 MI & End to end, CNN & 99.78\% \\
5. [PERSON] et al. [21] & 2019 & 12 lead, Normal and 6 MI class & ML-ResNet & 99.72\% \\
6. [PERSON] et al. [22] & 2021 & 12 lead, Normal, MI dan Non MI & CNN and biLSTM & 99.246\% \\
7. [PERSON] et al. [4] & 2021 & 12 lead, Normal and MI & CNN & 99.82\% \\
8. [PERSON] et al. [23] & 2022 & MI dan Healty & SLC-GAN & 99.06\% \\
9. Proposed & 2022 & 15 lead, Normal and 10 MI class & 1D-CNN & 99.98\% \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of methods for detecting MI.