## 5 Discussion  
This paper used 10-fold cross-validation to train the model, and the comparison of the results of the evaluation indicators obtained and the existing methods is shown in Table 6. It can be seen from Table 6 that the classification and recognition of myocardial infarction ECG have focused on multi-lead studies. By extracting the time-frequency domain features [3; 5] and the wavelet coefficient features [7] of multi-lead ECG signals, myocardial infarction classification can be achieved with a high recognition rate through SVM, KNN, and other methods. Unlike traditional 12 leads, 3 Frank leads also can be used to derive the vectorcardiogram (VCG) to detect myocardial infarction. [PERSON] et al. found that the 12 lead ECG could be linearly transformed from a 3 lead VCG [19]. Aiming at classify myocardial infarction with VCG signals, [PERSON] et al. acquired VCG signals from Frank XYZ leads and extracted 64 features to complete the detection [20]. Ge obtained multivariable autoregressive coefficients via the VCG signal to classify myocardial infarction [21]. However, the above methods all passed the complicated handcrafted feature extraction step, the calculation process was relatively cumbersome, and the data volume recorded by the multi-lead system was often very large and had more constraints on patients, which is not suitable for portable monitoring, and thus is limited to a certain extent. In studies of single-lead myocardial infarction, [PERSON] [6] and [PERSON] [22] used T-wave detection or morphological information as features to classify with the Naive Bayes and SVM methods, respectively; [PERSON] [13] achieved classification through the CNN structure with a 95.2% accuracy rate; unlike [PERSON] [13], this paper considered the ECG signal as a time series, and combined CNN and LSTM to extract deeper features and eliminated steps such as complex feature extraction without decreasing accuracy. It is worth mentioning that although the deep learning model can automatically learn to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Rate** & **0.0001** & **0.001** & **0.01** & **0.1** \\ \hline
**Optimizer** & & & _Acc_ & \\ \hline Adam & 95.4\% & 93.6\% & 91.0\% & 90.5\% \\ RMSprop & 94.2\% & 93.2\% & 90.5\% & 89.6\% \\ SGD & 94.8\% & 92.0\% & 89.4\% & 88.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of different optimizers and learning rates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layers** & **Type** & **Filter Number** & **Kernel Size** & **Output Shape** \\ \hline
0 & Input & & & \(600\times 1\) \\
1–3 & Convolution & & 4 & 5 & \(600\times 4\) \\
4 & Max-Pooling & & - & 5 & \(120\times 4\) \\
5–7 & Convolution & & 8 & 5 & \(120\times 8\) \\
8 & Max-Pooling & & - & 5 & \(24\times 8\) \\
9–11 & Convolution & & 16 & 5 & \(24\times 16\) \\
12 & Global-Avg-Pooling & - & - & 16 \\  & Concatenate & & - & \(16\times 5\) \\
13 & Dropout & & 0.5 & \(16\times 5\) \\
14 & LSTM & & - & \(16\times 1\) \\
15 & Flatten & & - & 16 \\
16 & Fully Connected & & - & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter setting of the model.  
obtain feature information, compared with the traditional method, the model has higher requirements on the amount and time of training data. The model spent about 15 s per epoch during training. However, there was no need to retrain when classifying the results. During the test of 358 datasets, the total test time was 2.2 s, and the average test time was about 60 ms. Although the processing speed of conventional portable devices cannot be consistent with that of the computer used in the experiment, the algorithm can be implemented on the cloud platform for real-time processing to meet the requirements of clinical applications.