## 3 Result
### VNVMC Classification  
To classify the VNVMC signal, we trained the classification model based on the LSTM technique described in Section 2.3. The number of LSTM cells with a time-step size of 16 was three and the hidden unit for each LSTM cell was set [128; 128; 128]. The parameters of the LSTM model were set empirically through the experiments to find the best performance. To evaluate the performance of the DNN model, the number of hidden layers was set as three and the number of hidden units was set as [64; 64; 64]. In addition, the activation function of the DNN model was an exponential linear unit (ELU) function, and cross-entropy was used for the loss function of the DNN model. In time, the parameters of the DNN model were empirically determined through the experiments with the best performance. When the number of the hidden layers increased to more than four layers, the overfitting problem occurred in the model. To train the SVM model, we chose the second-order polynomial function for the kernel function, and the kernel scale was searched for automatically log-scaled positive values in the range of [0.001, 1000]. At this time, we determined the second-order polynomial function through extensive experiments to find the best performance of the SVM model. Next, to learn the ANN model, we set one hidden layer with 128 hidden units, and we then selected the ELU function as the activation function of the ANN model. In addition, the parameters, including the weight matrix and the bias vector, were updated at the hidden layer based on the cross-entropy loss function. To equivalently evaluate both the conventional algorithm and the proposed algorithm, we employed the same feature vector and the same training/test DB. Finally, to evaluate the performance of the VNVMC algorithm, we compared the proposed method with conventional methods using the total accuracy, confusion matrix, AUC, and boxplot of the VNV reference and the prediction result. As a result, our experimental results, such as the total accuracy, confusion matrix and AUC, are summarized in Tables 1 and 2. At this time, the LSTM model shows a better performance than the conventional machine-learning method in terms of the evaluation results summarized in Tables 1 and 2. When we evaluated the performance of the models regarding each the ES frequency parameters, the performance of the LSTM model showed, on average, a larger AUC than the conventional model, as shown in Table 2 and Figure 9. As shown in Figure 10, the boxplot shows the results of the LSTM model and conventional model. The central line in each boxplot means median value, the edges of the boxplot indicate the 25 th (lower) and 75 th (upper) percentiles, and the outliers were plotted using the + symbol. The whiskers of the boxplot that extended to the maximum value were excluded as outliers. Since the box size was inversely proportional to the performance, the conventional models, including SVM, ANN, and DNN, were unstable, as shown in Figure 10, while the LSTM model yielded the precise classification for the VNVMC signal. Although the box size of the models was similar, as shown in Figure 9, the performance of the LSTM model was better than the conventional method in terms of the central line and minimum value of the box. Thus, as plotted in Figure 10, we confirmed that the LSTM model more accurately classified the VNVMC signal than the conventional model. As a result, since the cell state memorizes the previous information, the performance of the LSTM model regarding the sequential data is better than that of the model trained by the conventional method.