## 4 Discussion
By comparing the time consumption, we find that the model has the highest time consumption at the input length of 6s, and the time consumption is close at the input length of 25 s and 30 s. This is because the number of branches is highest when the input length is 6 s, and the number of branches is the same when the input length is 25 s and 30 s. By comparing the accuracy, we find that the model has the highest accuracy of 87.57% when the input length is 25 s.  
Figure 3 shows the visualization of the features of the different branches. For the convenience of observation, we reduced the feature size of each branch in the MCNN-BLSTM model (\(l=25\) s) from 256 to 3 by using the principal component analysis (PCA) method, and the size of the last layer of features from 48 to 3 before the _softmax_ layer. We use the three features of PCA after dimensionality reduction as the \(x\), \(y\), and \(z\) axes shown in Figure 3, respectively. Then, the results of these features of the N, AF, and O categories could be visualized as a three-dimensional space. As shown in Figure 3, the feature information extracted from each branch is different, which fully demonstrates the effectiveness of each branch. Each branch can extract rich feature information. However, it is still difficult to classify depending on the feature information extracted by a single branch. After the feature information of different branches is fused and BLSTM is used for feature enhancement, the degree of feature recognition is significantly increased, and the three features are separated, which also indicates that the model can effectively integrate the feature information of each branch and enhance the feature information through BLSTM. It makes sense to compare the results obtained in our study with those similar to ours. The compared results of the proposed model with the state-of-the-art methods are shown in Table 7. [PERSON] et al. [6] proposed a combined CNN and LSTM network model and obtained multi-classification F1 scores of 90.28%, 82.21%, and 73.24% in the CinC2017 database, respectively. [PERSON] et al. [18] proposed a two-layer LSTM network model with multi-classification F1 scores of 81%, 84%, and 70% in the CinC2017 database, respectively. [PERSON] et al. [7] proposed the BIT-CNN network model with multi-classification F1 scores of 89.73%, 81.06%, and 74.45% in the CinC2017 database, respectively. [PERSON] et al. [8] proposed a dual-channel VGG network model with multi-classification F1 scores of 90%, 83%, and 75% in the CinC2017 database, respectively. [PERSON] et al. [36] performed a research analysis on CinC2017 using the two most popular current models (Transformer and CNN-LSTM). The final F1 score of the model based on CNN-LSTM was 82.4%, which achieved better results. By comparison, our method can achieve a high level of F1 scores in normal and the other two categories, which are 92.02% and 79.62%, respectively. Although the F1 scores of AF did not reach the highest level, they are still at a high level, and we achieved the best F1 scores in both normal and other classifications. Finally, our average F1 score reaches the highest level, which is 84.56%.