# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 4 Experimental Result
### Experiment Result
The confusion matrix can visualize the actual and predicted values to observe the accurately classified predicted values for each class; the rows of the confusion matrix represent the actual values and the columns represent the predicted values. As a result of dividing the entire data of the PhysioNet/CinC 2016 Challenge Dataset into 70% training data and 30% test data, the test data used for classification included 947 in the abnormal class and 2957 in the normal class. As a result of visualization using a confusion matrix to analyze the number of classified for each class, 1D-CNN classified 871 for the Abnormal classes and 2880 for the Normal classes, and 2D-CNN classified 868 for the Abnormal classes and 2852 for the Normal classes were classified. The result of ensemble of the two models is 915 for the Abnormal classes and 2933 for the Normal classes, which shows that the number of classified classes for each class has improved compared to a single model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**2D-CNN Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{6}{c}{**Average for Each Class**} \\ \cline{3-8} \multicolumn{1}{c}{} & & **Precision** & **Recall** & **F1-Score** & **Sensitivity** & **Specificity** \\ \hline PhysioNet/CinC 2016 Challenge Dataset & 95.29\% & 0.93 & 0.94 & 0.94 & 0.94 & 0.94 \\ \hline PASCAL Classifying Heart & Dataset A & 87.93\% & 0.9 & 0.93 & 0.94 & 0.93 & 0.98 \\ \cline{2-8} Sounds Challenge Dataset & Dataset B & 86.49\% & 0.83 & 0.83 & 0.83 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance results of CWT-based 2D-CNN model for each dataset. Figure 14: _Cont._  
Figure 15 show the confusion matrix results using Dataset A from the PASCAL Classifying Heart Sounds Challenge data set. Figure 15a Shows the confusion matrix of 1D-CNN, Figure 15b shows the confusion matrix of 2D-CNN, and Figure 15c shows the confusion matrix of the ensemble. As a result of dividing the entire data of the PASCAL Classifying Heart Sounds Challenge Dataset A into 80% training data and 20% test data, the test data used for classification included 24 in the Artifact class, 7 in the Extrahls class, 13 in the Murmur class, and 14 in the Normal class. As a result of visualization using a confusion matrix to analyze the number of classified for each class, 1D-CNN classified 23 for the Artifact class, 6 for the Extrahls class, 12 for the Murmur class and 11 for the Normal class. Figure 14: 1D-CNN, 2D-CNN, and ensemble confusion matrix results for PhysioNet/CinC 2016 Challenge Dataset. (**a**) the confusion matrix of 1D-CNN, (**b**) the confusion matrix of 2D-CNN, and (**c**) the confusion matrix of the Ensemble. Figure 15: _Cont._  
2D-CNN classified 24 for the Artifact class, 3 for the Extrahls class, 10 for the Murmur class and 14 for the Normal class. The result of ensemble of the two models is 24 for the Artifact class, 6 for the Extrahls class, 12 for the Murmur class and 14 for the Normal class, which shows that the number of classified classes for each class has improved compared to a single model. Figure 16 show the confusion matrix results using Dataset B from the PASCAL Classifying Heart Sounds Challenge data set. Figure 16a Shows the confusion matrix of 1D-CNN, Figure 16b shows the confusion matrix of 2D-CNN, and Figure 16c shows the confusion matrix of the ensemble. As a result of dividing the entire data of the PASCAL Classifying Heart Sounds Challenge Dataset B into 80% training data and 20% test data, the test data used for classification included 18 in the Normal class, 6 in the Extrasystole class and 13 in the Murmur class. As a result of visualization using a confusion matrix to analyze the number of classified for each class, 1D-CNN classified 15 for the Normal class, 5 for the Extrasystole class and 12 for the Murmur class. 2D-CNN classified 16 for the Normal class, 4 for the Extrasystole class and 12 for the Murmur class.