# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 4 Experimental Result
### Performance Evaluation Method  
Accuracy, Precision, Recall, F1-Score, Sensitivity, and Specificity were used as evaluation index methods to classify normal phonocardiograms from phonocardiogram signals and those of patients with heart disease. Used to evaluate the model's performance using actual and predicted labels, True Positives (TP) refer to instances where the model correctly predicts the positive class and True Negatives (TN) refer to instances when the model correctly predicts the negative class. False Positives (FP) refer to instances where the model predicts a negative class as a positive class, and False Negatives (FN) refer to instances where the model predicts a positive class as a negative class.  
Accuracy is a method of checking whether the model correctly predicts the positive and true negative classes and is measured by adding TP and TN, which represent correctly classified instances, from the total number of instances, as shown in Equation (7).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
Precision is a method used to calculate the accuracy with which the model correctly classifies instances predicted as positive. As shown in Equation (8), the instance is calculated by dividing the number of correctly predicted TPs by the sum of TP + FP.  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
Recall is calculated using Equation (9) and is a method to check whether instances belonging to the positive class are correctly classified as the positive class.  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**PASCAL Classifying Heart Sounds Challenge Dataset—Signal Segmentation**} \\ \hline
**Class** & **Dataset A** & **Dataset B** \\ \hline Normal & 68 & 185 \\ \hline Murmur & 66 & 134 \\ \hline Extrahls & 37 & - \\ \hline Artifact & 120 & - \\ \hline Extrasystole & - & 185 \\ \hline Signal Segmentation & 291 & 504 \\ \hline \end{tabular}
\end{table}
Table 4: Number of segmented signals of the PASCAL Classifying Heart Sounds Challenge Dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**PASCAL Classifying Heart Sounds Challenge Dataset**} \\ \hline
**Class** & **Dataset A** & **Dataset B** \\ \hline Normal & 31 & 200 \\ \hline Murmur & 34 & 66 \\ \hline Extrahls & 19 & - \\ \hline Artifact & 40 & - \\ \hline Extrasystole & - & 46 \\ \hline Total & 124 & 312 \\ \hline \end{tabular}
\end{table}
Table 3: Composition of the PASCAL Classifying Heart Sounds Challenge Dataset.  
The F1-Score is obtained by combining Precision and Recall, as shown in Equation (10), to evaluate the performance of a balanced model considering FP and FN.  
\[\textit{F1-Score = 2 \times Precision \times Recall/Precision + Recall} \tag{10}\]  
Sensitivity is a performance evaluation method used to measure whether the model correctly classifies instances of the positive class as positive classes using Equation (11).  
\[\textit{Sensitivity}=\frac{TP}{TP+FN} \tag{11}\]  
Specificity is a performance evaluation method used to measure whether an instance of a negative class is correctly classified as a negative class using Equation (12).  
\[Specificity=\frac{TN}{TN+FP} \tag{12}\]