# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 4 Experimental Result
### Experiment Result  
In this study, we used the open datasets PhysioNet/CinC 2016 Challenge Dataset and PASCAL Classifying Heart Sounds Challenge Dataset for PCG heart sound classification. For feature extraction, WST and CWT were used based on wavelet analysis technology. The features extracted by WST were used to design a 1D-CNN model and a deep learning model suitable for the two datasets, and the images converted to time-frequency expression through CWT are using the 2D-CNN model. Classification performance was checked using Accuracy, Precision, Recall, F1-Score, Sensitivity, and Specificity. Table 5 shows the performance results of classifying the features extracted based on the WST into the 1D-CNN model using the PhysioNet/CinC 2016 Challenge Dataset and PASCAL Classifying Heart Sounds Challenge Dataset. The PhysioNet/CinC 2016 Challenge Dataset divided the data into 70% training and 30% testing and showed the highest accuracy when split with the following settings: QualityFactors of Waveletscattering [4 2 1], Filter size 5, number of filters 32, InitialLearnRate 0.001, MaxEpochs 200, and MiniBatchSize 64. Dataset A of the PASCAL Classifying Heart Sounds Challenge Dataset split the data into 80% training data and 20% test data and showed the highest accuracy when split with the following settings: QualityFactors of Waveletscattering [4 2 1], Filter size 9, Number of filters 64, InitialLearnRate 0.0001, MaxEpochs 300, and MiniBatchSize 128. Dataset B of the PASCAL Classifying Heart Sounds Challenge Dataset splits the data into 90% training data and 10% test data and showed the highest accuracy when split with the following settings: QualityFactors of Waveletscattering [4 2 1], Filter size 4, Number of filters 128, InitialLearnRate 0.0001, MaxEpochs 200, and MiniBatchSize 32. Table 6 shows the performance results of classifying the features extracted based on CWT into a 2D-CNN model using the PhysioNet/CinC 2016 Challenge Dataset and PASCAL Classifying Heart Sounds Challenge Dataset. The three datasets used the 2D-CNN transfer learning models GoogleNet, ResNet50, and ResNet101; when the results were confirmed, the model with the highest accuracy was used. The PhysioNet/CinC 2016 Challenge Dataset split the data into 70% training data and 30% test data, similar to the 1D-CNN, and confirmed that the ResNet50 model, which was set to MiniBatchSize \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**1D-CNN Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{5}{c}{**Average for Each Class**} \\ \cline{2-7} \multicolumn{2}{c}{**Dataset**} & & **Precision** & **Recall** & **F1-Score** & **Sensitivity** & **Specificity** \\ \hline PhysioNetCinC 2016 Challenge Dataset & 96.1\% & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 \\ \hline \multirow{2}{*}{PASCAL Classifying Heart Sounds Challenge Dataset B} & Dataset A & 89.66\% & 0.9 & 0.88 & 0.89 & 0.88 & 0.97 \\ \cline{2-7}  & Dataset A & 86.49\% & 0.84 & 0.86 & 0.85 & 0.86 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance results of WST-based 1D-CNN Model for each dataset. 64, MaxEpochs 30, and Validation Frequency 10, had the highest accuracy. Dataset A of the PASCAL Classifying Heart Sounds Challenge Dataset split the data into 80% training and 20% testing and confirmed that the GoogleNet model set to InitialLearRate 0.0001, MiniBatchSize 64, MaxEpochs 30, and Validation Frequency 50 had the highest accuracy. Dataset B of the PASCAL Classifying Heart Sounds Challenge Dataset was divided into 90% training data and 10% test data, and it was confirmed that the GoogleNet model set to MiniBatchSize 64, MaxEpochs 20, and Validation Frequency 50 had the highest accuracy. Figure 14 shows the confusion matrix results using the PhysioNet/CinC 2016 Challenge Dataset. Figure 14a shows the confusion matrix of 1D-CNN, Figure 14b shows the confusion matrix of 2D-CNN, and Figure 14c shows the confusion matrix of the Ensemble.