# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 5 Conclusions  
In this study, we propose a method to extract features using the WST and CWT methods based on wavelet analysis technology, classify cardiac abnormalities and heart sounds using a deep learning model, and ensemble the two models. In the medical field, accurate diagnosis and results must be derived, so the ensemble of deep learning models is used to improve accuracy by combining the powerful features of different models, and to reduce overfitting and improve reliability through results from various models. Because the PCG appears differently depending on the structure, size, and location of the heart as well as its physiological characteristics, PCG signal analysis is necessary for a detailed  
Figure 17: Ensemble accuracy of 1D-CNN and 2D-CNN through wavelet analysis technique for three datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Dataset** & **Feature Extraction** & **Classifier** & **Accuracy** \\ \hline [PERSON] (2021) [19] & PASCAL Dataset & MFCC & \begin{tabular}{c} Lightweight model using \\ CNN-based Inverted \\ Residual structure \\ \end{tabular} & Dataset A: 77\% \\ \hline [PERSON] (2021) [18] & & Wavelet scattering & SVM & 92.23\% \\ \hline [PERSON] (2022) [20] & PhysioNet/CinC 2016 & - & CNN-LSTM & 93.76\% \\ \hline [PERSON] (2022) [21] & Dataset & \begin{tabular}{c} Time-Frequency \\ entropy \\ \end{tabular} & SAE-DNN & 95.43\% \\ \hline [PERSON] (2022) [22] & & - & SpectroCardioNet & 97.77\% \\ \hline Proposed Method & PASCAL Dataset & WST, \
\begin{tabular}{c} 1D CNN + 2D CNN \\ CWT \\ \end{tabular} &
\begin{tabular}{c} Dataset A: 96.55\% \\ Dataset B: 89.19\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison for existing heart sound classification.  
and accurate diagnosis. For cardiac function analysis in PCG signals, early prediction and diagnosis of heart-related diseases can be made through the information on transformation invariance and the features of S1, S2, S3, and S4 of PCG. The features extracted through the WST design a 1D-CNN suitable for the dataset and check the classification performance. The features extracted through CWT were converted into time-frequency expressions, and the classification performance was checked using the transfer learning model GoogleNet and ResNet50 models, which are 2D-CNN models. Precision, Recall, F1-Score, Sensitivity, and Specificity were used to evaluate classification performance. The ensemble results of the predicted values classified through each model confirmed that the PhysioNet/CinC 2016 Challenge Dataset improved by 1.9%, Dataset A of the PASCAL Classifying Heart Sounds Challenge Dataset improved by 6.89%, and Dataset B improved by 2.7%.  
Conceptualization, J.-A.L. and K.-C.K.; Methodology, J.-A.L. and K.-C.K.; Software, J.-A.L. and K.-C.K.; Validation, J.-A.L. and K.-C.K.; Formal Analysis, J.-A.L. and K.-C.K.; Investigation, J.-A.L. and K.-C.K.; Resources, K.-C.K.; Data Curation, J.-A.L.; Writing--Original Draft Preparation, J.-A.L.; Writing--Review and Editing, K.-C.K.; Visualization, [PERSON]-A.L. and K.-C.K.; Supervision, K.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2017R1A6A1A03015496).  
Not applicable.  
The authors declare no conflict of interest.