## 5 Conclusions and Future Work  
A GAN-based architecture was implemented to generate synthetic heart sounds, which can be used to train/test classification models. The proposed GAN model is accompanied by a denoising stage using the empirical wavelet transform, which allows us to decrease the number of training epochs and therefore the total computational cost, obtaining a synthetic cardiac signal with a low noise level.  
The proposed method was compared with a mathematical model proposed in the state of the art [21]. Two evaluation tests were carried out: the first was to measure the distortion between the natural and synthetic cardiac signals, in order to objectively evaluate the similarity between them. In this case, the mel-cepstral distortion (MCD) method was used, being widely used in the evaluation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Extraction** & \multicolumn{4}{c}{**Classifier**} \\  & **SVM** & **KNN** & **RF** & **MLP** \\ \hline EWT + power [9] & 100\% & 100\% & 100\% & 100\% \\ MFCC + DWT [10] & 80\% & 90\% & 78\% & 82\% \\ Statistical, frequency, and perceptual [11] & 98\% & 78\% & 78\% & 60\% \\ LPC + MFCC [12] & 98\% & 96\% & 96\% & 82\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy results of synthetic signals, using the trained models proposed in articles [9, 10, 11, 12].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Extraction** & \multicolumn{4}{c}{**Classifier**} \\  & **SVM** & **KNN** & **RF** & **MLP** \\ \hline EWT + power [9] & 92.42\% & 99.25\% & 99.00\% & 98.63\% \\ MFCC + DWT [10] & 90.68\% & 91.18\% & 91.42\% & 91.55\% \\ Statistical, frequency, and perceptual [11] & 84.47\% & 93.66\% & 93.66\% & 92.54\% \\ LPC + MFCC [12] & 96.27\% & 95.52\% & 95.27\% & 97.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy results of the methods proposed in [9, 10, 11, 12], taken from article [9].  
of audio quality. In this test, the synthetic signal generated with the proposed method obtained a better similarity result with the natural signals compared to the mathematical model proposed in [21].  
The second method consisted of using different, pre-trained classification machine learning models with good precision performance, in order to use the synthetic signals as test dataset and verify if the different ML models perform well. In this test, the power characteristics proposed in [9] with the different machine learning models registered the best results. Generally speaking, most of the combinations of features with classification models performed well in discriminating synthetic heart sounds as normal, as shown in Table 4.  
According to the results obtained using the MCD distortion metric, and the performance of different ML models, a strong indication can be seen that synthetic signals can be used to improve the performance of heart sound classification models, since the number of samples in the training could be increased.  
As future work, we are implementing a GAN-based model that can generate abnormal types of heart sounds, delving into the generation of heart murmurs, since it is very difficult to acquire many samples of a specific type of abnormality. By creating a database of normal synthetic heart sounds and types of abnormalities, we hope to improve the performance of classification systems and advance the detection of types of heart abnormalities, generating significant support for healthcare professionals.  
Conceptualization, P.N. and W.S.P.; methodology, P.N. and W.S.P.; software, P.N.; validation, P.N. and W.S.P.; formal analysis, P.N. and W.S.P.; investigation, P.N.; resources, P.N.; data curation, P.N.; writing--original draft preparation, P.N.; writing--review and editing, P.N. and W.S.P.; visualization, P.N.; supervision, W.S.P.; project administration, P.N.; funding acquisition, P.N. and W.S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.