## 6 Conclusions  
Detecting cardiac abnormalities through cardiac sounds is a method that has been proven after a long period of research. However, since auscultation is a method that relies on human senses, it has some drawbacks. For example, different doctors have different auscultation skills, so sometimes the diagnosis is unreliable. Recently, studies have been conducted to diagnose cardiovascular diseases based on deep learning to compensate for this. In this paper, we proposed a preprocessing method for better deep learning training. In data generalization, we used an appropriate peak processing algorithm to generate uniform forms of data. Data augmentation solved the problems of an insufficient amount of data and the imbalance of pathologic heart data, which are difficult to obtain. The proposed method improved the accuracy of auscultation and demonstrated performance improvements through accuracy comparisons in the basic CNN network. Furthermore, performance improvement can be expected when applied to other deep learning networks. The proposed method showed an improved detection success rate for unhealthy heart sounds. It is considered that this could be applied to an automatic diagnosis system. Therefore, if the heart sound can be acquired, it will help to detect patients quickly and prevent cardiovascular disease through early detection. In particular, it would be useful in areas where medical services are scarce.  
In future work, we will apply better noise reduction and signal processing methods to obtain the ideal cardiac signal. In addition, studies will continue to diagnose various heart diseases that are not simply normal or abnormal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (Year)** & **Approach** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** \\ \hline [PERSON] et al. [25] (2016) & AdaBoost and CNN & 94.24 & 77.81 & 86.02 \\ \hline [PERSON] et al. [26] (2016) & Wavelet-based CNN & 81.2 & 84.8 & 77.6 \\ \hline [PERSON] et al. [13] (2017) & CNN & 72.78 & 95.21 & 83.99 \\ [PERSON] et al. [27] (2019) & CNN & 86.46 & 85.63 & 86.00 \\ \hline [PERSON] et al. [12] (2020) & CNN & 87.00 & 86.60 & 84.98 \\ \hline Ours & CNN & 93.32 & 83.00 & 91.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative evaluation of CNN models.  
Figure 9: Diagnostic performance. w/o: without; DG: data generalization; DA: data augmentation.  
**Author Contributions:** Conceptualization, Y.J.; methodology, Y.J.; software, Y.J.; validation, Y.J.; investigation, Y.J.; data curation, Y.J.; writing--original draft preparation, Y.J. and J.K.; writing--review and editing, Y.J., J.K., D.K., J.K. and K.L.; visualization, Y.J. and J.K.; supervision, Y.J.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT) under Grant 2020-0-01907.  
Not applicable.  
The PASCAL Heart Sound Challenge (HSC) dataset is available at [[http://www.peterjbentley.com/heartchallenge/](http://www.peterjbentley.com/heartchallenge/)]([http://www.peterjbentley.com/heartchallenge/](http://www.peterjbentley.com/heartchallenge/)) (accessed on 17 May 2021). The 2016 PhysioNet computing in cardiology challenge (CinC) dataset is available at [[https://physionet.org/content/challenge-2016/1.0.0/](https://physionet.org/content/challenge-2016/1.0.0/)]([https://physionet.org/content/challenge-2016/1.0.0/](https://physionet.org/content/challenge-2016/1.0.0/)), DOI:10.22489/CinC.2016.179-154 (accessed on 17 May 2021).  
The authors declare no conflict of interest.