# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 6 Conclusions  
In this paper, we propose an innovative HF prediagnosis method to investigate the use of the KS signal in the early diagnosis of HF. We investigated the effect of signal segmentation algorithms and different signal features on HF prediagnosis by creating ST-CWT, ST-MFCC, UST-CWT, and UST-MFCC datasets. Alexnet, VGG19, ResNet50, and Xception were the four DL networks used for training and classification. All the networks were validated using a fivefold cross-validation approach, and evaluated using the Acc, Se, Sp, AUC and Tc.  
According to our research, the deep neural network model performed better on unsegmented datasets than that on segmented datasets. By comparing the differences between the two data sets, we believe that the KS quiet period signal characteristics discarded by signal segmentation is the main cause of this phenomenon. Our study demonstrates that the KS silent period played a crucial role in KS-based HF prediagnosis, although the human ear is not sensitive to that. Furthermore, in both segmented datasets (ST-CWT, ST-MFCC) and unsegmented datasets (UST-CWT, UST-MFCC), the performance of the neural network based on the MFCC feature outperforms the neural network based on the CWT feature, illustrating the advantage of the MFCC feature extraction method in the audio recognition field. Our research shows the potential of KS-based noninvasive HF prediagnosis technology in early warning of HF potential risk groups and prognosis monitoring of HF patients, implying that KS-based methods can be an important auxiliary tool for HF detection. Reasonable signal processing, effective feature extraction, and efficient DL network selection, in our opinion, are the keys to achieving these goals.  
This is a preliminary study with a small sample size. In the future, we will conduct more in-depth research on the mechanism of KS generation, the mechanism of correlation between KS and HF, and the mechanism of correlation between drugs, basic diseases, and KS. Prospective testing and blind verification in the test population will also be conducted. Simultaneously, we will select an efficient DL model and perform optimization and large-scale database verification based on the existing research foundation to achieve embedded device migration and community promotion.  
Conceptualization, [PERSON] ([PERSON]) and S.X.; methodology, [PERSON] ([PERSON]); software, R.W.; validation, [PERSON] ([PERSON]) and S.X.; formal analysis, [PERSON] ([PERSON]) and S.X.; investigation, [PERSON] ([PERSON]), [PERSON], S.J. and [PERSON]; resources, S.J. and Y.W.; data curation, [PERSON] ([PERSON]), S.J. and Y.W.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), R.W.; visualization, [PERSON] ([PERSON]) and R.W.; supervision, [PERSON] ([PERSON]) and S.X.; project administration, [PERSON] ([PERSON]) and S.X.; funding acquisition, [PERSON] ([PERSON]) and S.X. All authors have read and agreed to the published version of the manuscript.  
The paper was supported by the Key R&D Program of Zhejiang Province of China (No. 2021C03030).  
Not applicable.  
The data that support the findings of this study are available from The Fourth Affiliated Hospital Zhejiang University School of Medicine, Zhejiang University, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.  
The authors declare no conflict of interest.