# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 1 Introduction  
According to the World Health Organization's 2022 statistics report [1], 33.2 million people worldwide died of non-communicable diseases in 2019, which was a 28% increase from 2000, and women in more than half of all countries and men in 3/4 of all countries face premature death from cardiovascular disease [2]. Although coronary computed tomography (CT) [3] and echocardiography [4] can be used to diagnose potential heart disease patients, these tests are time-consuming, costly, and require specialized testing equipment. More frustrating, the disruption in medical services caused by the COVID-19 pandemic may expose existing patients to increased risks of serious illness and death [5]. Given the dire situation, we urgently need to develop low-cost heart disease prediagnosis equipment and increase the popularity of early non-destructive heart disease testing, which will greatly promote heart disease diagnosis, treatment, and prevention while reducing the harm caused by heart disease, particularly heart failure (HF). In this paper, we attempt to develop a Korotkoff sound (KS)-based HF prediagnosis method based on deep learning (DL) by investigating the signal preprocessing and feature extraction techniques of KS data gathered. As a long-term stable and accurate blood pressure monitoring signal, KS has been widely employed in the clinical diagnosis of hypertension and the daily monitoring ofpotential patients [6, 7]. Researchers have been making continuous efforts in the field of KS-based disease detection, and the QKD interval [8], the ankle-bracchial index (ABI) [9], and other indexes have been proposed and used to diagnose cardiovascular diseases. The QKD interval is the period between the beginning of depolarization on the electrocardiogram (Q) and the detection of the last Korotkoff sound (K) at the level of the brachial artery during cuff deflation, which corresponds to diastolic blood pressure (D). Although these approaches have limits in terms of accuracy, sensitivity, and specificity, they have proven successful in the early detection of cardiovascular disorders as a method of early prediagnosis. With the advancement of digital signal processing, big data, and artificial intelligence technology, we now have a more powerful tool to re-examine the role of KS signals in disease prediagnosis. To date, a large number of machine learning (ML) algorithms have been used to classify non-stationary time series signals [10], with impressive results. However, traditional ML approaches typically involve a large number of time-consuming preprocessing tasks, such as noise reduction [11, 12], segmentation [13, 14], and feature extraction [15]. Furthermore, the ML algorithm is heavily reliant on the accuracy of feature selection, and different feature sets have a significant impact on classification results. Deep convolutional neural (CNN) network avoids many of the problems associated with traditional ML, solves the problem of signal feature selection, and greatly improves the classifier's classification accuracy [16]. The CNN algorithm has become increasingly significant in processing non-stationary time series data as people's understanding of the CNN network has deepened [17, 18]. In recent decades, heart sound (HS) [19, 20], electrocardiograms (ECG) [21], and cardiac interbeat interval (RR interval) [22] have gradually attracted people's attention due to the benefits of convenience, non-invasion, and simple promotion [23]. Many scholars have developed a variety of ML and deep-learning computer-aided diagnosis systems for heart disease based on these signals [24, 25]. These systems assist researchers in identifying key factors from large amounts of tedious data, reducing the burden on doctors and medical staff significantly. However, the popularity of the heart disease prediagnosis system in communities and families remains inadequate. According to some surveys, most people do not prioritize the early prevention of heart disease due to a lack of knowledge and attention [26].