Prediagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound

Abstract

Heart failure (HF) is a devastating condition that impairs people’s lives and health. Because of the high morbidity and mortality associated with HF, early detection is becoming increasingly critical. Many studies have focused on the field of heart disease diagnosis based on heart sound (HS), demonstrating the feasibility of sound signals in heart disease diagnosis. In this paper, we propose a non-invasive early diagnosis method for HF based on a deep learning (DL) network and the Korotkoff sound (KS). The accuracy of the KS-based HF prediagnosis was investigated utilizing continuous wavelet transform (CWT) features, Mel frequency cepstrum coefficient (MFCC) features, and signal segmentation. Fivefold cross-validation was applied to the four DL models: AlexNet, VGG19, ResNet50, and Xception, and the performance of each model was evaluated using accuracy (Acc), specificity (Sp), sensitivity (Se), area under curve (AUC), and time consumption (Tc). The results reveal that the performance of the four models on MFCC datasets is significantly improved when compared to CWT datasets, and each model performed considerably better on the non-segmented dataset than on the segmented dataset, indicating that KS signal segmentation and feature extraction had a significant impact on the KS-based CHF prediagnosis performance. Our method eventually achieves the prediagnosis results of Acc (96.0%), Se (97.5%), and Sp (93.8%) based on a comparative study of the model and the data set. The research demonstrates that the KS-based prediagnosis method proposed in this paper could accomplish accurate HF prediagnosis, which will offer new research approaches and a more convenient way to achieve early HF prevention.

Keywords

• heart failure
• Korotkoff sound
• deep learning
• MFCC
• neural networks
• prediagnosis