Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the
feature extraction methods about heart sound have focused on linear time-variant or time …

Cardiovascular disease (CVD) is considered one of the leading causes of death worldwide.
In recent years, this research area has attracted researchers' attention to investigate heart …

Phonocardiogram (PCG) recordings contain valuable information about the functioning and
state of the heart that is useful in the diagnosis of cardiovascular diseases. The first heart …

A novel method is presented to extract robust features for automatic classification of heart
sounds based on Empirical Mode Decomposition (EMD). The work decomposes segmented …

针对心音信号非平稳性, 非线性的特征, 为了更直观地把心音信号的特征显示出来,
提高分类识别的高效性, 提出了一种自适应噪声完备经验模态分解 (CEEMDAN) …

This paper proposes a method of complete empirical modal decomposition with adaptive
noise (CEEMDAN) arrangement entropy as a characteristic vector of heart sound signal and …

Abstract Analysis of heart sound signals for automatic segmentation and classification has
revealed in recent decades that it has the potential to detect pathology accurately in clinical …

Background Heart sound plays a vital role to achieve an accurate diagnosis of
cardiovascular diseases, and its auxiliary diagnosis methods have become a hotspot. Aim …

Introduction To realize noninvasive diagnosis and early diagnosis of coronary heart disease,
the study proposes a new time-frequency method for analyzing heart sound signals. This …

We purpose a novel method that combines modified frequency slice wavelet transform
(MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal …