文献类型：期刊文献

英文题名：Research on heart and lung sound separation method based on DAE-NMF-VMD

作者：Sun, Wenhui[1,2];Zhang, Yipeng[1,2];Chen, Fuming[2]

第一作者：Sun, Wenhui

通信作者：Chen, FM[1]

机构：[1]Chinese Peoples Liberat Army, Med Secur Ctr, 940th Hosp Joint Logist Support Force, Lanzhou 730050, Gansu, Peoples R China;[2]Gansu Univ Tradit Chinese Med, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China

第一机构：Chinese Peoples Liberat Army, Med Secur Ctr, 940th Hosp Joint Logist Support Force, Lanzhou 730050, Gansu, Peoples R China

通信机构：[1]corresponding author), Gansu Univ Tradit Chinese Med, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China.|[10735b4fd93d6e0fa5aa4]甘肃中医药大学信息工程学院（教育技术中心）;[10735]甘肃中医药大学;

年份：2024

卷号：2024

期号：1

外文期刊名：EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING

收录：;EI(收录号：20241916036726);Scopus(收录号：2-s2.0-85192049328);WOS:【SCI-EXPANDED(收录号:WOS:001214762200001)】；

基金：Not applicable.

语种：英文

外文关键词：Heart and lung sound separation; Deep autoencoder; Variational mode decomposition; Nonnegative matrix factorization; Deep learning

摘要：Auscultation is the most effective method for diagnosing cardiovascular and respiratory diseases. However, stethoscopes typically capture mixed signals of heart and lung sounds, which can affect the auscultation effect of doctors. Therefore, the efficient separation of mixed heart and lung sound signals plays a crucial role in improving the diagnosis of cardiovascular and respiratory diseases. In this paper, we propose a blind source separation method for heart and lung sounds based on deep autoencoder (DAE), nonnegative matrix factorization (NMF) and variational mode decomposition (VMD). Firstly, DAE is employed to extract highly informative features from the heart and lung sound signals. Subsequently, NMF clustering is applied to group the heart and lung sounds based on their distinct periodicities, achieving the separation of the mixed heart and lung sounds. Finally, variational mode decomposition is used for denoising the separated signals. Experimental results demonstrate that the proposed method effectively separates heart and lung sound signals and exhibits significant advantages in terms of standardized evaluation metrics when compared to contrast methods.