Lung-Heart Sound Separation Using Noise Assisted Multivariate Empirical Mode Decomposition

被引：0
|
作者：
Lin, ChingShun [1 ]
Tanumihardja, Wisena A. [1 ]
Shih, HongHui [1 ]
机构：
[1] Natl Taiwan Univ Sci & Technol, Dept Elect & Comp Engn, Taipei, Taiwan
来源：
2013 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATIONS SYSTEMS (ISPACS) | 2013年
关键词：
Breath sound recordings; Lung sound extraction; Empirical mode decomposition; Ensemble empirical mode decomposition; Multivariate empirical mode decomposition; Noise-assisted multivariate empirical mode decomposition; REDUCTION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Separating lung sound (LS) from breath sound (BS) recording has been of interest to doctors and researchers in the last two decades. Many algorithms have been developed to solve this question, one of them is based on the empirical mode decomposition (EMD). Due to the notorious mode mixing issue in the standard EMD, this paper surveys LS extraction based on EMD extensions, including ensemble EMD (EEMD), multivariate EMD (M-EMD), and noise assisted M-EMD (NAM-EMD). In this study, the algorithm for LS extraction is composed of heart sound (HS) segmentation, LS separation, and segments reconstruction. The performance evaluation by auditory and numerical analyses reveals that NAM-EMD based LS extraction is superior to the standard EMD and its extensions.
引用
收藏
页码：726 / 730
页数：5
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