Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis

被引：280

作者：

Mijovic, Bogdan ^{[1
]}

De Vos, Maarten ^{[1
]}

Gligorijevic, Ivan ^{[1
]}

Taelman, Joachim ^{[1
]}

Van Huffel, Sabine ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Elect Engn, SISTA COSIC DOCARCH Div, B-3001 Louvain, Belgium

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2010年 / 57卷 / 09期

关键词：

Blind source separation (BSS); empirical-mode decomposition (EMD); feature extraction; independent component analysis (ICA); single-channel signal analysis; CONTAMINATION; FMRI; ICA;

D O I：

10.1109/TBME.2010.2051440

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

In biomedical signal processing, it is often the case that many sources are mixed into the measured signal. The goal is usually to analyze one or several of them separately. In the case of multichannel measurements, several blind source separation techniques are available for decomposing the signal into its components [e.g., independent component analysis (ICA)]. However, only a few techniques have been reported for analyses of single-channel recordings. Examples are single-channel ICA (SCICA) and wavelet-ICA (WICA), which all have certain limitations. In this paper, we propose a new method for a single-channel signal decomposition. This method combines empirical-mode decomposition with ICA. We compare the separation performance of our algorithm with SCICA and WICA through simulations, and we show that our method outperforms the other two, especially for high noise-to-signal ratios. The performance of the new algorithm was also demonstrated in two real-life applications.

引用

页码：2188 / 2196

页数：9

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