Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 57, Issue 9, Pages 2188-2196Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2010.2051440
Keywords
Blind source separation (BSS); empirical-mode decomposition (EMD); feature extraction; independent component analysis (ICA); single-channel signal analysis
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Funding
- Research Council, Katholieke Universiteit Leuven [CoE EF/05/006, IDO 05/010EEG-fMRI]
- Flemish Government [G.0427.10N, TBM070713-Accelero, TBM080658-MRI]
- Belgian Federal Science Policy Office [IUAP P6/04]
- European Union [BM0601]
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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.
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