Single-channel mixed signal blind source separation algorithm based on multiple ICA processing

Take separating the fetal heart sound signal from the mixed signal that get from the electronic stethoscope as the research background, the paper puts forward a single-channel mixed signal blind source separation algorithm based on multiple ICA processing. Firstly, according to the empirical mode decomposition (EMD), the single-channel mixed signal get multiple orthogonal signal components which are processed by ICA. The multiple independent signal components are called independent sub component of the mixed signal. Then by combining with the multiple independent sub component into single-channel mixed signal, the single-channel signal is expanded to multipath signals, which turns the under-determined blind source separation problem into a well-posed blind source separation problem. Further, the estimate signal of source signal is get by doing the ICA processing. Finally, if the separation effect is not very ideal, combined with the last time’s separation effect to the single-channel mixed signal, and keep doing the ICA processing for more times until the desired estimated signal of source signal is get. The simulation results show that the algorithm has good separation effect for the single-channel mixed physiological signals.

[1]  Norden E. Huang,et al.  New method for nonlinear and nonstationary time series analysis: empirical mode decomposition and Hilbert spectral analysis , 2000, SPIE Defense + Commercial Sensing.

[2]  Klaus Nordhausen,et al.  On robustifying some second order blind source separation methods for nonstationary time series , 2012, Statistical Papers.

[3]  Liu Ju,et al.  Applications of Independent Sub-Band Functions and Wavelet Analysis in Single-Channel Noisy Signal BSS:Model and Crucial Technique , 2009 .

[4]  Zhang Shao-bai A Single-Channel Mixed Signal BSS New Method Without Using the Prior Knowledge , 2011 .

[5]  Sun Ke-xue Research of Underdetermined Blind Source Separation Method on Dual-channel Heart Sound Signal , 2012 .

[6]  Kenya Jin'no,et al.  Analysis of convergence property of PSO and its application to nonlinear blind source separation , 2013, 2013 IEEE Congress on Evolutionary Computation.

[7]  S. Varadarajan,et al.  Analysis of atmospheric radar echoes using Wavelets and EMD , 2013, INTERNATIONAL CONFERENCE ON SMART STRUCTURES AND SYSTEMS - ICSSS'13.

[8]  Xue Bi-cui The Similitude Phase Diagram in BSS , 2006 .

[9]  Christian Jutten,et al.  Non-Negative Blind Source Separation Algorithm Based on Minimum Aperture Simplicial Cone , 2014, IEEE Transactions on Signal Processing.

[10]  Joyanta Basu,et al.  Blind source separation: A review and analysis , 2013, 2013 International Conference Oriental COCOSDA held jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE).