Blind source separation (BSS) has applications in the fields of data compression, feature recognition, speech, audio, and biosignal processing. Identification of ECG signal is one of the challenges in the biosignal processing. Proposed in this paper is a new method, which is the combination of related function relevance to estimated signal and negative entropy in fast independent component analysis (FastICA) as objective function, and the iterative formula is derived without any assumptions; then the independent components are found by maximizing the objective function. The improved algorithm shorthand for R-FastICA is applied to extract random mixed signals and ventricular late potential (VLP) signal from normal ECG signal; simultaneously the performance of R-FastICA algorithm is compared with traditional FastICA through simulation. Experimental results show that R-FastICA algorithm outperforms traditional FastICA with higher similarity coefficient and separation precision.

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source separation method ventricular late potential vlp signal independent components estimated signal related function relevance negative entropy normal ecg fast independent component analysis fastica algorithm extract random mixed signals fields of data compression feature recognition speech audio and biosignal processing identification of ecg signal similarity coefficient rfastica

Dengao Li,Junmin Zhao,Hongyan Liu,Defeng Hao,.The Application of FastICA Combined with Related Function in Blind Signal Separation. 2014 (),.

Dengao Li,Junmin Zhao,Hongyan Liu,Defeng Hao,"The Application of FastICA Combined with Related Function in Blind Signal Separation" 2014

Dengao Li,Junmin Zhao,Hongyan Liu,Defeng Hao,"The Application of FastICA Combined with Related Function in Blind Signal Separation"