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Indexed by:期刊论文

Date of Publication:2008-12-01

Journal:NEURAL PROCESSING LETTERS

Included Journals:SCIE、EI、Scopus

Volume:28

Issue:3

Page Number:209-225

ISSN No.:1370-4621

Key Words:Blind source extraction; Noisy independent component analysis; Generalized autocorrelations; Gaussian moments; Fetal electrocardiogram

Abstract:In the blind source extraction problem, the concept of generalized autocorrelations has been successfully used when the desired signal has special temporal structures. However, their applications are only limited to noise-free mixtures, which is not realistic. Therefore, this paper addresses the extraction of the noisy model based on these temporal characteristics of sources. An objective function, which combines Gaussian moments and generalized autocorrelations, is proposed. Maximizing this objective function, we present a blind source extraction algorithm for noisy mixtures. Simulations on synthesized signals, images, artificial electrocardiogram (ECG) data and the real-world ECG data show the better performance of the proposed algorithm. Moreover, comparisons with the existing algorithms further indicate its validity and also show its robustness to the estimated error of time delay.