In this paper we present a method for removing artifacts from biomedical signals acquired by wearable systems, taking advantage of multichannel data acquisition since both artifacts and signals of interest show common features in different channels. In order to take into account the effects of the different paths from the source signals to the sensors, we propose a method based on blind separation of convolutive mixtures: the observed data are seen as linear mixtures of filtered source signals where neither the source signals nor the convolution and mixing processes are known. The only hypothesis we make to recover the original sources is the statistical independence among them. The proposed method was applied on real ECG signals corrupted by motion artifacts with satisfactory results.
Frequency domain approach to blind source separation in ECG monitoring by wearable system RID A-6953-2008
VANELLO, NICOLA;DE ROSSI, DANILO EMILIO;LANDINI, LUIGI
2005-01-01
Abstract
In this paper we present a method for removing artifacts from biomedical signals acquired by wearable systems, taking advantage of multichannel data acquisition since both artifacts and signals of interest show common features in different channels. In order to take into account the effects of the different paths from the source signals to the sensors, we propose a method based on blind separation of convolutive mixtures: the observed data are seen as linear mixtures of filtered source signals where neither the source signals nor the convolution and mixing processes are known. The only hypothesis we make to recover the original sources is the statistical independence among them. The proposed method was applied on real ECG signals corrupted by motion artifacts with satisfactory results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
