Isolation of multiple electrocardiogram artifacts using independent vector analysis

Abstract

Electrocardiogram (ECG) signals are normally contaminated by various physiological and nonphysiological artifacts. Among these artifacts baseline wandering, electrode movement and muscle artifacts are particularly difficult to remove. Independent component analysis (ICA) is a well-known technique of blind source separation (BSS) and is extensively used in literature for ECG artifact elimination. In this article, the independent vector analysis (IVA) is used for artifact removal in the ECG data. This technique takes advantage of both the canonical correlation analysis (CCA) and the ICA due to the utilization of second-order and high order statistics for un-mixing of the recorded mixed data. The utilization of recorded signals along with their delayed versions makes the IVA-based technique more practical. The proposed technique is evaluated on real and simulated ECG signals and it shows that the proposed technique outperforms the CCA and ICA because it removes the artifacts while altering the ECG signals minimally.

Implementation of Tools for Lessening the Influence of Artifacts in EEG Signal Analysis

“…Jiang et al provide, in [5], a review of artifact removal techniques, which includes regression methods, the use of wavelet transform (WT), and blind source separation schemes, including PCA, ICA, canonical correlation analysis (CCA), empirical mode decomposition schemes, filtering methods, and other hybrid techniques. Among them, ICA, which is our selected analysis tool, seems to have attracted the most attention in the past years, even in other related contexts, such as electrocardiogram signal analyses [6]. In spite of the many existent proposals, the authors still observe artifact removal as an open problem.…”