Isolation of multiple electrocardiogram artifacts using independent vector analysis

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.

Automated summary

This article presents a technique using independent vector analysis (IVA) for eliminating various artifacts in electrocardiogram (ECG) signals. The technique combines canonical correlation analysis (CCA) and independent component analysis (ICA) to effectively separate mixed data. The proposed technique is practical and outperforms CCA and ICA by minimizing changes to the ECG signals while removing artifacts.