ECG Artifact Removal from EMG Recordings using Independent Component Analysis and Adapted Filter

Surface electromyography (sEMG) recordings from trunk or limb muscles are often easily corrupted by electrocardiography (ECG) signals. In order to remove or reduce ECG in sEMG so as to improve the practicability, a novel signal filtering method with joint independent component analysis (ICA) and adaptive filtering (AF) is proposed in this paper. The method is validated with synthetic noisy EMG signals derived from 8-channel real sEMG added with 8-channel ECG recordings. Two groups of sEMG signals and two groups of ECG signals were used to examine the performance of the proposed method in our validation study. Experimental results demonstrate that the ICA+AF signal filtering method achieves better performance on reduction ECG artifact than the conventional Butterworth High-pass filter with 30 Hz cutoff frequency. The proposed method also performed well with 8-channel real ECG contaminated sEMG signals.