Key result
An automated ICA-based ECG-artifact detection algorithm recognized ECG components with 100% sensitivity and 99% specificity, yielding significantly higher correlation with original EMG than a Butterworth filter (p<0.001).
Why the study?
Does an automated ICA-based method improve ECG artifact detection and removal from trunk SEMG recordings compared to visual inspection and standard high-pass filtering?
Does an automated ICA-based method improve ECG artifact detection and removal from trunk SEMG recordings compared to visual inspection and standard high-pass filtering?
p-value: p=<0.001
An automated ICA-based method successfully detects and removes ECG artifacts from trunk SEMG signals with high sensitivity and specificity, outperforming standard high-pass filtering.
May improve trunk SEMG processing efficiency; leaves open prospective clinical validation before adoption.
This study aimed at developing a method for automated electrocardiography (ECG) artifact detection and removal from trunk electromyography signals. Independent Component Analysis (ICA) method was applied to the simulated data set of ECG-corrupted surface electromyography (SEMG) signals. Independent Components (ICs) correspond to ECG artifact were then identified by an automated detection algorithm and subsequently removed. The detection performance of the algorithm was compared to that by visual inspection, while the artifact elimination performance was compared with Butterworth high pass filter at 30 Hz cutoff (BW HPF 30). The automated ECG-artifact detection algorithm successfully recognized the ECG source components in all data sets with a sensitivity of 100% and specificity of 99%. Better performance indicated by a significantly higher correlation coefficient (p<0.001) with the original EMG recordings was found in the SEMG data cleaned by the ICA-based method, than that by BW HPF 30. The automated ECG-artifact removal method for trunk SEMG recordings proposed in this study was demonstrated to produce a very good detection rate and preserved essential EMG components while keeping its distortion to minimum. The automatic nature of our method has solved the problem of visual inspection by standard ICA methods and brings great clinical benefits.
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Mak et al. (2010) studied ECG artifacts in trunk electromyography signals. Automated Independent Component Analysis (ICA)-based ECG-artifact detection and removal method vs. Visual inspection and Butterworth high pass filter at 30 Hz cutoff was evaluated on Detection performance (sensitivity and specificity) and artifact elimination performance (correlation coefficient with original EMG) (p=<0.001). An automated ICA-based ECG-artifact detection algorithm recognized ECG components with 100% sensitivity and 99% specificity, yielding significantly higher correlation with original EMG than a Butterworth filter (p<0.001).
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