Abstract
Background: Electrocardiogram (ECG) contamination is present in diaphragm electromyography (EMGdi) recordings. Obtaining EMGdi without ECG contamination is crucial for EMG amplitude analysis. Manually selecting EMGdi in between QRS complexes has been most commonly applied in recent years (manual method). We developed a semi-automated analysis method based on Least Mean Square Adaptive Filtering combined with a synchronously recorded separate ECG channel to remove ECG artifacts from the EMGdi signals. We hypothesized that this approach would shorten analysis duration and might minimize the potential for inter-rater disagreement.Aims: We aimed to evaluate agreement between the semi-automated method and the manual method and inter-rater reliability of the manual method.Methods: Electromyography signals of seven patients with COPD were recorded using an esophageal catheter during an exercise test on a cycle ergometer. Four patients subsequently participated in an inspiratory muscle training (IMT) program for 8 weeks. After IMT, the tests were repeated. EMGdi/EMGdiMax as obtained either manually by the two assessors or retrieved from the semi-automated method were compared.Results: Semi-automated EMGdi/EMGdiMax agreed well with values obtained by one of the two manual assessors (assessor 1) both at pre-intervention measurements (mean difference −0.5%, 95% CI: −19.6 to 18.6%) and for the pre/post IMT differences (mean difference 1.2%, 95% CI: −16.8 to 19.2%). Intra-class correlation coefficients between methods were 0.96 (95% CI: 0.94–0.97) at pre-intervention measurements and 0.78 (95% CI: 0.58–0.89) for pre/post IMT differences (both p < 0.001). EMGdi/EMGdiMax from assessor 2 was systematically lower than from assessor 1 and agreed less well with the semi-automated method both at pre-intervention measurements (mean difference: 9.3%, 95% CI: −11.4 to 29.9%) and for pre/post IMT differences (mean difference 7.0%, 95% CI: −20.4 to 34.4%). Analysis duration of the semi-automated method was significantly shorter (29 ± 9 min) than the manual method (82 ± 20 min, p < 0.001).Conclusion: The developed semi-automated method is more time efficient and will be less prone to inter-rater variability that was observed when applying the manual analysis method. It is, therefore, proposed as a new standard for objective EMGdi amplitude analyses in future studies.
Highlights
Electromyography (EMG) is an assessment of muscle activation by recording the electrical activity of the muscle tissue
The intra-class correlation coefficients (ICC) between diaphragm activation signals obtained with the manual methods by two assessors at pre-measurement was 0.94, p < 0.0001, 95% confidence intervals (CI): 0.17–0.98 (Figure 4A)
The ICC between EMGdi signals from the semi-automated method and the results obtained by using the manual method from assessor 1 and 2 at pre-measurement were 0.96, p < 0.0001, 95% CI: 0.94–0.97 (Figure 4B) and 0.91, p < 0.0001, 95% CI: 0.60–0.97 (Figure 4C), respectively
Summary
Electromyography (EMG) is an assessment of muscle activation by recording the electrical activity of the muscle tissue. Separating ECG from EMGdi is challenging in EMG amplitude analyses, especially during exercise, since the EMGdi amplitude can be larger than the ECG. This makes it more difficult to identify ECG artifacts within the EMG signal. We developed a semi-automated analysis method based on Least Mean Square Adaptive Filtering combined with a synchronously recorded separate ECG channel to remove ECG artifacts from the EMGdi signals. We hypothesized that this approach would shorten analysis duration and might minimize the potential for inter-rater disagreement
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