Abstract
Manual diaphragmatic electromyography (EMGdi) analysis is laborious and subjective. We present a free, automated, open-source, citable approach (RespMech; RM) for automated ECG reduction, frequency-based noise reduction, and detailed EMGdi analyses. Herein, we assess non-inferiority versus the standard manual analysis and explore the utility of EMGdi integral as a potential marker of diaphragmatic neural drive.
Crural EMGdi (%max) at rest, 40W, highest within-group iso-work rate, and peak exercise was retrospectively assessed in incremental cardiopulmonary exercise tests with EMGdi in 17 mature (8m:9f; age 62±9y) healthy controls and 10 patients with COPD (4m:6f; age 68±7y; FEV1=56±21%pred) by RM vs manual analysis (MA; mean of 2 raters).
RM had strong agreement with MA for EMGdi%max: ICC 0.87 (95% CI [0.81–0.92], comparable to inter-rater agreement: ICC 0.91 (0.85–0.95]. EMGdi integral was strongly associated with EMGdi%max (r=0.88; p<0.001). EMGdi%max was closely associated with related physiologic parameters for both RM and MA, including Vt/Ti (Figure 1).
RespMech performs non-inferiorly to MA, offers more detailed analysis options, and is freely available in the public domain, decreasing barriers to EMGdi as well as respiratory mechanics analysis as part of a combined mechanics-EMG analysis package (RespMech).
Footnotes
Cite this article as Eur Respir J 2022; 60: Suppl. 66, 2801.
This article was presented at the 2022 ERS International Congress, in session “-”.
This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).
- Copyright ©the authors 2022
