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September 10, 2025Open Access

Estimation of Respiratory Effort Through Diaphragmatic Electromyography Features

GGGabriela GrońskaEindhoven University of Technology EPElisabetta PeriEindhoven University of Technology XLXi LongNational University of Defense Technology

Key Points

Time-domain dEMG features show moderately strong correlations with respiratory effort in OSA patients, indicating their effectiveness.
Features such as the filtered envelope and waveform length exhibit strong performance, with correlations over 0.6 when measured against esophageal pressure.
Comparative analysis used the Kruskal–Wallis test to assess robustness of features across varying signal quality levels.
Findings emphasize the need for selecting optimal dEMG features to ensure reliable estimations of respiratory effort, paving the way for further research.

Abstract

Respiratory effort is a critical parameter for assessing respiratory function in various pathological conditions such as obstructive sleep apnea (OSA), as well as in patients undergoing respiratory ventilation. Currently, the gold-standard method for measuring it is esophageal pressure (Pes), which is obtrusive and uncomfortable for patients. An alternative approach is using diaphragmatic electromyography (dEMG), a non-obtrusive method that directly reflects the electrical drive triggering respiratory effort, holding potential for quantifying effort. Despite progress in this area, there is still no clear agreement on the best features for assessing respiratory effort from dEMG. This feasibility study considers several time, frequency, and statistical domain features, providing a comparative analysis to determine their performance in estimating respiratory effort. In particular, we evaluate the correlation of the different features with Pes using overnight recordings from 10 OSA patients and assess their robustness across different signal quality levels with the Kruskal–Wallis test. Our results support that time-domain dEMG features such as the filtered envelope, root mean square, and waveform length (WL) exhibit moderately strong correlations (R > 0.6) with respiratory effort. In terms of robustness to noise, the best features were WL, the area under the curve, and the slope sign change, demonstrating moderately strong to fair correlations (R > 0.5) even in low- to very low-quality signals. In contrast, features like skewness, the mean frequency, and the median frequency performed poorly (R < 0.3), regardless of signal quality, likely because they focus on overall signal characteristics rather than the dynamic and transient changes associated with respiratory effort by temporal features. These findings highlight the importance of selecting optimal features to obtain a reliable estimation of respiratory effort, providing a foundation for future research on non-intrusive methods.

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