Evidence map›Paper›PMID 40942892›Full record

ArticleSensors (Basel, Switzerland)2025

Estimation of Respiratory Effort Through Diaphragmatic Electromyography Features.

Gabriela Grońska, Elisabetta Peri, Xi Long, Sebastiaan Overeem, Johannes van Dijk, Massimo Mischi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Gabriela GrońskaDepartment of Electrical Engineering, Eindhoven University of Technology, De Groene Loper 19, 5612AP Eindhoven, The Netherlands.ORCID 0009-0008-2915-232X
Elisabetta PeriDepartment of Electrical Engineering, Eindhoven University of Technology, De Groene Loper 19, 5612AP Eindhoven, The Netherlands.ORCID 0000-0002-1231-9372
Xi LongDepartment of Electrical Engineering, Eindhoven University of Technology, De Groene Loper 19, 5612AP Eindhoven, The Netherlands.ORCID 0000-0001-9505-1270
Sebastiaan OvereemDepartment of Electrical Engineering, Eindhoven University of Technology, De Groene Loper 19, 5612AP Eindhoven, The Netherlands.ORCID 0000-0002-6445-9836
Johannes van DijkDepartment of Electrical Engineering, Eindhoven University of Technology, De Groene Loper 19, 5612AP Eindhoven, The Netherlands.ORCID 0000-0001-6188-2973
Massimo MischiDepartment of Electrical Engineering, Eindhoven University of Technology, De Groene Loper 19, 5612AP Eindhoven, The Netherlands.ORCID 0000-0002-1179-5385

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

DiaphragmElectromyographySleep Apnea, ObstructiveAdultFemaleHumansMaleMiddle AgedRespirationSignal Processing, Computer-Assisteddiaphragmatic electromyographyesophageal pressurefeaturesobstructive sleep apnearespiratory effort

Identifiers

PMID40942892
PMCPMC12431343

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.