ArticleERJ open research2025
Characterising dysfunctional breathing seen in post-acute sequelae of SARS-CoV-2 using approximate entropy.
Article in ERJ open research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Abnormal breathing patterns and hyperventilation are common in patients with chronic fatigue syndrome during exercise.Frontiers in medicine · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
Rationale: Dysfunctional breathing (DB) is a commonly identified abnormality in post-acute sequelae of SARS-CoV-2 (PASC) patients undergoing cardiopulmonary exercise testing (CPET), and is potentially a contributor to ongoing symptoms. Currently, this oscillating, irregular breathing pattern is identified by visual observation of CPET data by an experienced interpreter, which is subjective. We hypothesise that approximate entropy (ApEn), a regularity statistic that quantifies the unpredictability of time-series data can reliably distinguish DB from normal breathing states. Methods: Breath-by-breath CPET data were obtained for 82 PASC subjects and 25 controls. CPETs were visually analysed for DB prior to analysis. Minute ventilation ( Results: The ApEn Conclusions: Identifying DB on CPET requires visual recognition, which has limitations. ApEn
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