ArticleScientific reports2020
Autonomous control of ventilation through closed-loop adaptive respiratory pacing.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed.
- A Computational Model of the Respiratory CPG for the Artificial Control of Breathing.Bioengineering (Basel, Switzerland) · 2025Article
- The road toward a physiological control of artificial respiration: the role of bio-inspired neuronal networks.Frontiers in neuroscience · 2025Article
- Validation of an Enhanced Telehealth Platform for Toddlers at Increased Likelihood for a Diagnosis of Autism Spectrum Disorder (ASD).Journal of autism and developmental disorders · 2024Article
- Co-activation of the diaphragm and external intercostal muscles through an adaptive closed-loop respiratory pacing controller.Frontiers in rehabilitation sciences · 2023Article
- Supervisory Algorithm for Autonomous Hemodynamic Management Systems.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Mechanical ventilation is the standard treatment when volitional breathing is insufficient, but drawbacks include muscle atrophy, alveolar damage, and reduced mobility. Respiratory pacing is an alternative approach using electrical stimulation-induced diaphragm contraction to ventilate the lung. Oxygenation and acid-base homeostasis are maintained by matching ventilation to metabolic needs; however, current pacing technology requires manual tuning and does not respond to dynamic user-specific metabolic demand, thus requiring re-tuning of stimulation parameters as physiological changes occur. Here, we describe respiratory pacing using a closed-loop adaptive controller that can self-adjust in real-time to meet metabolic needs. The controller uses an adaptive Pattern Generator Pattern Shaper (PG/PS) architecture that autonomously generates a desired ventilatory pattern in response to dynamic changes in arterial CO
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Registered trials
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