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Unraveling sleep apnea dynamics: quantifying loop gain using dynamical modeling of ventilatory control
Thijs Nassi1,2, Yalda Amidi1, Eline Oppersma2
1Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA, United States.
Sleep
|July 24, 2025
Summary
This study introduces an automated method to measure loop gain (LG) from respiratory inductance plethysmography (RIP) signals, improving sleep apnea diagnosis and treatment. The new technique accurately quantifies LG across different patient types, aiding personalized care.
Area of Science:
- Sleep Medicine
- Respiratory Physiology
- Biomedical Engineering
Background:
- Loop gain (LG) is crucial for assessing ventilatory control stability in sleep apnea.
- Current LG estimation methods are complex and require specialized equipment, limiting clinical use.
- Automated quantification of LG can enhance precision management of sleep apnea.
Purpose of the Study:
- To develop an automated method for quantifying loop gain (LG) from respiratory inductance plethysmography (RIP) signals.
- To improve the precision management of sleep apnea through enhanced ventilatory control assessment.
- To provide a scalable and non-invasive tool for sleep apnea endotyping.
Main Methods:
- Analyzed polysomnography data from diverse patient cohorts, including those with sleep apnea and heart failure.
- Filtered, normalized, and segmented RIP signals into 8-minute windows for analysis.
- Employed an augmented Mackey-Glass equation and expectation-maximization algorithm for LG estimation, validated with synthetic data.
Main Results:
- Successfully analyzed data from 465 patients, demonstrating accurate LG estimation across various apnea phenotypes.
- Identified significantly higher median LG values in patients with central apnea, high self-similarity, or heart failure compared to obstructive apnea.
- Observed significantly elevated LG during non-rapid eye movement sleep and at higher altitudes.
Conclusions:
- The developed automated LG estimation method offers a scalable, non-invasive tool for sleep apnea endotyping.
- Accurate modeling of patient-specific ventilatory control supports personalized management strategies.
- This approach has broader clinical implications beyond sleep apnea management.
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