Quantifying Loop Gain using Dynamical Modelling of Ventilatory Control - Data and Code

This repository provides data and code to accompany the manuscript,
"Unravelling Sleep Apnea Dynamics: Quantifying Loop Gain using Dynamical
Modeling of Ventilatory Control". The manuscript describes an an automated
method for quantifying Loop Gain (LG) from respiratory inductance
plethysmography signals to enhance precision management of sleep apnea. We
analyzed data from 465 patients, including 400 from Massachusetts General
Hospital and 65 heart failure patients. Our method accurately estimated LG
across diverse apnea phenotypes. Patients with higher central apnea index,
high self-similarity, or heart failure exhibited significantly higher median
LG values (0.19, 0.27, and 0.41 respectively) compared to those with
obstructive apnea (median LG = 0.11-0.14; p < 0.001). Additionally, LG was
significantly elevated during non-rapid eye movement sleep and at higher
altitudes. This automated LG estimation method provides a scalable, non-
invasive tool for endotyping in sleep apnea to support personalized management
strategies.