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.

本仓库配套提供了与学术论文"解析睡眠呼吸暂停动力学：基于通气控制动力学建模量化环路增益"（Unravelling Sleep Apnea Dynamics: Quantifying Loop Gain using Dynamical Modeling of Ventilatory Control）相关的数据与代码。该论文阐述了一种从呼吸感应体积描记法（respiratory inductance plethysmography）信号中量化环路增益（Loop Gain，LG）的自动化方法，旨在提升睡眠呼吸暂停的精准管理水平。本研究共分析了465例患者的数据，其中400例来自麻省总医院（Massachusetts General Hospital），另有65例为心力衰竭患者。我们提出的方法可在多种呼吸暂停表型下准确估算环路增益。与阻塞性睡眠呼吸暂停患者（环路增益中位数为0.11~0.14；p<0.001）相比，中枢性呼吸暂停指数更高、自相似性更强或合并心力衰竭的患者，其环路增益中位数显著更高，分别为0.19、0.27和0.41。此外，在非快速眼动睡眠（non-rapid eye movement sleep）期以及高海拔环境下，环路增益水平会显著升高。该自动化环路增益估算方法可为睡眠呼吸暂停的表型分型（endotyping）提供一种可规模化、非侵入性的工具，助力个性化管理策略的落地。

搜集汇总

背景与挑战

背景概述

该数据集提供了用于量化呼吸控制动态模型中环路增益(LG)的数据和代码，包含465名患者的临床数据，支持睡眠呼吸暂停的精准分型和个性化管理。数据集还包含不同海拔高度的呼吸数据，为研究呼吸控制系统的动态特性提供了重要资源。