Dynamic Structural Causal Modeling for Sleep

2026-08-20

AI Summary

研究使用PCMCI+算法从家庭睡眠呼吸暂停测试记录中学习动态因果图，揭示不同性别和年龄组的睡眠呼吸障碍的因果结构差异，以解决复杂且多变的睡眠呼吸障碍问题。

Abstract

The causal dynamics of sleep-disordered breathing are complex and vary across patient populations, hindering the development of targeted interventions. We learn dynamic causal graphs of sleep-disordered breathing from Home Sleep Apnea Test (HSAT) recordings, revealing systematic differences in causal structure across sex and age subcohorts. We do so using the PCMCI+ algorithm on windowed fractional variables derived from 105 HSAT recordings, exploiting domain knowledge via edge blacklisting and employing bootstrap aggregation to address small subcohort sizes. The learned graphs show that temporal self-dependencies and the apnea-desaturation relationship persist across all cohorts, while other relationships vary substantially.

Problem

• sleep-disordered breathing
• causal dynamics
• patient populations
• targeted interventions

Innovation

• PCMCI+
• dynamic causal graphs
• sleep-disordered breathing
• edge blacklisting
• bootstrap aggregation