Dynamic Structural Causal Modeling for Sleep: Learning Causal Graphs from Home Sleep Apnea Tests

Forum topic · 小凯 · 2026-08-22

Summary

This paper (arXiv:2608.20285) introduces a dynamic structural causal modeling approach for sleep apnea, learning dynamic causal graphs of sleep-disordered breathing directly from home sleep apnea test (HSAT) recordings. The authors apply the PCMCI+ algorithm to windowed score variables derived from 105 HSAT records, incorporating domain knowledge through edge blacklists and using bootstrap aggregation to address the small sizes of patient subgroups. The learned graphs reveal systematic differences in causal structure across sex and age subgroups: temporal self-dependencies and the apnea–desaturation relationship persist across all cohorts, while other causal relationships vary substantially. These findings provide a causal perspective on the heterogeneity of sleep-disordered breathing and may guide the development of more personalized treatment strategies for conditions such as obstructive sleep apnea. The work combines causal discovery, time-series analysis, and clinical sleep data, making it relevant to researchers in machine learning for healthcare and computational sleep medicine.

Paper Overview

Field: Machine Learning Authors: Ranveer Singh, Saurabh Mathur, Pranuthi Tenali, Arun Badi Published: 2026-08-22 arXiv: 2608.20285

Abstract (English Translation)

The causal dynamics of sleep-disordered breathing are complex and vary across patient populations, hindering the development of targeted interventions. This paper learns dynamic causal graphs of sleep-disordered breathing from home sleep apnea test (HSAT) recordings, revealing systematic differences in causal structure between sex and age subgroups.

The authors use the PCMCI+ algorithm on windowed score variables derived from 105 HSAT records. Domain knowledge is incorporated through an edge blacklist, and bootstrap aggregation is employed to address the small subgroup sample sizes.

Key Findings

Persistent relationships: Temporal self-dependencies and the apnea–desaturation relationship persist across all studied cohorts.
Variable relationships: Other causal relationships change substantially between subgroups.
Heterogeneity: The results provide a causal perspective on the heterogeneity of sleep-disordered breathing, potentially informing more personalized treatment strategies.

Methods Summary

Data source: 105 home sleep apnea test (HSAT) recordings
Algorithm: PCMCI+ causal discovery on windowed score variables
Domain knowledge integration: edge blacklisting
Robustness: bootstrap aggregation to handle small subgroup sizes

---

*Automatically collected on 2026-08-22.*