Dynamic Structural Causal Modeling for Sleep

Ranveer Singh; Saurabh Mathur; Pranuthi Tenali; Arun Badi; Sriraam Natarajan

cs.LGAug 20, 2026

Dynamic Structural Causal Modeling for Sleep

Authors: Ranveer Singh, Saurabh Mathur, Pranuthi Tenali, Arun Badi, Sriraam Natarajan

Organizations: The University of Texas at Dallas, Richardson, USA · TU Darmstadt, Germany · ENT & Sleep Medicine of Dallas, Dallas, USA

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.

Figures & tables

Panel of Figure 1 — Figure 1: The Average Time Series Causal Graph across the different subcohorts and the entire cohort learnt using PCMCI $+$ . The color of the edges indicates the sign of the Momentary Conditional Independence (MCI), with green being positive and red being negative. The thickness of the edge indicates the magnitude of MCI, with greater thickness corresponding to a larger magnitude.

Figure 2: Age distribution for the patients based on sex

Feature	Description
	(Fraction of the analysis window)
Apnea Hypopnea Events (A)	Occupied by Apnea and Hypopnea events [ 14 ]
Desaturation (D)	Spent in Oxygen Desaturation episodes [ 23 ]
Flow Limitation (I)	Exhibiting inspiratory flow-limited breathing [ 10 ]
Flow Limited Respiratory Effort (F)	Characterized by Flow-limited breathing
	with elevated respiratory effort

Table 1: Constructed Windowed Features as a fraction of the analysis window

Feature	All	Male	Female	Old	Young
A	0.02 $\scriptstyle\pm 0.06$	$0.02\scriptstyle\pm 0.06$	$0.02\scriptstyle\pm 0.06$	$0.02\scriptstyle\pm 0.05$	$0.03\scriptstyle\pm 0.07$
D	$0.02\scriptstyle\pm 0.02$	$0.02\scriptstyle\pm 0.03$	$0.01\scriptstyle\pm 0.02$	$0.01\scriptstyle\pm 0.02$	$0.02\scriptstyle\pm 0.03$
F ( $\times 10^{-5}$ )	$1.36\scriptstyle\pm 7.47$	$2.43\scriptstyle\pm 9.89$	$0.00\scriptstyle\pm 0.00$	$1.30\scriptstyle\pm 7.61$	$1.61\scriptstyle\pm 7.36$
I	$0.29\scriptstyle\pm 0.12$	$0.28\scriptstyle\pm 0.13$	$0.30\scriptstyle\pm 0.10$	$0.30\scriptstyle\pm 0.11$	$0.24\scriptstyle\pm 0.12$
P	$0.03\scriptstyle\pm 0.03$	$0.04\scriptstyle\pm 0.03$	$0.03\scriptstyle\pm 0.02$	$0.03\scriptstyle\pm 0.03$	$0.03\scriptstyle\pm 0.03$
S	$0.25\scriptstyle\pm 0.14$	$0.25\scriptstyle\pm 0.12$	$0.26\scriptstyle\pm 0.16$	$0.25\scriptstyle\pm 0.14$	$0.25\scriptstyle\pm 0.15$

Table 2: Average value of the fractional features across the different cohorts

Time-Series Causal Discovery

Time-series causal discovery seeks to infer which variables influence others over time and when those effects occur.

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Jun 16, 2026J/K move · Enter open · S save

Beyond AHI: An Interpretable Causal-Discovery-Guided Framework for Sleep Recovery in Connected Health

Saba A. Farahani, Elahe Khatibi, Manoj Vishwanath, Amir M. Rahmani+1

University of California, Irvine, CA, USA

Clinical Outcome Prediction

Open paper AskPDF arXiv

Objective sleep assessment relies on polysomnography (PSG), yet clinical impact is often better reflected in patient-reported outcomes (PROs) such as sleepiness and fatigue. Existing summary indices, including the Apnea-Hypopnea Index (AHI), provide limited insight into the multidomain physiology underlying functional recovery. We propose an interpretable, causal-discovery-guided framework for deriving a hierarchical Sleep Recovery Score (SRS) from multimodal PSG. Using two large population cohorts (MESA:

n=1{,}540

; MrOS:

n=825

), we apply directed acyclic graph (DAG) learning to identify candidate physiological drivers spanning respiratory burden, hypoxic burden, sleep fragmentation, sleep architecture, and autonomic regulation. Although derived from clinical PSG, these domains map naturally to sensing streams increasingly available in connected health technologies, including wearable ECG, oximetry, and sleep-stage estimation devices. To preserve mechanistic plausibility, we introduce a two-stage screening process that combines physiology-based constraints with constrained LLM-assisted auditing to identify and remove structural confounders and construct-overlapping variables. Across cohorts, these five domains emerge as recurrent physiological domains associated with recovery, and the resulting SRS shows up to

3.4\times

stronger alignment with perceived recovery than AHI. By linking multimodal sleep physiology to patient-centered outcomes through an interpretable, bias-aware, and domain-structured framework, this work provides a practical foundation for recovery modeling across both clinical sleep studies and emerging smart and connected health settings.

Abstract

Time-Series Causal Discovery

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