KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry

TL;DR

KindSleep is introduced, a deep learning framework that integrates clinical knowledge with single-channel patient-specific oximetry signals and clinical data for precise OSA diagnosis and provides a more transparent and trustworthy diagnostic tool for sleep medicine practices.

Abstract

Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. Traditional diagnosis through polysomnography is resource-intensive and limits widespread access, creating a critical need for accurate and efficient alternatives. In this paper, we introduce KindSleep, a deep learning framework that integrates clinical knowledge with single-channel patient-specific oximetry signals and clinical data for precise OSA diagnosis. KindSleep first learns to identify clinically interpretable concepts, such as desaturation indices and respiratory disturbance events, directly from raw oximetry signals. It then fuses these AI-derived concepts with multimodal clinical data to estimate the Apnea-Hypopnea Index (AHI). We evaluate KindSleep on three large, independent datasets from the National Sleep Research Resource (SHHS, CFS, MrOS; total n = 9,815). KindSleep demonstrates excellent performance in estimating AHI scores (R2 = 0.917, ICC = 0.957) and consistently outperforms existing approaches in classifying OSA severity, achieving weighted F1-scores from 0.827 to 0.941 across diverse populations. By grounding its predictions in a layer of clinically meaningful concepts, KindSleep provides a more transparent and trustworthy diagnostic tool for sleep medicine practices.

Figures (7)

• Figure 1: Overview of KindSleep. KindSleep involved two main components: the sleep annotation model, which extracts clinically relevant metrics from raw oximetry signals, and the regression model, which integrates these metrics with processed clinical data to estimate the AHI. (Right) Example of oximetry signals from a mild OSA patient (top; reference AHI = 5.65) and a healthy control (bottom; reference AHI = 0.175), annotated with hypopnea events (green) and desaturations (red), alongside corresponding attention maps from the sleep annotation model that highlight the regions the model concentrates on, and the resulting AHI estimations from the regression model.
• Figure 2: (a) Parity plots, (b) Bland–Altman plots, and (c) confusion matrix results for SHHS1, SHHS2, CFS and MrOS.
• Figure 3: Outcome comparison across varying proportions of knowledge-informed metrics.
• Figure 4: Radar charts comparing various performance metrics of our KindSleep model against two baseline multimodal integration methods on the (a) SHHS1, (b) SHHS2, (c) CFS.
• Figure 5: Attention mechanism employed by the SLAM model across oximetry signals, with events (e.g., desaturation, apnea, and artifacts) identified from ground truth annotations. The top section displays the global signal over the full duration (0–25,200 seconds), highlighting areas of high activation that correspond to physiologically relevant events, such as desaturation and apnea, while effectively ignoring artifact-prone regions. The middle section provides a focused view of the signal between 5,000 and 9,000 seconds, where the model demonstrates precise attention on desaturation events and hypopneas. The bottom section zooms into a segment from 20,000 to 25,000 seconds, showcasing the model’s ability to neglect low-amplitude, artifact-dominated regions devoid of clinically significant activities.