Problem
Research questions and friction points this paper is trying to address.
Obstructive Sleep Apnea
Oximetry
Diagnosis
Apnea-Hypopnea Index
Clinical Knowledge
KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry
🤖 AI Summary
This study addresses the high cost and limited accessibility of polysomnography for diagnosing obstructive sleep apnea (OSA) by proposing KindSleep, a novel framework that integrates clinical prior knowledge into a deep learning pipeline. Leveraging only a single-channel oxygen saturation signal and clinical metadata, KindSleep identifies interpretable clinical concepts—such as the desaturation index and respiratory disturbance events—to enable multimodal fusion for accurate estimation of the apnea–hypopnea index (AHI). Evaluated across three independent datasets comprising 9,815 subjects, the model achieves an R² of 0.917 (intraclass correlation coefficient: 0.957) for AHI estimation and weighted F1-scores of 0.827–0.941 for OSA severity classification, substantially outperforming existing approaches while maintaining high accuracy and model transparency.
📝 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 outperformed 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
knowledge-informed deep learning
oximetry-based OSA diagnosis
clinically interpretable concepts
Apnea-Hypopnea Index estimation
multimodal fusion
🔎 Similar Papers
No similar papers found.
