Detection and Severity Classification of Sleep Apnea Using Continuous Wearable SpO2 Signals: A Multi-Scale Feature Approach

The use of wearable devices for sleep apnea detection is growing, but their limited signal resolution poses challenges for accurate diagnosis. This study explores the feasibility of using SpO₂ signals from wearable sensors for detecting sleep apnea and classifying its severity. We propose a novel multi-scale feature engineering approach, which extracts features from coarsely grained SpO₂ signals across timescales ranging from 1 s to 600 s. Our results show that traditional SpO₂ markers, such as the oxygen desaturation index (ODI) and Lempel-Zip complexity, lose their relevance with the Apnea-Hypopnea Index (AHI) at longer timescales. In contrast, non-linear features like complex entropy, sample entropy, and fuzzy entropy maintain strong correlations with AHI, even at the coarsest timescales (up to 600 s), making them well suited for low-resolution data. Multi-scale feature extraction improves model performance across various machine learning algorithms by alleviating model bias, particularly with the Bayes and CatBoost models. These findings highlight the potential of multi-scale feature engineering for wearable device applications where only low-resolution data are commonly available. This could improve accessibility to low-cost, at-home sleep apnea screening, reducing reliance on expensive and labor-intensive polysomnography. Moreover, it would allow even healthy individuals to proactively monitor their sleep health at home, facilitating the early identification of potential sleep problems.