Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation

Sleep-related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to detect sleep apnea automatically from a simple, easy-to-wear device. The objective was to detect abnormal respiration and estimate the Apnea-Hypopnea Index (AHI) automatically with a wearable respiratory device with and without SpO₂ signals using a large (n = 412) dataset serving as ground truth.

Simultaneously recorded polysomnography (PSG) and wearable respiratory effort data were used to train and evaluate models in a cross-validation fashion. Time domain and complexity features were extracted, important features were identified, and a random forest model was employed to detect events and predict AHI. Four models were trained: one each using the respiratory features only, a feature from the SpO₂ (%)-signal only, and two additional models that use the respiratory features and the SpO₂ (%) feature, one allowing a time lag of 30 s between the two signals.

Event-based classification resulted in areas under the receiver operating characteristic curves of 0.94, 0.86, and 0.82, and areas under the precision-recall curves of 0.48, 0.32, and 0.51 for the models using respiration and SpO₂, respiration-only, and SpO₂-only, respectively. Correlation between expert-labelled and predicted AHI was 0.96, 0.78, and 0.93, respectively.

A wearable respiratory effort signal with or without SpO₂ signal predicted AHI accurately, and best performance was achieved with using both signals.