Published in:
18-08-2021 | Sleep Apnea | Sleep Breathing Physiology and Disorders • Original Article
Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation
Authors:
Wolfgang Ganglberger, Abigail A. Bucklin, Ryan A. Tesh, Madalena Da Silva Cardoso, Haoqi Sun, Michael J. Leone, Luis Paixao, Ezhil Panneerselvam, Elissa M. Ye, B. Taylor Thompson, Oluwaseun Akeju, David Kuller, Robert J. Thomas, M. Brandon Westover
Published in:
Sleep and Breathing
|
Issue 3/2022
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Abstract
Objective
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.
Design
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.
Results
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.
Conclusions
A wearable respiratory effort signal with or without SpO2 signal predicted AHI accurately, and best performance was achieved with using both signals.