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

Autor: Ganglberger W; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.; Sleep & Health Zurich, University of Zurich, Zurich, Switzerland., Bucklin AA; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA., Tesh RA; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA., Da Silva Cardoso M; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA., Sun H; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA., Leone MJ; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA., Paixao L; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.; Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, 63110, USA., Panneerselvam E; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA., Ye EM; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA., Thompson BT; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA., Akeju O; Department of Anesthesiology, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA., Kuller D; MyAir Inc., Boston, MA, USA., Thomas RJ; Division of Pulmonary, Critical Care & Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA., Westover MB; Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA. mwestover@mgh.harvard.edu.
Jazyk: angličtina
Zdroj: Sleep & breathing = Schlaf & Atmung [Sleep Breath] 2022 Sep; Vol. 26 (3), pp. 1033-1044. Date of Electronic Publication: 2021 Aug 18.
DOI: 10.1007/s11325-021-02465-2
Abstrakt: 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 SpO 2 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 SpO 2 (%)-signal only, and two additional models that use the respiratory features and the SpO 2 (%) 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 SpO 2 , respiration-only, and SpO 2 -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 SpO 2 signal predicted AHI accurately, and best performance was achieved with using both signals.
(© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
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