Towards automatic home-based sleep apnea estimation using deep learning.

Apnea-hypopnea indices; Home-based; Polysomnography; Pulse oximetry; Pulse-oximetry; Single sensor; Sleep apnea; Sleep disorders; Sleep studies; Wearable devices; Medicine (miscellaneous); Health Informatics; Computer Science Applications; Health Information Management

Abstract :

[en] Apnea and hypopnea are common sleep disorders characterized by the obstruction of the airways. Polysomnography (PSG) is a sleep study typically used to compute the Apnea-Hypopnea Index (AHI), the number of times a person has apnea or certain types of hypopnea per hour of sleep, and diagnose the severity of the sleep disorder. Early detection and treatment of apnea can significantly reduce morbidity and mortality. However, long-term PSG monitoring is unfeasible as it is costly and uncomfortable for patients. To address these issues, we propose a method, named DRIVEN, to estimate AHI at home from wearable devices and detect when apnea, hypopnea, and periods of wakefulness occur throughout the night. The method can therefore assist physicians in diagnosing the severity of apneas. Patients can wear a single sensor or a combination of sensors that can be easily measured at home: abdominal movement, thoracic movement, or pulse oximetry. For example, using only two sensors, DRIVEN correctly classifies 72.4% of all test patients into one of the four AHI classes, with 99.3% either correctly classified or placed one class away from the true one. This is a reasonable trade-off between the model's performance and the patient's comfort. We use publicly available data from three large sleep studies with a total of 14,370 recordings. DRIVEN consists of a combination of deep convolutional neural networks and a light-gradient-boost machine for classification. It can be implemented for automatic estimation of AHI in unsupervised long-term home monitoring systems, reducing costs to healthcare systems and improving patient care.

Disciplines :

Life sciences: Multidisciplinary, general & others

Author, co-author :

RETAMALES BARAONA, Maria Gabriela ^✱; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > AI Modelling and Prediction

GAVIDIA, Marino ^✱; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Systems Control > Team Jorge GONCALVES

BAUSCH, Ben ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Imaging AI

Montanari, Arthur N ; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg ; Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA

HUSCH, Andreas ; University of Luxembourg

GONCALVES, Jorge ^✱; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Systems Control ; Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, UK. jmg77@cam.ac.uk

^✱ These authors have contributed equally to this work.

External co-authors :

Language :

English

Title :

Towards automatic home-based sleep apnea estimation using deep learning.

Publication date :

01 June 2024

Journal title :

npj Digital Medicine

eISSN :

2398-6352

Publisher :

Nature Research, England

Volume :

Issue :

Pages :

144

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.nature.com/articles/s41746-024-01139-z.pdf

Funders :

Fonds National de la Recherche Luxembourg
Fonds National de la Recherche Luxembourg
Fonds National de la Recherche Luxembourg

Funding number :

PRIDE15/10907093/CriTiCS; AFR/17022833; INTER/DFG/21/15020234; 458610525

Funding text :

The authors acknowledge support from the Luxembourg National Research Fund (FNR) through grants PRIDE15/10907093/CriTiCS, AFR/17022833 and INTER/DFG/21/15020234, the latter is co-funded by the Deutsche Forschungsgemeinschaft (DFG) project number 458610525.

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