Towards Automatic Home-based Sleep Apnea Estimation Using Deep Learning

Overview

Journal NPJ Digit Med

Specialty Medical Informatics

Date 2024 Jun 1

PMID 38824175

Authors

Gabriela Retamales

Marino E Gavidia

Ben Bausch

Arthur N Montanari

Andreas Husch

Jorge Goncalves

Affiliations

Soon will be listed here.

Abstract

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.

References

Kotzen K, Charlton P, Salabi S, Amar L, Landesberg A, Behar J . SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography. IEEE J Biomed Health Inform. 2022; 27(2):924-932. DOI: 10.1109/JBHI.2022.3225363. View

Chen X, Wang R, Zee P, Lutsey P, Javaheri S, Alcantara C . Racial/Ethnic Differences in Sleep Disturbances: The Multi-Ethnic Study of Atherosclerosis (MESA). Sleep. 2014; 38(6):877-88. PMC: 4434554. DOI: 10.5665/sleep.4732. View

Chen S, Redline S, Eden U, Prerau M . Dynamic models of obstructive sleep apnea provide robust prediction of respiratory event timing and a statistical framework for phenotype exploration. Sleep. 2022; 45(12). PMC: 9742895. DOI: 10.1093/sleep/zsac189. View

Ramachandran A, Karuppiah A . A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems. Healthcare (Basel). 2021; 9(7). PMC: 8306425. DOI: 10.3390/healthcare9070914. View

Yumino D, Kasai T, Kimmerly D, Amirthalingam V, Floras J, Bradley T . Differing effects of obstructive and central sleep apneas on stroke volume in patients with heart failure. Am J Respir Crit Care Med. 2012; 187(4):433-8. DOI: 10.1164/rccm.201205-0894OC. View

Alvarez-Estevez D, Moret-Bonillo V . Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review. Sleep Disord. 2015; 2015:237878. PMC: 4523666. DOI: 10.1155/2015/237878. View

Riha R . Defining obstructive sleep apnoea syndrome: a failure of semantic rules. Breathe (Sheff). 2022; 17(3):210082. PMC: 8753646. DOI: 10.1183/20734735.0082-2021. View

Punjabi N . The epidemiology of adult obstructive sleep apnea. Proc Am Thorac Soc. 2008; 5(2):136-43. PMC: 2645248. DOI: 10.1513/pats.200709-155MG. View

Senaratna C, Perret J, Lodge C, Lowe A, Campbell B, Matheson M . Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Med Rev. 2016; 34:70-81. DOI: 10.1016/j.smrv.2016.07.002. View

10.

Huang L, Qin J, Zhou Y, Zhu F, Liu L, Shao L . Normalization Techniques in Training DNNs: Methodology, Analysis and Application. IEEE Trans Pattern Anal Mach Intell. 2023; 45(8):10173-10196. DOI: 10.1109/TPAMI.2023.3250241. View

11.

Mendonca F, Mostafa S, Ravelo-Garcia A, Morgado-Dias F, Penzel T . A Review of Obstructive Sleep Apnea Detection Approaches. IEEE J Biomed Health Inform. 2018; 23(2):825-837. DOI: 10.1109/JBHI.2018.2823265. View

12.

Ganapathy N, Swaminathan R, Deserno T . Deep Learning on 1-D Biosignals: a Taxonomy-based Survey. Yearb Med Inform. 2018; 27(1):98-109. PMC: 6115218. DOI: 10.1055/s-0038-1667083. View

13.

Levy J, Alvarez D, Campo F, Behar J . Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry. Nat Commun. 2023; 14(1):4881. PMC: 10423260. DOI: 10.1038/s41467-023-40604-3. View

14.

McNicholas W . Diagnosis of obstructive sleep apnea in adults. Proc Am Thorac Soc. 2008; 5(2):154-60. DOI: 10.1513/pats.200708-118MG. View

15.

Blackwell T, Yaffe K, Ancoli-Israel S, Redline S, Ensrud K, Stefanick M . Associations between sleep architecture and sleep-disordered breathing and cognition in older community-dwelling men: the Osteoporotic Fractures in Men Sleep Study. J Am Geriatr Soc. 2011; 59(12):2217-25. PMC: 3245643. DOI: 10.1111/j.1532-5415.2011.03731.x. View

16.

Ribeiro A, Horta Ribeiro M, Paixao G, Oliveira D, Gomes P, Canazart J . Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020; 11(1):1760. PMC: 7145824. DOI: 10.1038/s41467-020-15432-4. View

17.

Faust O, Hagiwara Y, Hong T, Lih O, Rajendra Acharya U . Deep learning for healthcare applications based on physiological signals: A review. Comput Methods Programs Biomed. 2018; 161:1-13. DOI: 10.1016/j.cmpb.2018.04.005. View

18.

Hutchison K, Song Y, Wang L, Malow B . Analysis of sleep parameters in patients with obstructive sleep apnea studied in a hospital vs. a hotel-based sleep center. J Clin Sleep Med. 2008; 4(2):119-22. PMC: 2335406. View

19.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

20.

Zhang G, Cui L, Mueller R, Tao S, Kim M, Rueschman M . The National Sleep Research Resource: towards a sleep data commons. J Am Med Inform Assoc. 2018; 25(10):1351-1358. PMC: 6188513. DOI: 10.1093/jamia/ocy064. View