Accelerometry-derived respiratory index estimating apnea-hypopnea index for sleep apnea screening. (August 2021)

• Record Type: Journal Article
• Authors: Bricout, Aurélien; Fontecave-Jallon, Julie; Pépin, Jean-Louis; Guméry, Pierre-Yves
• Is Part Of: Computer methods and programs in biomedicine. Volume 207(2021)
• Publication Date: 2021-08
• Subjects: Polysomnography -- Screening -- Sleep apnea syndrome -- Machine learning -- Accelerometry -- Respiration; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106209
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract: Background and objective: Sleep Apnea Syndrome (SAS) is a multimorbid chronic disease with individual and societal deleterious consequences. Polysomnography (PSG) is the multi-parametric reference diagnostic tool that allows a manual quantification of the apnea-hypopnea index (AHI) to assess SAS severity. The burden of SAS is affecting nearly one billion people worldwide explaining that SAS remains largely under-diagnosed and undertreated. The development of an easy to use and automatic solution for early detection and screening of SAS is highly desirable. Methods: We proposed an Accelerometry-Derived Respiratory index (ADR) solution based on a dual accelerometry system for airflow estimation included in a machine learning process. It calculated the AHI thanks to a RUSBoosted Tree model and used physiological and explanatory specifically developed features. The performances of this method were evaluated against a configuration using gold-standard PSG signals on a database of 28 subjects. Results: The AHI estimation accuracy, specificity and sensitivity of the ADR index were 89%, 100% and 80% respectively. The added value of the specifically developed features was also demonstrated. Conclusion: Overnight physiological monitoring with the proposed ADR solution using a machine learning approach provided a clinically relevant estimate of AHI for SAS screening. The physiological component of the solution has a real interest for improving performance and facilitating physician's adhesion to an automatic AHI estimation. Highlights: Sleep Apnea Syndrome is a multimorbid chronic disease largely under-diagnosed. The development of automatic solution for early SAS detection is highly desirable. Accelerometry Derived Respiration Index can improve the screening of sleep apnea. Machine Learning approaches using explanatory features may improve physician's adhesion. Interchangeability was demonstrated between nasal canula and Accelerometry Derived Respiration Index.