Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts. Issue 5 (18th November 2019)

• Record Type: Journal Article
• Authors: Olsen, Mads; Mignot, Emmanuel; Jennum, Poul Jorgen; Sorensen, Helge Bjarup Dissing
• Is Part Of: Sleep. Volume 43:Issue 5(2020)
• Publication Date: 2019-11-18
• Subjects: electrocardiogram -- sleep-disordered breathing -- apnea -- recurrent neural network; Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsz276
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Study Objectives: Up to 5% of adults in Western countries have undiagnosed sleep-disordered breathing (SDB). Studies have shown that electrocardiogram (ECG)-based algorithms can identify SDB and may provide alternative screening. Most studies, however, have limited generalizability as they have been conducted using the apnea-ECG database, a small sample database that lacks complex SDB cases. Methods: Here, we developed a fully automatic, data-driven algorithm that classifies apnea and hypopnea events based on the ECG using almost 10 000 polysomnographic sleep recordings from two large population-based samples, the Sleep Heart Health Study (SHHS) and the Multi-Ethnic Study of Atherosclerosis (MESA), which contain subjects with a broad range of sleep and cardiovascular diseases (CVDs) to ensure heterogeneity. Results: Performances on average were s e n s i t i v i t y ( S e ) = 68.7 %, p r e c i s i o n ( P r ) = 69.1 %, s c o r e ( F 1 ) = 66.6 % per subject, and accuracy of correctly classifying apnea–hypopnea index (AHI) severity score was A c c = 84.9 % . Target AHI and predicted AHI were highly correlated ( R 2 = 0.828) across subjects, indicating validity in predicting SDB severity. Our algorithm proved to be statistically robust between databases, between different periodic leg movement index (PLMI) severity groups, and for subjects with previous CVD incidents. Further, our algorithm achieved the state-of-the-art performance of S e = 87.8 %, S p = 91.1 %, A c c = 89.9 % using independent comparisons and S e = 90.7 %, S p = 95.7 %, A c c = 93.8 % using a transfer learning comparison on the apnea-ECG database. Conclusions: Our robust and automatic algorithm constitutes a minimally intrusive and inexpensive screening system for the detection of SDB events using the ECG to alleviate the current problems and costs associated with diagnosing SDB cases and to provide a system capable of identifying undiagnosed SDB cases.