Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts

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 sensitivity(Se)=68.7%, precision (Pr)=69.1%, score (F1)=66.6% per subject, and accuracy of correctly classifying apnea–hypopnea index (AHI) severity score was Acc=84.9%. Target AHI and predicted AHI were highly correlated (R2 = 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 Se=87.8%, Sp=91.1%, Acc=89.9% using independent comparisons and Se=90.7%, Sp=95.7%, Acc=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.

Wearable monitoring of sleep-disordered breathing: estimation of the apnea–hypopnea index using wrist-worn reflective photoplethysmography

“…Most of them focused on fingertip transmissive photoplethysmography and electrocardiography (ECG) 20 . Recent ECG-based methods showed good AHI estimation and OSA screening-performance in large and heterogeneous populations 21 , 22 . rPPG can potentially provide similar information as the ECG and it can be embedded in wrist-worn devices that are more accepted and easier to wear in a free-living context compared to ECG-patches or -belts 14 , 23 .…”

“…The performance of cardiovascular-based OSA monitoring algorithms is influenced by the presence of other sleep disorders and associated events, and by the types of respiratory events 21 , 22 , 27 . Previously, we reported that other sleep disorders can constitute a confounding factor when detecting respiratory events 21 , 27 .…”

Transfer learning for non-image data in clinical research: A scoping review

“…Time series was the most common target data type among the included studies, dominated by studies of sleep staging and seizure detection based on EEG data [19,32,[40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] and ECG analyses [23,24,[56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]] (e.g. arrhythmia classification).…”

Transfer learning for non-image data in clinical research: a scoping review

“…Time series was the most common target data type among the included studies, dominated by studies of sleep staging and seizure detection based on EEG data [19,32,[40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] and ECG analyses [23,24,[56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]] (e.g. arrhythmia classification).…”