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. 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 (CVD) to ensure heterogeneity. Performances on average were Se=68.7%, Pr=69.1%, F1=66.6% per subject, and accuracy of correctly classifying AHI severity score was Acc=84.9%. Target apnea-hypopnea index (AHI) and predicted AHI were highly correlated (R2=0.828) across subjects, indicating validatity 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 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. Our robust and automatic algorithm constitutes a minimally intrusive and inexpensive screening system for the detection of SDB event 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.
- Sleep-disordered breathing
- Recurrent neural network