Abstract
Introduction/Objective: Sleep apnea is one of the most common sleep-related breathing disorders that affects nearly a billion people across the world (Benjafield et al., 2019, The Lancet). Polysomnography (PSG), the gold standard test for apnea, is expensive and inconvenient. Thus, there is a strong impetus for alternate methods of diagnosis. We seek to validate the performance of a deep learning based apnea detection system using instantaneous heart rate (IHR) derived from single-channel electrocardiogram (ECG) (Z3Score®-HRV, Neurobit Technologies Pte Ltd) on the CINC open dataset (Goldberger et al., 2020).
Methods: Single channel ECG was processed on the cloud using the Z3Score® System (https://www.z3score.com/). Apnea-Hypopnea Index (AHI) estimation was based on total sleep-time derived from the IHR.
Results: Estimated AHI from Z3score correlated very strongly (r=0.85) with true AHI from PSG (Figure 1A). Sensitivity, specificity and AUC of binary classification at various diagnostically relevant AHI thresholds are also presented in figure 1B.
Conclusion: Our results demonstrated the feasibility of apnea event detection using only IHR derived from ECG channel. This allows ECG-patch based devices (e.g. Z3Pulse,Neurobit Technologies Pte Ltd) to be used as a more convenient and affordable alternative for Sleep apnea screening.
Footnotes
Cite this article as ERJ Open Research 2021; 7: Suppl. 7, 78.
This is an ERS Lung Science Conference abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).
- Copyright ©the authors 2021
