Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network

Quanan Yang; Lang Zou; Keming Wei; Guanzheng Liu

doi:10.1016/j.compbiomed.2021.105124

Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network

Comput Biol Med. 2022 Jan:140:105124. doi: 10.1016/j.compbiomed.2021.105124. Epub 2021 Dec 6.

Authors

Quanan Yang¹, Lang Zou¹, Keming Wei¹, Guanzheng Liu²

Affiliations

¹ School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China.
² School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China. Electronic address: liugzh3@mail.sysu.edu.cn.

PMID: 34896885
DOI: 10.1016/j.compbiomed.2021.105124

Abstract

Obstructive sleep apnea (OSA), which has high morbidity and complications, is diagnosed via polysomnography (PSG). However, this method is expensive, time-consuming, and causes discomfort to the patient. Single-lead electrocardiogram (ECG) is a potential alternative to PSG for OSA diagnosis. Recent studies have successfully applied deep learning methods to OSA detection using ECG and obtained great success. However, most of these methods only focus on heart rate variability (HRV), ignoring the importance of ECG-derived respiration (EDR). In addition, they used relatively simple networks, and cannot extract more complex features. In this study, we proposed a one-dimensional squeeze-and-excitation (SE) residual group network to thoroughly extract the complementary information between HRV and EDR. We used the released and withheld sets in the Apnea-ECG dataset to develop and test the proposed method, respectively. In the withheld set, the method has an accuracy of 90.3%, a sensitivity of 87.6%, and a specificity of 91.9% for per-segment detection, indicating an improvement over existing methods for the same dataset. The proposed method can be integrated with wearable devices to realize inexpensive, convenient, and highly efficient OSA detectors.

Keywords: Deep learning; ECG-Derived respiration; Electrocardiogram; Heart rate variability; Obstructive sleep apnea.