Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network. (January 2022)

• Record Type: Journal Article
• Authors: Yang, Quanan; Zou, Lang; Wei, Keming; Liu, Guanzheng
• Is Part Of: Computers in biology and medicine. Volume 140(2022)
• Publication Date: 2022-01
• Subjects: Obstructive sleep apnea -- Deep learning -- Electrocardiogram -- Heart rate variability -- ECG-Derived respiration; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105124
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: A deep neural network model was constructed for sleep apnea diagnosis. The model integrates ECG-derived respiration and heart rate variability. It requires only a single ECG lead for data collection. It achieves an accuracy of 90.3%, a sensitivity of 87.6%, a specificity of 91.9%. The model can be integrated with wearable devices.