Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network

Abstract

An increasing number of people are being influenced by sleep apnea (SA), a disease that brings complications such as hypertension and arrhythmia. Single-lead electrocardiogram (ECG) SA detection based on portable devices is of increased interest as an alternative to the expensive and time-consuming polysomnography (PSG) performed in hospitals, because of its low cost and the use of light-weight portable devices. In single-lead ECG SA detection, researchers have found that considering the neighborhood information of the ECG segment can improve detection performance. However, existing work fails to reduce the impact of noise in the neighborhood information. To address this issue, we propose a effective deep-shallow fusion network, EDSFnet, with simple architecture. We use a deeper residual network to extract higher-level features of the original ECG segments, which are semantically strong and contain less noise, and lower level features with high resolution, containing more detailed neighborhood information fro- m ECG segments. Effective channel attention (ECA) is then used to fuse these two types of features to exploit their complementary nature. EDSFnet is state-of-the-art on the PhysioNet Apnea-ECG dataset, with accuracy of 92.6% per-segment, and 100% per-recording. EDSFnet achieved competitive results in subject-independent experiments on the FAH-ECG clinical dataset.