A Dual Soft Attention Network for Automatic Sleep Staging via Single-Lead Electrocardiogram Signals

Polysomnography (PSG) is the current gold standard for sleep monitoring; however, it is complex, burdensome, and costly. In contrast, commercial health monitoring devices, such as the Apple Watch, offer sleep scoring at a low cost over a few days. This paper proposes a dual-attention mechanism-based sleep staging method using single-lead electrocardiogram (ECG) signals, named SleepECGNet. The method extracts multi-scale features by employing convolutional kernels of different sizes, thereby capturing subtle details in the ECG signals. The model then integrates an SE module and a Luong attention-based encoder-decoder architecture to filter redundant information and dynamically focus on different sleep stages. Experiments were conducted using the HMC, MGH, and SHHS1 datasets for sleep staging tasks and the Apnea-ECG corpus was employed to execute transfer learning strategies directed at sleep apnea recognition. Comprehensive ablation experiments were conducted to verify the effectiveness of each component. Moreover, an analysis of sequence duration demonstrated that expanding the input timeframe directly improves classification outcomes.