A Multi-scale Attention Network for Sleep Arousal Detection with Single-Channel ECG

Arousal detection is of great importance to measure sleep quality. Traditional diagnosis of arousal that relies on polysomnography (PSG) signals is cumbersome and costly, requiring an overnight stay in a sleep laboratory with multiple sensors attached to the subjects, which limits widespread adoption. Conversely, electrocardiogram (ECG) signal offers a convenient and affordable alternative due to its ease of integration into portable devices. However, most of methods for automatic arousal detection were based on PSG signals. Besides, they ignored the importance of learning neighborhood information of physiological signals. To address these issues, we propose a Multi-Scale Attention Network called MSANet to detect sleep arousal using single-channel ECG. Specifically, it consists of encoder-decoder structure, multi-scale channel attention module and temporal module. The encoder-decoder structure as the foundational framework is to extract ECG signal features across multiple time scales. The multi-scale channel attention module aims to help the model focus on essential features. In the temporal module, we utilize dilated convolutions to capture neighborhood information in the ECG signal. Besides, considering the rarity of sleep arousal events during the sleep period, we apply focal loss to address class imbalance issue. Experiment results on four publicly available sleep datasets show that our proposed model can achieve a competitive performance for arousal detection.