Deep adaptation network for subject-specific sleep stage classification based on a single-lead ECG

•An end-to-end domain adaptation network is proposed for subject-specific sleep staging.•A single-lead Electrocardiograph (ECG) signal is utilized as the input.•Multi-class focal loss and a domain aligning layer are combined to solve data imbalances and domain shifts.•State-of-the-art performance of sleep staging based on single-lead ECG is obtained on the public SHHS2, SHHS1, and MESA datasets, respectively.•The method has excellent performance for accuracy and Cohen’s Kappa in terms of cross datasets.