TLA-SleepNet: A Transformer–BiLSTM–Attention Network for Automatic Sleep Staging Using Single-Channel Ballistocardiogram Signals

Traditional sleep staging studies typically rely on signals collected using contact-based sensors, which may interfere with the natural sleep state of subjects and thus affect the authenticity and reliability of the recorded data. To address this limitation, this study proposes an automatic sleep staging method based on non-contact single-channel ballistocardiogram (BCG) signals. First, band-pass filtering is applied to the raw BCG signals to separate the heart rate and respiratory components. Heart rate variability (HRV) and respiratory rate variability (RRV) features are then extracted, and mutual information is used to select key feature subsets that exhibit strong correlations with different sleep stages. Considering the complexity and prominent temporal characteristics of real-world sleep data, a temporal modeling network named TLA-SleepNet is constructed to enhance the model’s capability in capturing complex sequential features and improving robustness. Experiments conducted on 10 independent sleep recordings containing a total of 10,614 sleep epochs demonstrate that, under subject non-independent testing conditions with five-fold cross-validation, the proposed method achieves an accuracy of 87.1% in the sleep staging task, with precision, kappa coefficient, and F1-score reaching 92.4%, 81.9%, and 88.7%, respectively. The results indicate that the proposed method can achieve a reliable sleep staging performance without direct contact between sensors and the human body, providing a feasible solution for non-contact sleep monitoring in home-based and mobile healthcare applications.