Automatic Sleep Stage Classification via Non-Contact BCG and Dual-Layer Modeling

Traditional sleep staging studies often rely on contact sensors, which may interfere with natural sleep. Therefore, this study proposes an automatic sleep staging method based on non-contact single channel ballistocardiogram (BCG) signals. Firstly, discrete wavelet transform (DWT) is used to decompose the BCG signal to extract the required heart rate and respiratory components. Subsequently, the mapping relationship between BCG features and sleep staging was established through feature peak detection and further calculation of heart rate variability (HRV) and respiratory variability (RRV). Besides, a Dual-Layer model is introduced to enhance classification accuracy. The five-class classification task is transformed into two three-class problems to improve the distinction between similar sleep stages. Finally, the model was validated on a dataset of 15 independent BCG records (16,660 segments). It achieved an accuracy of 82.9%, improving by 2% to 5% over traditional sleep staging models. It outperformed traditional methods by 2%. This approach overcomes the limitations of sensor contact and offers a more comfortable and accurate method for sleep staging.