Automatic sleep staging based on single-channel ballistocardiogram signals and multiple scales temporal feature analysis

Traditional sleep staging studies often rely on contact sensors for signal data acquisition, which may compromise the integrity of sleep data. Our research presents an automatic sleep staging method utilizing non-contact single-channel Ballistocardiogram (BCG) signals to address this limitation. This study proposes a multiple scale(multi-scale) time window feature extraction method based on heart rate variability (HRV) and respiratory rate variability (RRV) to establish a more precise correlation between BCG signals and sleep stages. Additionally, we present an advanced two-layer stacked ensemble model designed to enhance the accuracy and robustness of sleep staging. The innovative sleep staging model is subjected to a rigorous 5-fold cross-validation on 10 diverse recordings, encompassing 10,614 sleep segments. Experimental results indicate that the proposed feature extraction method constructs a Top-50 feature set with an average weight of 0.2082, representing a 195.8 % improvement compared to the 0.6158 of the traditional 30s feature set. Additionally, the proposed classification model achieves an accuracy of 89.15 %, outperforming traditional sleep staging models by 2 percentage points. It provides valuable references for research based on HRV, RRV, and other biological information features. This advancement enhances sleep monitoring, especially for home and mobile healthcare, offering a more user-friendly experience and practical medical tools. "The BCG signal sleep data source code is available at [https://github.com/ZJSRU-ICLaboratory/BCG-Sleepstaging.]."