Topological data analysis assisted automated sleep stage scoring using airflow signals

Objective: Breathing pattern variability (BPV), as a universal physiological feature, encodes rich health information. We aim to show that, a high-quality automatic sleep stage scoring based on a proper quantification of BPV extracting from the single airflow signal can be achieved. Methods: Topological data analysis (TDA) is applied to characterize BPV from the intrinsically nonstationary airflow signal. The extracted features from TDA are utilized to train an automatic sleep stage scoring model using the XGBoost learner. Additionally, the noise and artifacts that are typically present in the air flow signal are leveraged to improve the performance of the trained system. To evaluate the effectiveness of the proposed approach, a state-of-the-art method is implemented for comparison purposes. Results: A leave-one-subject-out cross-validation was conducted on a dataset comprising 30 whole-night polysomnogram signals with standard annotations. The results show that the proposed features outperform those considered in the state-of-the-art work in terms of overall accuracy (78.8% ± 8.7% vs. 75.0% ± 9.6%) and Cohen's kappa (0.56 ± 0.15 vs. 0.50 ± 0.15) for automatically scoring wake, rapid eye movement (REM), and non-REM (NREM) stages. An external validation conducted on a dataset comprising 80 whole-night polysomnogram signals with standard annotations shows a result of overall accuracy 74.1%±11.6% and Cohen's kappa 0.42±0.15, which again outperforms the state-of-the-art work. Furthermore, the analysis of feature importance reveals that the TDA features provide complementary information to the traditional features commonly used in the literature, and the respiratory quality index is identified as an essential component. Conclusion: The proposed TDA-assisted automatic annotation system can accurately distinguish wake, REM and NREM from the airflow signal. Significance: The utilization of a single air flow channel and the universality of BPV suggest the potential of TDA-assisted signal processing in addressing various biomedical signals and homecare issues beyond sleep stage annotation.