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, where the extracted features are used to train an automatic sleep stage scoring model using the XGBoost learner. The noise and artifacts commonly present in the airflow signal are recycled to enhance the performance of the trained system. The state-of-the-art approach is implemented for a comparison. Results: When applied to 30 whole night polysomnogram signals with standard annotations, the leave-one-subject-out cross-validation shows that the proposed features (overall accuracy 78.8\%$\pm$8.7\% and Cohen's kappa 0.56$\pm 0.15$) outperforms those considered in the state-of-the-art work (overall accuracy 75.0\%$\pm$9.6\% and Cohen's kappa 0.50$\pm 0.15$) when applied to automatically score wake, rapid eyeball movement (REM) and non-REM (NREM). The TDA features are shown to contain complementary information to the traditional features commonly used in the literature via examining the feature importance. The respiratory quality index is found to be essential in the trained system. Conclusion: The proposed TDA-assisted automatic annotation system can accurately distinguish wake, REM and NREM from the airflow signal. Significance: Since only one single airflow channel is needed and BPV is universal, the result suggests that the TDA-assisted signal processing has potential to be applied to other biomedical signals and homecare problems other than the sleep stage annotation.

翻译：目的：呼吸模式变异性（BPV）作为一种普遍的生理特征，蕴含着丰富的健康信息。我们旨在证明，基于单路气流信号中BPV的恰当量化，能够实现高质量的自动睡眠分期评分。方法：应用拓扑数据分析（TDA）从本质上非平稳的气流信号中刻画BPV，所提取的特征用于训练基于XGBoost学习器的自动睡眠分期评分模型。将气流信号中常见的噪声与伪迹加以循环利用，以增强训练系统的性能。同时实现了一种当前最优方法作为对比。结果：应用于30份带有标准标注的整夜多导睡眠图信号时，留一法交叉验证表明，所提出的特征（总体准确率78.8%±8.7%，Cohen's kappa系数0.56±0.15）在自动评分清醒期、快速眼球运动期（REM）和非快速眼球运动期（NREM）方面优于当前最优工作中考虑的特征（总体准确率75.0%±9.6%，Cohen's kappa系数0.50±0.15）。通过特征重要性分析表明，TDA特征包含了文献中常用传统特征的互补信息。呼吸质量指数被证明在训练系统中至关重要。结论：所提出的TDA辅助自动标注系统能够从气流信号中准确区分清醒期、REM期和NREM期。意义：由于仅需单路气流通道且BPV具有普遍性，该结果表明TDA辅助信号处理具有应用于睡眠分期标注以外的其他生物医学信号及家庭护理问题的潜力。