Objective: We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one EDR signal that is of higher quality. Methods: We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection. Results: The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (-score), optimal transport (OT) distance, and average frequency (AF) score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance. Conclusion: The sync-ensembled EDR provides robust respiratory information from electrocardiogram. Significance: Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

翻译：目的：本研究旨在融合不同心电衍生呼吸（EDR）算法的输出信号，生成更高质量的单一EDR信号。方法：将每个EDR算法视为从不同观测角度记录呼吸活动的软件传感器，基于呼吸信号质量指标识别高质量软件传感器，采用基于图拉普拉斯连接矩阵的相位同步技术对齐最高质量的EDR信号，最终融合这些对齐后的高质量EDR信号。我们将输出称为同步集成EDR信号。在两大整夜多导睡眠图数据库上评估所提算法性能，使用三种不同硬件传感器记录的呼吸信号进行验证，并与现有EDR算法进行对比。针对五种情形开展灵敏度分析：取EDR信号均值融合、以及分别进行/不进行同步对齐与信号质量选择的四种组合。结果：在同步相关系数（z-score）、最优传输距离（OT）和平均频率分数（AF）三项指标上，同步集成EDR算法均以统计学显著优势优于现有算法。灵敏度分析表明，信号质量选择与EDR信号对齐对性能均有统计学显著的贡献。结论：同步集成EDR能从心电图稳健提取呼吸信息。意义：相位同步不仅在理论上严谨，在设计鲁棒EDR算法时也具有实际应用价值。