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Objective: We aimed to fuse the outputs of differentelectrocardiogram-derived respiration (EDR) algorithms to create one EDR signalthat is of higher quality. Methods: We viewed each EDR algorithm as a softwaresensor that recorded breathing activity from a different vantage point,identified high-quality software sensors based on the respiratory signalquality index, aligned the highest-quality EDRs with a phase synchronizationtechnique based on the graph connection Laplacian, and finally fused thosealigned, high-quality EDRs. We refer to the output as the sync-ensembled EDRsignal. The proposed algorithm was evaluated on two large-scale databases ofwhole-night polysomnograms. We evaluated the performance of the proposedalgorithm using three respiratory signals recorded from different hardwaresensors, and compared it with other existing EDR algorithms. A sensitivityanalysis was carried out for a total of five cases: fusion by taking the meanof EDR signals, and the four cases of EDR signal alignment without and withsynchronization and without and with signal quality selection. Results: Thesync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluatedby the synchronized correlation (-score), optimal transport (OT) distance, andaverage frequency (AF) score, all with statistical significance. Thesensitivity analysis shows that the signal quality selection and EDR signalalignment are both critical for the performance, both with statisticalsignificance. Conclusion: The sync-ensembled EDR provides robust respiratoryinformation from electrocardiogram. Significance: Phase synchronization is notonly theoretically rigorous but also practical to design a robust EDR.

Unsupervised Ensembling of Multiple Software Sensors with Phase Synchronization: A Robust approach For Electrocardiogram-derived Respiration