Sleep disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies “expressed/manifest” high loop gain via a cyclical self-similarity feature in effort-based respiration signals.
Working under the assumption that high loop gain increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI)>10. Central apnea labels are obtained both from manual scoring by sleep technologists, and from an automated algorithm developed for this study. The Massachusetts General Hospital (MGH) sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings.
Diagnostic CAI based on technologist labels predicted REC with an AUC of 0.82 ±0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ±0.02. A subanalysis was performed on a population with technologist labeled diagnostic CAI>5. Full night similarity predicted REC with an AUC of 0.57 ±0.07 for manual and 0.65 ±0.06 for automated labels.
The proposed self-similarity feature, as a surrogate estimate of expressed respiratory high loop gain and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow-limitation, and can aid prediction of REC.