Cyclic Acyclic Patterns (CAP) framework in Sleep Microstructure of Sleep Disorders: Markers of Sleep Instability Using Healthy Controls as Reference

This study introduces a novel multi-feature Sleep Instability Score (SLEIS) to assess sleep disorders. We evaluate its performance in distinguishing among seven sleep disorders, using a healthy control group as a reference. For the first time, our study extracts an exhaustive set of macrostructural and microstructural CAP sleep features from an open sleep disorder database. We measured the deviation from the healthy control group for all extracted features, quantifying effect sizes with Cohens d. We produced two versions of the SLEIS score: one where the individual feature value is multiplied by its corresponding Cohens d, and another based on cumulative weights over feature groups. A Random Forest (RF) model was used to rank the features that best distinguish the seven sleep disorders. This approach helped us identify a novel multi-feature marker of sleep instability. RF classification on the original feature values, using an eight-class approach, failed to robustly discriminate between disorders and healthy controls (precision = 56.44%, recall = 60%, F1-score = 57.87%). Both SLEIS versions led to clear improvement (feature groups/individual features: precision = 95.23% / 100%, recall = 90.71% / 100%, F1-score = 92.23% / 100%). Weighting macro- and microstructural features by their effect sizes, as deviations from a normative sample, is key. Our approach offers a promising solution for defining the new SLEIS marker that accounts for the heterogeneity of sleep disorders.