In Sleep
Differentiation of central disorders of hypersomnolence (DOH) is challenging but important for patient care. This study aimed to investigate whether biomarkers derived from sleep structure evaluated both by manual scoring as well as with artificial intelligence (AI) algorithms allow distinction of patients with different DOH. We included video-polysomnography data of 40 narcolepsy type 1 (NT1), 26 narcolepsy type 2 (NT2), 23 idiopathic hypersomnia (IH) patients and 54 subjects with subjective excessive daytime sleepiness (sEDS). Sleep experts manually scored sleep stages. A previously validated AI algorithm was employed to obtain automatic hypnograms and hypnodensity graphs (where each epoch is represented as a mixture of sleep stage probabilities). One-thousand-three features describing sleep architecture and instability were extracted from manual/automatic hypnogram and hypnodensity graphs. After feature selection, random forest classifiers were trained and tested in a 5-fold-cross-validation scheme to distinguish groups pairwise (NT1-vs-NT2, NT1-vs-IH, …) and single groups from the pooled remaining ones (NT1-vs-rest, NT2-vs-rest,…). The accuracy/F1-score values obtained in the test sets were: 0.74±0.04/0.79±0.05 (NT1-vs-NT2), 0.89±0.09/0.91±0.08 (NT1-vs-IH), 0.93±0.06/0.91±0.07 (NT1-vs-sEDS), 0.88±0.04/0.80±0.07 (NT1-vs-rest), 0.65±0.10/0.70±0.09 (NT2-vs-IH), 0.72±0.12/0.60±0.10 (NT2-vs-sEDS), 0.54±0.19/0.38±0.13 (NT2-vs-rest), 0.57±0.11/0.35±0.18 (IH-vs-sEDS), 0.71±0.08/0.35±0.10 (IH-vs-rest) and 0.76±0.08/0.71±0.13 (sEDS-vs-rest). The results confirm previous findings on sleep instability in NT1 patients and show that combining manual and automatic AI-based sleep analysis could be useful for better distinction of NT2 from IH, but no precise sleep biomarker of NT2 or IH could be identified. Validation in a larger and multi-centric cohort is needed to confirm these findings.
Cesari Matteo, Egger Kristin, Stefani Ambra, Bergmann Melanie, Ibrahim Abubaker, Brandauer Elisabeth, Högl Birgit, Heidbreder Anna
2022-Dec-02
Computerized analysis, Excessive daytime sleepiness, Hypersomnia, Machine learning, Sleep instability
