Deep Learning-Based Assessment of Brain Connectivity Related to Obstructive Sleep Apnea and Daytime Sleepiness

초록

Purpose: Obstructive sleep apnea (OSA) is associated with altered pairwise connections between brain regions, which might explain cognitive impairment and daytime sleepiness. By adopting a deep learning method, we investigated brain connectivity related to the severity of OSA and daytime sleepiness. Patients and Methods: A cross-sectional design applied a deep learning model on structural brain networks obtained from 553 subjects (age, 59.2 +/- 7.4 years; men, 35.6%). The model performance was evaluated with the Pearson's correlation coefficient (R) and probability of absolute error less than standard deviation (PAE Results: We achieved a meaningful R (up to 0.74) and PAE Conclusion: A deep learning method can assess the association of brain network character-istics with OSA severity and daytime sleepiness and specify the relevant brain connectivity.

키워드