Identification of atypical sleep microarchitecture biomarkers in children with autism spectrum disorder - Martinez, Caroline; Chen, Zhe Sage (Frontiers in Psychiatry, 2023)
Purpose Sleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis. Methods Sleep polysomnogram data was obtained from the Nationwide Children’ Health (NCH) Sleep DataBank. Children (age: 8-16 yr) with 149 autism and 197 age-matched controls without neurodevelopmental diagnosis were selected for analysis. An additional independent age-matched control group selected from the Childhood Adenotonsillectomy Trial (CHAT) and an independent smaller NCH cohort of younger infants and toddlers were used for additional validation. We computed periodic and nonperiodic characteristics from sleep EEG recordings. Machine learning models were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. Results In the NCH study, the Random Forest Model outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93,0.98]). Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal’s spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models. Conclusions Our results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD in children.