Automatic Diagnosis of Intellectual and Developmental Disorder Using Machine Learning Based on Resting-State EEG Recordings

Intellectual and developmental disorder results in lifetime impairments in cognitive capabilities and adaptability is highly prone to developing several forms of further diseases and is associated both with high care costs. In this study, we applied a machine learning approach for differentiating healthy individuals and individuals with intellectual and developmental disorder using resting-state electroencephalography recordings. Our approach sets a new benchmark with a balanced accuracy of 91.67%. In addition, by adopting novel model interpretability methods, we highlighted the low beta sub-band in the range of 19.5–21 Hz as the most important distinctive feature. Individuals with an intellectual and developmental disorder show significantly lower beta activity.