Estimation of biomarkers for autism and its co-morbidities using resting state EEG

Autism Spectrum Disorder(ASD) is a collection of heterogeneous disorders with prevalent cognitive and behavioral abnormalities. ASD is generally considered a life-long disability occurring as a stand-alone disorder but it occurs with possible co-morbid conditions such as Attention Deficit Hyperactivity Disorder (ADHD), epilepsy, Obsessive-Compulsive Disorder (OCD), Anxiety etc., Electroencephalography (EEG) studies have been identified as one of the most widely used tool for assessing the cognitive functions. Literature suggests strong evidences of stable pattern of EEG associated with Autism Spectrum Disorder. But the understanding of the subtle variations between various co-morbidities and the pathophysiology behind it needs appropriate signal processing routines. Hence, this work focuses on the identification of electrophysiological biomarkers from the acquired EEG signals of low-functioning autistic children to distinguish between the various co-morbidities of autism. Results show that the sub band power and coherence parameters estimated from segmented resting state EEG waveforms are capable of differentiating the various subgroups under consideration. The identified biomarkers can thus act as supportive tools for the physician in clinically assessing the Autistic children of different groups and to define the various training approaches for children who differ widely.