Detection of an Autism EEG Signature Through a New Processing Method Based on a Topological Approach

A new pre-processing approach of EEG data to detect topological EEG features has been applied to a continuous segment of artifact-free EEG data lasting 10 minutes in ASCII format derived from 50 ASD children and 50 children with other Neuro-psychiatric disorders, matched for age and male/female ratios. Each EEG was manipulated using a Cin-Cin algorithm, based on an input vector characterized by a linear composition of city-block matrix distances among19 electrodes. From the resulting triangular matrix of 171 numbers expressing all of the one-by-one distances among the 19 electrodes a minimum spanning tree(MST) is calculated. Electrode identification serial codes, sorted according to the decreasing number of links in MST, and the number of links in MST are taken as input vectors for machine learning systems. With this method all the content of an EEG is transformed in 38 numbers which represent the input vectors for machine learning systems classifiers. Machine learning systems have been applied to build up a predictive model to distinguish between the two diagnostic classes. The best machine learning system (KNN algorithm) obtained a global accuracy of 93.2% (92.37 % sensitivity and 94.03 % specificity) in differentiating ASD subjects from NPD subjects. In conclusion the results obtained in this study suggest that the two new pre-processing methods introduced, in particular the MST algorithm, have great potential to allow a machine learning system to discriminate EEGs obtained from subjects with autism from EEGs obtained from subjects affected by other psychiatric disorders.