Autism diagnosis via correlation between vectors of direct quadrature instantaneous frequency of EEG analytic normalized intrinsic mode functions

Autism spectrum disorder (ASD) is a neurological and developmental disorder that commences usually in the early years of age. It impacts the social interaction, communication and learning. It is believed that it is mainly caused by an abnormal connectivity between brain zones. The presented work applies an EEG-based nonlinear method for the classification of ASD and neuro-typical groups. The suggested procedure does not require pre-assumptions and is completely data-driven. Also, the main advantage is the accurate tracking of the pace of EEG activity without the effect of amplitude modulation on the spectral information. In addition, the tracking is conducted point-by-point to avoid shortcomings of inexact global features. First, for every (ASD or neuro-typical) volunteer, the recorded EEG channels (64 channels) are decomposed by empirical mode decomposition (EMD) in order to get the underlying components (intrinsic mode functions-IMFs). Second, the direct quadrature (DQ) method is used to normalize the IMFs, and to dissociate between amplitude and frequency contents of the resulted components, as well as to help extract then the point-by-point spectral information from the analytic normalized IMFs by Hilbert transform. Third, the correlation coefficients between the instantaneous frequency vectors of the counterpart components will be computed over all channels (i.e., between components number 'i' of channels 'x' and 'y', 1 i < number of components, 1 x < 64, 1 y < 64). Fourth, correlation coefficients array is constructed. Fifth, the dimension of the feature array is reduced without loss of significant information. Sixth, classification of reduced features is achieved via neural network. Finally, the statistical assessment of the classification outcome is conducted. The proposed method yields a test accuracy of 94.1%-100%.