Authors: Anita E. Igberaese; Gleb V. Tcheslavski
Addresses: Department of Electrical Engineering, Lamar University, P.O. Box 10029, Beaumont, TX, 77710, USA ' Department of Electrical Engineering, Lamar University, P.O. Box 10029, Beaumont, TX, 77710, USA
Abstract: The aim of this study was to assess whether power spectrum estimates of electroencephalogram (EEG) can be used as a biomarker for autism spectrum disorder (ASD). EEG collected from ASD and control participants performing a short-memory task was preprocessed to remove noise and artefacts, power spectral density (PSD) estimates were obtained by the modified covariance method and used as the study features that were subjected next to the Kruskal-Wallis analysis of differences. After verifying that the features (PSD estimates) were statistically different between the autistic and control subjects, these PSD estimates were classified using the 'k nearest neighbour' (KNN) classification algorithm with the average accuracy of 89.29%. This result indicates that EEG of autistic and control individuals may contain statistically different features; therefore, EEG power spectrum may be used as a biomarker for autism.
Keywords: autism; autism spectrum disorder; ASD; electroencephalography; EEG; diagnostics.
International Journal of Electronic Healthcare, 2018 Vol.10 No.4, pp.275 - 286
Available online: 02 Aug 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article