The online version of this article (doi:10.1186/1866-1955-6-12) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
JE carried out the analysis, wrote codes to do the necessary calculations, performed the classification, and drafted the corresponding sections of the manuscript. AEL collected the data on which the results are reported, contributed to the introduction and methods section of the manuscript, and aided in the design of the ERP study. MB aided in the design of the classifier and the discussion of the results. SD aided in the design of the experiment, the design of the artifact removal and feature extraction process, and the discussion of results. All authors read and approved the final manuscript.
It is commonly reported that children with autism spectrum disorder (ASD) exhibit hyper-reactivity or hypo-reactivity to sensory stimuli. Electroencephalography (EEG) is commonly used to study neural sensory reactivity, suggesting that statistical analysis of EEG recordings is a potential means of automatic classification of the disorder. EEG recordings taken from children, however, are frequently contaminated with large amounts of noise, making analysis difficult. In this paper, we present a method for the automatic extraction of noise-robust EEG features, which serve to quantify neural sensory reactivity. We show the efficacy of a system for the classification of ASD using these features.
An oddball paradigm was used to elicit event-related potentials from a group of 19 ASD children and 30 typically developing children. EEG recordings were taken and robust features were extracted. A support vector machine, logistic regression, and a naive Bayes classifier were used to classify the children as having ASD or being typically developing.
A classification accuracy of 79% was achieved, making our method competitive with other automatic diagnosis methods based on EEG. Additionally, we found that classification performance is reduced if eye blink artifacts are removed during preprocessing.
This study shows that robust EEG features that quantify neural sensory reactivity are useful for the classification of ASD. We showed that noise-robust features are crucial for our analysis, and observe that traditional preprocessing methods may lead to poor classification performance in the face of a large amount of noise. Further exploration of alternative preprocessing methods is warranted.
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- Robust features for the automatic identification of autism spectrum disorder in children
Alison E Lane
- BioMed Central
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