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Early Detection of risk of autism spectrum disorder based on recurrence quantification analysis of electroencephalographic signals

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Authors
Pistorius, T
Aldrich, Chris
Auret, Lidia
Pineda, J
Date
2013
Type
Conference Paper

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Pistorius, T. and Aldrich, C.and Auret, L. and Pineda, J. 2013. Early Detection of risk of autism spectrum disorder based on recurrence quantification analysis of electroencephalographic signals, in 6th International IEEE/EMBS Conference on Neural Engineering (NER), Nov 6-8 2013, pp. 198-201. San Diego CA: The Printing House.
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6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013 Proceedings
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6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013
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Abstract

Early detection of autism spectrum disorder (ASD) in infants is vital in maximizing the impact and potential long-term outcomes of early delivery of rehabilitative therapies. To date no definitive diagnostic test for ASD exists. Electroencephalography is a noninvasive method used to capture underlying electrical changes in brain activity. This proof-of-concept study suggests that recurrence quantification analysis features computed from resting state spontaneous eyes-closed electroencephalographic (EEG) signals may be useful biomarkers for early detection of risk of ASD.