Tosca-Marie Heunis|Chris Aldrich|Jurriaan M. Peters|Shafali Jeste|Mustafa ?ahin|C. Scheffer|Petrus J. de Vries
Recurrence quantification analysis of resting state EEG signals in autism spectrum disorder – a systematic methodological exploration of technical and demographic confounders in the search for biomarkers
Stellenbosch University|Curtin University|Boston Children's Hospital|University of California, Los Angeles|Harvard University|Boston Children's Hospital|Stellenbosch University|University of Cape Town
autism spectrum disorder asd neurodevelopmental disorder worldwide prevalence 12 lowresource environments particular early identification diagnosis significant challenge therefore great demand languagefree culturally fair lowcost screening tools asd require highly trained professionals electroencephalography eeg seen growing interest investigational tool biomarker development asd neurodevelopmental disorders one key challenges identification appropriate multivariate nextgeneration analytical methodologies characterise complex nonlinear dynamics neural networks brain mindful technical demographic confounders may influence biomarker findings aim study evaluate robustness recurrence quantification analysis rqa potential biomarker asd using systematic methodological exploration range potential technical demographic confoundersrqa feature extraction performed continuous 5second segments resting state eeg rseeg data linear nonlinear classifiers tested data analysis progressed full sample 16 asd 46 typically developing td individuals age 018 years 4802 eeg segments subsample 16 asd 19 td children age 06 years 1874 segments agematched sample 7 asd 7 td children age 26 years 666 segments prevent sample bias avoid misinterpretation classification results attributable technical demographic confounders clinical scenario diagnosing unseen subject simulated using leaveonesubjectout classification approachin agematched sample leaveonesubjectout classification nonlinear support vector machine classifier showed 929 accuracy 100 sensitivity 857 specificity differentiating asd td age sex intellectual ability number training test segments per group identified possible demographic technical confounders consistent repeatability ie correct identification segments per subject found challengerqa rseeg accurate classifier asd agematched sample suggesting potential approach global screening asd however study also showed experimentally range technical challenges demographic confounders skew results highlights importance probing future studies recommend validation methodology large wellmatched sample infants children preferably low middleincome setting