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

Abstract

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder with a worldwide prevalence of 1–2%. In low-resource environments, in particular, early identification and diagnosis is a significant challenge. Therefore, there is a great demand for ‘language-free, culturally fair’ low-cost screening tools for ASD that do not require highly trained professionals. Electroencephalography (EEG) has seen growing interest as an investigational tool for biomarker development in ASD and neurodevelopmental disorders. One of the key challenges is the identification of appropriate multivariate, next-generation analytical methodologies that can characterise the complex, nonlinear dynamics of neural networks in the brain, mindful of technical and demographic confounders that may influence biomarker findings. The aim of this study was to evaluate the robustness of recurrence quantification analysis (RQA) as a potential biomarker for ASD using a systematic methodological exploration of a range of potential technical and demographic confounders.MethodsRQA feature extraction was performed on continuous 5-second segments of resting state EEG (rsEEG) data and linear and nonlinear classifiers were tested. Data analysis progressed from a full sample of 16 ASD and 46 typically developing (TD) individuals (age 0–18 years, 4802 EEG segments), to a subsample of 16 ASD and 19 TD children (age 0–6 years, 1874 segments), to an age-matched sample of 7 ASD and 7 TD children (age 2–6 years, 666 segments) to prevent sample bias and to avoid misinterpretation of the classification results attributable to technical and demographic confounders. A clinical scenario of diagnosing an unseen subject was simulated using a leave-one-subject-out classification approach.ResultsIn the age-matched sample, leave-one-subject-out classification with a nonlinear support vector machine classifier showed 92.9% accuracy, 100% sensitivity and 85.7% specificity in differentiating ASD from TD. Age, sex, intellectual ability and the number of training and test segments per group were identified as possible demographic and technical confounders. Consistent repeatability, i.e. the correct identification of all segments per subject, was found to be a challenge.ConclusionsRQA of rsEEG was an accurate classifier of ASD in an age-matched sample, suggesting the potential of this approach for global screening in ASD. However, this study also showed experimentally how a range of technical challenges and demographic confounders can skew results, and highlights the importance of probing for these in future studies. We recommend validation of this methodology in a large and well-matched sample of infants and children, preferably in a low- and middle-income setting.

Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals

“…From Table 3, it is apparent that nonlinear features have been prevalently used to diagnose AD [49,50,54,55,57]. Additionally, SVM classifiers have also been commonly employed to classify EEG signals for the detection of ASD [52,54,56,[58][59][60] similar to our study. Although a classification study was done, lower accuracies were achieved in the following studies: [52,54,57,59,60] as compared to ours.…”

“…Additionally, SVM classifiers have also been commonly employed to classify EEG signals for the detection of ASD [52,54,56,[58][59][60] similar to our study. Although a classification study was done, lower accuracies were achieved in the following studies: [52,54,57,59,60] as compared to ours. Although higher classification accuracies of 100% [58] and 99.71% [47] were achieved in these particular two studies as compared to our study, smaller data sizes were used for training in both studies.…”

“…After pre-processing the EEG signals, principal component analysis (PCA) was employed for dimensionality reduction, prior to extracting recurrence quantification analysis (RQA) nonlinear features from the signals, in another study. The SVM classifier coupled with leave-one-subject-out validation yielded a high classification accuracy of 92.9% [54].…”

A novel automated autism spectrum disorder detection system

“…Heunis et al [69] obtained EEG data from the typical 19 clinical electrode system. The data were obtained from 46 healthy subjects and 16 autistic patients.…”

Studying the functional connectivity of the primary motor cortex with the binarized cross recurrence plot: The influence of Parkinson’s disease

“…fMRI usually groups BOLD-data into time-intervals (task-blocks) that are used as time-units. The RP showed these task-blocks, but also other BOLD-responses that were shorter (0–45 vs. 50–160 sec) or longer than the task-blocks, and whose interpretation according to criteria established for the RP may help to understand the complex short and long-term dynamic of individual brain areas [ 15 , 34 , 35 ]. The bRP is an adaptation of the RP which assembles the recurrence plot of the subjects to form the recurrence plot of an experimental groups.…”