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[Electroencephalogram feature extraction and classification of autistic children based on recurrence quantification
Jie Zhao1,2, Zhiming Zhang1, Lingyan Wan1
1Institute of Electronic Information Engineering, Hebei University, Baoding, Hebei 071000, P.R.China.
Summary
This study reveals that nonlinear electroencephalogram (EEG) signal features can differentiate children with autism spectrum disorder (ASD) from typically developing (TD) children. Recurrence quantitative analysis (RQA) combined with machine learning offers a promising tool for ASD diagnosis.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Developmental Psychology
Background:
- Autism Spectrum Disorder (ASD) diagnosis and treatment require understanding electroencephalogram (EEG) signal characteristics.
- Nonlinear analysis of EEG signals may reveal distinct patterns in individuals with ASD compared to typically developing (TD) children.
Purpose of the Study:
- To explore differences in nonlinear EEG signal characteristics between children with ASD and TD children using Recurrence Quantitative Analysis (RQA).
- To evaluate the efficacy of machine learning models in classifying ASD and TD children based on these nonlinear EEG features.
Main Methods:
- Recurrence Quantitative Analysis (RQA) was employed to extract nonlinear features: recurrence rate (RR), determinism (DET), and length of average diagonal line (LADL) from EEG signals across different brain regions.
- Support Vector Machine (SVM) was utilized to classify children with ASD and TD based on the extracted RQA features.
- Performance metrics including classification accuracy, sensitivity, specificity, and Area Under the Curve (AUC) were calculated.
Main Results:
- The combination of RR, DET, and LADL features achieved a maximum classification accuracy of 84% for the whole brain area (parietal, frontal, occipital, temporal lobes), with 76% sensitivity, 92% specificity, and an AUC of 0.875.
- For the parietal and frontal lobes specifically, the same feature combination yielded a maximum accuracy of 82%, 72% sensitivity, 92% specificity, and an AUC of 0.781.
- Statistically significant differences in nonlinear EEG characteristics were observed between ASD and TD children, particularly in the parietal-frontal lobe region.
Conclusions:
- Nonlinear EEG signal characteristics, analyzed via RQA, serve as objective indicators for distinguishing between children with ASD and TD.
- The integration of RQA-derived features and machine learning provides valuable auxiliary indicators for clinical ASD diagnosis.
- The parietal-frontal lobe exhibits distinct nonlinear EEG signal characteristics in children with ASD, offering insights into regional brain function and potential diagnostic targets.
