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Published on: September 6, 2017
Resting state EEG classifies developmental status in three-year-old children
Dhanya Parameshwaran1, Supriya Bhavnani2, Debarati Mukherjee3
1Sapien Labs Center for Human Brain and Mind at Krea, IFMR, Chennai, India.
Insights
Short resting-state electroencephalography (rs-EEG) can identify developmental delays in young children. This quick, low-resource method aids early detection for timely intervention in cognitive and motor skills.
Area of Science:
- Neuroscience
- Developmental Psychology
- Biomedical Engineering
Background:
- Early childhood cognitive development monitoring is crucial for timely intervention.
- Current assessment methods are time-consuming and resource-intensive.
- Need for efficient, accessible tools for developmental screening.
Purpose of the Study:
- To evaluate the efficacy of short resting-state EEG (rs-EEG) in classifying developmental outcomes in 33-40-month-old children.
- To determine if rs-EEG can predict performance across multiple developmental domains.
- To assess feasibility in low-resource settings using portable EEG.
Main Methods:
- Collected 3-minute rs-EEG data from 70 children (33-40 months) using a 14-channel portable device.
- Applied supervised learning models using spectral and novel time-domain EEG features.
- Correlated EEG features with scores from the Bayley's Scale of Infant and Toddler Development, 3rd Edition (BSID-III).
Main Results:
- rs-EEG moderately classified five developmental domains (cognition, receptive/expressive language, fine/gross motor) with AUCs 0.70-0.84.
- Time-domain features showed stronger correlations and contributions than spectral frequencies.
- Accurate predictions were achievable with as few as 4 EEG channels.
Conclusions:
- Short rs-EEG is a viable, quick, and reliable indicator of cognitive developmental status in early childhood.
- This method can aid in identifying children needing developmental support, especially in low-resource environments.
- Portable, low-channel EEG systems show promise for accessible developmental screening.
Abstract:
Monitoring cognitive development in early childhood enables detection of problems for timely intervention. However, currently recommended methods require lengthy evaluations of task performance, and are resource intense. Here we examined whether 3 minutes of resting-state EEG (rs-EEG) recorded in 70 33-40-month-old children using a 14-channel portable EEG device in low-resource households could classify performance on five domains of developmental outcomes (cognition, receptive language, expressive language, fine motor and gross motor coordination) as measured by the Bayley's Scale of Infant and Toddler Development, 3rd Edition (BSID-III). Applying supervised learning models to a combination of spectral features and novel time-domain features derived from EEG data, we predicted BSID-III domain scores with moderate accuracy (AUCs ranging from 0.70 to 0.84 and F1-scores ranging from 0.58 to 0.76). While spectral frequencies significantly correlated with cognitive and language domain scores, time-domain features describing amplitude variability were more significantly correlated and contributed more substantially to model outcomes. Model performance was reliable even with a subset of 4 channels. Overall, this study provides a first demonstration that rs-EEG from low electrode configuration devices can serve as a quick and reliable indicator of cognitive developmental outcomes and aid in identifying those requiring support during early childhood.
