Abstract
Objectives This study assesses the ability of a novel, “automatic classification” approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. Methods Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH−). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. Results Infants were classified with about 80% accuracy into FH+ and FH− groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants’ EEG complexity patterns were significantly different from FH− infants. Conclusions The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. Significance The ability to identify infants at highest risk for LLD using “automatic classification” strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation.
| Original language | American English |
|---|---|
| Pages (from-to) | 2695-2703 |
| Number of pages | 9 |
| Journal | Clinical Neurophysiology |
| Volume | 127 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 1 2016 |
ASJC Scopus subject areas
- Sensory Systems
- Neurology
- Clinical Neurology
- Physiology (medical)
Keywords
- Developmental language disorder
- EEG
- Infant
- Machine learning
- Network analysis
- Support vector machine (SVM)