Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine

- Novel machine learning approaches were used to study selected features within infant resting EEG.
- Two infant groups who differed on familial risk for language learning disorder (LLD) were assessed.
- Identification of infants at higher risk for LLD may facilitate earlier diagnosis and remediation.

ObjectivesThis 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.MethodsNetwork 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.ResultsInfants 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.ConclusionsThe 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.SignificanceThe 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.