Inter-site generalizability of EEG based age prediction algorithms in the preterm infant

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Objective. To overcome the effects of site differences in EEG-based brain age prediction in preterm infants. Approach. We used a ‘bag of features’ with a combination function estimated using support vector regression (SVR) and feature selection (filter then wrapper) to predict post-menstrual age (PMA). The SVR was trained on a dataset containing 138 EEG recordings from 37 preterm infants (site 1). A separate set of 36 EEG recordings from 36 preterm infants was used to validate the age predictor (site 2). The feature distributions were compared between sites and a restricted feature set was constructed using only features that were not significantly different between sites. The mean absolute error between predicted age and PMA was used to define the accuracy of prediction and successful validation was defined as no significant differences in error between site 1 (cross-validation) and site 2. Main results. The age predictor based on all features and trained on site 1 was not validated on site 2 (p < 0.001; MAE site 1 = 1.0 weeks, n = 59 versus MAE site 2 = 2.1 weeks, n = 36). The MAE was improved by training on a restricted features set (MAE site 1 = 1.0 weeks, n = 59 versus MAE site 2 = 1.1 weeks, n = 36), resulting in a validated age predictor when applied to site 2 (p = 0.68). The features selected from the restricted feature set when training on site 1 closely aligned with features selected when trained on a combination of data from site 1 and site 2. Significance. The ability of EEG classifiers, such as brain age prediction, to maintain accuracy on data collected at other sites may be challenged by unexpected, site-dependent differences in EEG signals. Permitting a small amount of data leakage between sites improves generalization, leading towards universal methods of EEG interpretation in preterm infants.