A machine learning approach for distinguishing age of infants using auditory evoked potentials

OBJECTIVE: To develop a high performance machine learning (ML) approach for predicting the age and consequently the state of brain development of infants, based on their event related potentials (ERPs) in response to an auditory stimulus. METHODS: The ERP responses of twenty-nine 6-month-olds, nineteen 12-month-olds and 10 adults to an auditory stimulus were derived from electroencephalogram (EEG) recordings. The most relevant wavelet coefficients corresponding to the first- and second-order moment sequences of the ERP signals were then identified using a feature selection scheme that made no a priori assumptions about the features of interest. These features are then fed into a classifier for determination of age group. RESULTS: We verified that ERP data could yield features that discriminate the age group of individual subjects with high reliability. A low dimensional representation of the selected feature vectors show significant clustering behavior corresponding to the subject age group. The performance of the proposed age group prediction scheme was evaluated using the leave-one-out cross validation method and found to exceed 90% accuracy. CONCLUSIONS: This study indicates that ERP responses to an acoustic stimulus can be used to predict the age and consequently the state of brain development of infants. SIGNIFICANCE: This study is of fundamental scientific significance in demonstrating that a machine classification algorithm with no a priori assumptions can classify ERP responses according to age and with further work, potentially provide useful clues in the understanding of the development of the human brain. A potential clinical use for the proposed methodology is the identification of developmental delay: an abnormal condition may be suspected if the age estimated by the proposed technique is significantly less than the chronological age of the subject.