Automating the analysis of EEG recordings from prematurely-born infants: A Bayesian approach

Objective: To implement an automated analysis of EEG recordings from prematurely-born infants and thus provide objective, reproducible results. Methods: Bayesian probability theory is employed to compute the posterior probability for developmental features of interest in EEG recordings. Currently, these features include smooth delta waves (0.5-1.5. Hz, >100 μV), delta brushes (delta portion: 0.5-1.5. Hz, >100 μV; " brush" portion: 8-22. Hz, <75 μV), and interburst intervals (<10 μV), though the approach taken can be generalized to identify other EEG features of interest. Results: When compared with experienced electroencephalographers, the algorithm had a true positive rate between 72% and 79% for the identification of delta waves (smooth or " brush") and interburst intervals, which is comparable to the inter-rater reliability. When distinguishing between smooth delta waves and delta brushes, the algorithm's true positive rate was between 53% and 88%, which is slightly less than the inter-rater reliability. Conclusion: Bayesian probability theory can be employed to consistently identify features of EEG recordings from premature infants. Significance: The identification of features in EEG recordings provides a first step towards the automated analysis of EEG recordings from premature infants.