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 (0.5-1.5Hz, >100μV), delta brushes (delta portion: 0.5-1.5Hz, >100μV; "brush" portion: 8-22Hz, <75μV), and interburst intervals (<10μV), 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.

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