TY - JOUR

T1 - Automating the analysis of EEG recordings from prematurely-born infants

T2 - A Bayesian approach

AU - Mitchell, Timothy J.

AU - Neil, Jeffrey J.

AU - Zempel, John M.

AU - Thio, Liu Lin

AU - Inder, Terrie E.

AU - Bretthorst, G. Larry

N1 - Funding Information:
This research was supported by grants from the National Institute of Health (R01HD057098 and P30HD062171) and the Doris Duke Distinguished Clinical Scientist Award.

PY - 2013/3

Y1 - 2013/3

N2 - 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.

AB - 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.

KW - Bayesian

KW - EEG

KW - Neonatal

UR - http://www.scopus.com/inward/record.url?scp=84873268372&partnerID=8YFLogxK

U2 - 10.1016/j.clinph.2012.09.003

DO - 10.1016/j.clinph.2012.09.003

M3 - Article

C2 - 23014143

AN - SCOPUS:84873268372

VL - 124

SP - 452

EP - 461

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

SN - 1388-2457

IS - 3

ER -