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

作者：Mitchell Timothy J^*; Neil Jeffrey J; Zempel John M; Thio Liu Lin; Inder Terrie E; Bretthorst G Larry

来源：Clinical Neurophysiology, 2013, 124(3): 452-461.

DOI：10.1016/j.clinph.2012.09.003

• EEG
• Bayesian
• Neonatal

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

• 出版日期2013-3