Document Detail


Automating the analysis of EEG recordings from prematurely-born infants: A Bayesian approach.
MedLine Citation:
PMID:  23014143     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Timothy J Mitchell; Jeffrey J Neil; John M Zempel; Liu Lin Thio; Terrie E Inder; G Larry Bretthorst
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-9-24
Journal Detail:
Title:  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology     Volume:  -     ISSN:  1872-8952     ISO Abbreviation:  Clin Neurophysiol     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-9-27     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100883319     Medline TA:  Clin Neurophysiol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012. Published by Elsevier Ireland Ltd.
Affiliation:
Department of Pediatrics, Washington University, St. Louis, MO 63110, USA. Electronic address: mitchell@physics.wustl.edu.
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