| Automating the analysis of EEG recordings from prematurely-born infants: A Bayesian approach. | |
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MedLine Citation:
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PMID: 23014143 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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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Authors:
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Timothy J Mitchell; Jeffrey J Neil; John M Zempel; Liu Lin Thio; Terrie E Inder; G Larry Bretthorst |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2012-9-24 |
Journal Detail:
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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:
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Created Date: 2012-9-27 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 100883319 Medline TA: Clin Neurophysiol Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2012. Published by Elsevier Ireland Ltd. |
Affiliation:
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Department of Pediatrics, Washington University, St. Louis, MO 63110, USA. Electronic address: mitchell@physics.wustl.edu. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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