An Approximate Nearest Neighbour System For Neonatal EEG Recall

Clinical neurophysiologists often find it difficult to recall rare EEG patterns despite the fact that this information could be diagnostic and help with treatment intervention. Traditional search methods may take time to retrieve the archived EEGs that could provide the meaning or cause of the speci...

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Bibliographic Details
Published in2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2018; pp. 283 - 286
Main Authors Murphy, B. M., Boylan, G. B., Lightbody, G., Marnane, W. P.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2018
Subjects
Artificial neural networks
Electroencephalography
Force
Pediatrics
Time series analysis
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Summary:Clinical neurophysiologists often find it difficult to recall rare EEG patterns despite the fact that this information could be diagnostic and help with treatment intervention. Traditional search methods may take time to retrieve the archived EEGs that could provide the meaning or cause of the specific pattern which is not acceptable as time can be critical for sick neonates. If neurophysiologists had the ability to quickly recall similar patterns, the prior occurrence of the pattern may help make an earlier diagnosis. This paper presents a system that may be used to assist a clinical neurophysiologist in the recall of neonatal EEG patterns. The proposed system consists of an alignment technique followed by an approximate nearest neighbour search algorithm called locality sensitive hashing. The system was tested on six different neonatal EEG pattern types with 430 events in total and the results are presented in this paper.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2018.8512222