Bag of Patterns for Nearest Neighbour 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 in2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2019; pp. 4558 - 4561
Main Authors Murphy, B. M., Boylan, G. B., Lightbody, G., Marnane, W. P.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
Subjects
Cluster Analysis
Early Diagnosis
Electroencephalography
Euclidean distance
Feature extraction
Humans
Infant, Newborn
Memory
Mental Recall
Neurophysiology
Pediatrics
Time series analysis
Vocabulary
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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 undesirable 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. This paper compares two brute force approaches for the task of neonatal EEG recall and looks at the performance accuracy, speed and memory requirements. This 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.2019.8857631