Document Detail

Dynamic time warping based neonatal seizure detection system.
MedLine Citation:
PMID:  23367031     Owner:  NLM     Status:  MEDLINE    
Neonatal seizures patterns evolve with changing frequency, morphology and propagation. This study is an initial attempt to incorporate the characteristics of temporal evolution of neonatal seizures into our developed neonatal seizure detector. The previously designed SVM-based neonatal seizure detector is modified by substituting the Gaussian kernel with the Gaussian dynamic time warping kernel, to enable the SVM to classify variable length sequences of feature vectors of neonatal seizures. The preliminary results obtained compare favorably with the conventional SVM. The fusion of the two approaches is expected to improve the current state of the art neonatal seizure detection system.
Rehan Ahmed; Andrey Temko; William Marnane; Geraldine Boylan; Gordon Lighbody
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2012     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-31     Completed Date:  2013-08-01     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  4919-22     Citation Subset:  IM    
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MeSH Terms
Artificial Intelligence*
Diagnosis, Computer-Assisted / methods*
Electroencephalography / methods*
Infant, Newborn
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Support Vector Machines*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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