Document Detail


Seizure detection using the phase-slope index and multichannel ECoG.
MedLine Citation:
PMID:  22271828     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Detection and analysis of epileptic seizures is of clinical and research interest. We propose a novel seizure detection and analysis scheme based on the phase-slope index (PSI) of directed influence applied to multichannel electrocorticogram data. The PSI metric identifies increases in the spatio-temporal interactions between channels that clearly distinguish seizure from interictal activity. We form a global metric of interaction between channels and compare this metric to a threshold to detect the presence of seizures. The threshold is chosen based on a moving average of recent activity to accommodate differences between patients and slow changes within each patient over time. We evaluate detection performance over a challenging population of five patients with different types of epilepsy using a total of 47 seizures in nearly 258 h of recorded data. Using a common threshold procedure, we show that our approach detects all of the seizures in four of the five patients with a false detection rate less than two per hour. A variation on the global metric is proposed to identify which channels are strong drivers of activity in each patient. These metrics are computationally efficient and suitable for real-time application.
Authors:
Puneet Rana; John Lipor; Hyong Lee; Wim van Drongelen; Michael H Kohrman; Barry Van Veen
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2012-01-18
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  59     ISSN:  1558-2531     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2012 Apr 
Date Detail:
Created Date:  2012-03-22     Completed Date:  2012-07-23     Revised Date:  2013-06-26    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1125-34     Citation Subset:  IM    
Affiliation:
Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53715, USA. rana.puneet@gmail.com
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MeSH Terms
Descriptor/Qualifier:
Adolescent
Algorithms*
Child
Child, Preschool
Diagnosis, Computer-Assisted / methods*
Electroencephalography / methods*
Female
Humans
Infant
Infant, Newborn
Male
Pattern Recognition, Automated / methods*
Reproducibility of Results
Seizures / diagnosis*
Sensitivity and Specificity
Grant Support
ID/Acronym/Agency:
R21 EB009749/EB/NIBIB NIH HHS; R21EB009749/EB/NIBIB NIH HHS
Comments/Corrections

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


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