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Detection of epileptic seizure based on entropy analysis of short-term EEG

Li, Peng, Karmakar, Chandan, Yearwood, John, Venkatesh, Svetha, Palaniswami, Marimuthu and Liu, Changchun 2018, Detection of epileptic seizure based on entropy analysis of short-term EEG, PLoS One, vol. 13, no. 3, pp. 1-17, doi: 10.1371/journal.pone.0193691.

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Title Detection of epileptic seizure based on entropy analysis of short-term EEG
Author(s) Li, Peng
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Yearwood, JohnORCID iD for Yearwood, John orcid.org/0000-0002-7562-6767
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Palaniswami, Marimuthu
Liu, Changchun
Journal name PLoS One
Volume number 13
Issue number 3
Article ID e0193691
Start page 1
End page 17
Total pages 17
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2018-03
ISSN 1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
EXTREME LEARNING-MACHINE
APPROXIMATE ENTROPY
SAMPLE ENTROPY
PERMUTATION ENTROPY
TIME-SERIES
COMPLEXITY
ELECTROENCEPHALOGRAM
SIGNALS
Summary Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods-fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)-were used that have valid outputs for any given data lengths. We performed feature selection and trained classifiers based on a cross-validation process. The results show that performance of FuzzyEn and DistEn may complement each other and the best performance can be achieved by combining: 1) FuzzyEn of one 5-s window and the averaged DistEn of five 1-s windows for classifying normal from epileptic group (accuracy: 0.93, sensitivity: 0.91, specificity: 0.96); and 2) the averaged FuzzyEn of five 1-s windows and DistEn of one 5-s window for classifying ictal from interictal EEGs (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90). Further studies are warranted to examine whether this proposed short-term analysis procedure can help track the epileptic activities in real time and provide prompt feedback for clinical practices.
Language eng
DOI 10.1371/journal.pone.0193691
Field of Research MD Multidisciplinary
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018 Li et al.
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30109489

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.