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Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy

Li, Peng, Karmakar, Chandan, Yan, Chang, Palaniswami, Marimuthu and Liu, Changchun 2016, Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy, Frontiers in physiology, vol. 7, pp. 1-9, doi: 10.3389/fphys.2016.00136.

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Title Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy
Author(s) Li, Peng
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Yan, Chang
Palaniswami, Marimuthu
Liu, Changchun
Journal name Frontiers in physiology
Volume number 7
Start page 1
End page 9
Total pages 9
Publisher Frontiers Research Foundation
Place of publication Lausanne, Switzerland
Publication date 2016-04-14
ISSN 1664-042X
Keyword(s) electroencephalogram (EEG)
epileptic seizure
distribution entropy (DistEn)
sample entropy (SampEn)
short-length EEG analysis
Summary Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure-sample entropy (SampEn)-and a more recently proposed complexity measure-distribution entropy (DistEn)-were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.
Language eng
DOI 10.3389/fphys.2016.00136
Field of Research 080109 Pattern Recognition and Data Mining
090609 Signal Processing
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085293

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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.